A Risk-Informed BIM-LCSA Framework for Lifecycle Sustainability Optimization of Bridge Infrastructure

Abstract

The sustainability of bridge infrastructure is becoming increasingly important due to rising environmental, economic, and social demands. However, most current assessment models remain fragmented, often overlooking the social pillar, underutilizing risk integration across the lifecycle, and failing to fully leverage digital tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA), resulting in incomplete sustainability evaluations. This study addresses these limitations by introducing a practical and adaptable model that integrates BIM, LCSA, and expert-driven risk prioritization. Five Hungarian bridge projects were modeled using Tekla Structures and analyzed in OpenLCA to quantify environmental, economic, and social performance. A custom Sustainability Level Change (SLC) algorithm was developed to compare baseline scenarios (equal weighting) with risk-informed alternatives, simulating the impact of targeted improvements. The results demonstrated that prioritizing high-risk sustainability indicators leads to measurable lifecycle gains, typically achieving SLC improvements between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked, high-risk indicators, pushed gains up to +12%. Even underperforming bridges exhibited performance enhancements when targeted actions were applied. The proposed framework is robust, standards-aligned, and methodologically adaptable to various bridge types and lifecycle phases through its data-driven architecture. It empowers infrastructure stakeholders to make more informed, risk-aware, and data-driven sustainability decisions, advancing best practices in bridge planning and evaluation. Compared to earlier tools that overlook risk dynamics and offer limited lifecycle coverage, this framework provides a more comprehensive, actionable, and multi-dimensional approach.

1. Introduction

1.1. Background

The sustainability of bridge infrastructure is increasingly prioritized due to rising environmental, economic, and social demands. Given their long service life, bridges require adaptive lifecycle planning that addresses evolving challenges. Tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA) support integrated, data-driven decision-making; however, they must also account for risks such as material scarcity, safety concerns, and long-term uncertainties. BIM has proven effective in enhancing collaboration, visualization, and workflow efficiency across infrastructure projects [1,2,3,4]. In highway bridge engineering, its adoption has delivered measurable benefits, although technical and regulatory barriers remain [5,6]. When combined with LCSA and Multi-Criteria Decision-Making (MCDM) methods, BIM enables a more structured evaluation of sustainability across the environmental, economic, and social pillars [7,8,9,10,11]. This integration facilitates lifecycle-based assessments of carbon emissions, cost efficiency, and other performance metrics that inform decision-making from design through to operation [12,13]. In recent years, sustainability models have begun incorporating social dimensions such as accessibility, user well-being, and labor conditions to produce more holistic evaluations [14,15,16]. Several of these frameworks employ quantitative methods, including MCDM tools, fuzzy logic, and scoring systems, to address the competing priorities inherent in sustainability assessment [10,11,17,18,19,20]. Such methods provide enhanced analytical capacity for managing the complex trade-offs typical of bridge projects. Risk-informed models are also gaining traction, particularly in maintenance planning and resilience optimization. These models integrate concepts from utility theory, climate impact simulation, Bayesian networks, and fuzzy mathematics to address lifecycle uncertainties [21,22,23,24,25]. Their application supports robust decision-making, especially under scenarios involving variable environmental loads or resource constraints. More recent research has proposed integrated frameworks that embed risk analysis within sustainability evaluations by leveraging BIM and LCSA across project phases [2,26]. Life Cycle Assessment remains a core methodology for comparing the environmental impacts of different design alternatives, such as steel versus concrete superstructures [8,26,27]. These comparisons have demonstrated that structural choices and material quantities significantly influence indicators such as Global Warming Potential and embodied energy [27,28]. Nevertheless, many existing efforts remain limited in scope. Numerous studies continue to focus narrowly on environmental aspects, often overlooking the complex and dynamic conditions that characterize real-world projects [26,27,28]. Key challenges persist in addressing social sustainability, fully integrating risk across the entire lifecycle, and developing assessment tools applicable during early design phases [4,26,27,28,29]. Additionally, technical barriers such as data incompatibility and the absence of flexible, standardized evaluation frameworks continue to hinder the broader adoption of sustainability assessment methodologies [6,29,30]. Despite the growing use of LCSA in infrastructure projects, most existing models lack direct integration of risk considerations, particularly at the indicator level. Prior approaches often address risk through supplementary analyses using fuzzy logic, Bayesian networks, or probabilistic simulations, but do not embed it within the core LCSA structure [23,24,25]. Furthermore, many models isolate risk assessments to specific lifecycle stages, such as construction or maintenance, rather than addressing risk exposure comprehensively across all sustainability pillars throughout the entire bridge lifecycle [7,8,17]. Few studies have attempted to quantify how varying risk levels influence sustainability indicator weightings or decision-making priorities. This gap highlights the need for a risk-informed LCSA model that integrates expert risk scoring into indicator weighting and evaluates its impact systematically across lifecycle phases. The framework proposed in this paper addresses this need directly by embedding risk as a foundational input within both the weighting scheme and the performance evaluation algorithm, thereby advancing the current state of lifecycle-based sustainability assessment in bridge infrastructure.

To advance the field, this study introduces a BIM-enabled, risk-informed framework for evaluating sustainability across the full lifecycle of bridge infrastructure. The model identifies the sustainability indicators most vulnerable to risk exposure and quantifies their impact using expert-driven weighting, integrated with BIM and LCSA tools. Designed to operate across diverse bridge types and lifecycle phases, the framework maintains interoperability with OpenLCA and aligns with ISO 31000 [31]. By embedding risk directly at the indicator level, the model supports more adaptive, targeted, and context-aware decision-making, particularly in complex or resource-constrained environments.

1.2. Research Gap, Objectives, and Scope

Despite the growing focus on sustainable infrastructure, many existing bridge assessment models remain fragmented, primarily concentrating on environmental impacts while lacking integration of social factors and lifecycle risk considerations [7,8,13,28,32]. Additionally, digital tools such as Building Information Modeling (BIM) and OpenLCA are underutilized in this context, and risk management frameworks based on ISO 31000 are rarely embedded within sustainability evaluations [2,6,23,25,31,33].

This study addresses these limitations by proposing a BIM-enabled, risk-informed model for evaluating sustainability across the full lifecycle of bridge infrastructure. The framework integrates expert-weighted risk indicators with LCSA metrics and introduces a Sustainability Level Change (SLC) algorithm to compare baseline (equal weighting) and risk-informed scenarios.

The study is guided by the following research questions:

• Which sustainability indicators are most impacted by risk exposure across the bridge lifecycle?
• How can risk-informed weightings be integrated into a quantitative model using BIM and LCSA tools?
• How can this model be applied to various bridge types, lifecycle phases, and geographic contexts while remaining ISO 31000-compliant and interoperable with OpenLCA?

By addressing these questions, the proposed model enhances decision-making processes in sustainable bridge management by providing a structured, risk-informed, and data-driven framework. It empowers stakeholders to systematically evaluate, prioritize, and implement sustainability interventions across diverse bridge types, lifecycle phases, and geographic contexts, while ensuring alignment with international standards.

2. Review of Quantitative Sustainability Assessment Models in Bridge Projects

Assessing sustainability in bridge projects requires models that address the full lifecycle and its evolving challenges. While approaches such as Life Cycle Assessment (LCA), Multi-Criteria Decision-Making (MCDM), fuzzy logic, and hybrid probabilistic models provide valuable tools, many of these methods underrepresent social impacts and struggle to effectively manage risk and uncertainty. These limitations, in turn, compromise the accuracy and reliability of sustainability evaluations. This section presents a critical overview of these existing methodologies, highlighting key limitations that inform the development of the proposed risk-informed sustainability assessment framework.

2.1. Hybrid Bayesian and Fuzzy Logic Models

Hybrid models that combine probabilistic reasoning with fuzzy logic are increasingly utilized in bridge sustainability assessments due to their strength in managing uncertainty across environmental impacts, long-term performance, and evolving risks. By integrating fuzzy mathematics with Bayesian networks, several studies have enhanced traditional LCA methods, enabling multi-level sensitivity analyses and probabilistic inference throughout the lifecycle of complex bridge types, such as cable-stayed systems [34]. Risk-aware maintenance strategies have also benefited from this integration. For instance, Multi-Attribute Utility Theory (MAUT)-based models calibrate risk preferences to balance cost efficiency with sustainability outcomes [17,21]. More advanced LCA approaches now incorporate probabilistic inputs to account for time-dependent deterioration and climate-induced hazards, improving the accuracy of environmental and economic projections [22]. Other frameworks combine Bayesian reasoning with structural reliability analysis to model risk propagation, employing techniques such as subset simulation to enhance predictive precision [24]. In seismic risk assessments, Bayesian Belief Networks (BBNs) apply expert-informed probabilities to simulate structural responses under conditions of uncertainty [25]. These developments signify a shift toward hybrid models that integrate dynamic risk factors and support adaptive, lifecycle-oriented decision-making. Such models offer robust tools for sustainability planning amidst growing environmental and operational complexities.

2.2. Life Cycle Assessment (LCA)-Based Models

Life Cycle Assessment (LCA) remains a cornerstone of sustainability evaluation in bridge projects, traditionally focused on environmental impacts but increasingly expanded to include economic and social dimensions. LCA provides a comprehensive view of sustainability, covering material selection, design, maintenance, and decommissioning phases. Comparative LCA studies have demonstrated how design choices influence both structural efficiency and ecological performance [8]. Recent efforts have integrated social indicators into tools like OpenLCA, enabling joint environmental-social impact analyses during early design stages [7]. Other advancements incorporate degradation data, particularly for coastal structures, to enhance lifecycle impact estimations [32]. However, many LCA applications continue to face challenges, including methodological inconsistencies, limited integration across sustainability pillars, and insufficient modeling of early-stage uncertainties [28]. Accounting for climate-related deterioration in sustainability models has revealed that Global Warming Potential (GWP) and Life Cycle Costs (LCC) may increase by more than 12%, emphasizing the importance of integrating long-term climate risks into bridge planning and assessment [22]. These developments reaffirm LCA’s critical role in bridge sustainability while highlighting the need for more integrated, adaptive, and risk-sensitive approaches.

2.3. Multi-Criteria Decision-Making (MCDM) Models

MCDM methods play a key role in bridge sustainability assessments by providing structured tools to balance environmental, economic, and social criteria across lifecycle phases. Early models employed basic scoring systems to evaluate lifecycle costs, environmental impacts, and user needs [17], forming the foundation for more advanced frameworks. Techniques such as the Analytic Hierarchy Process (AHP) have since been integrated with rating systems like Envision and Greenroads to enable context-sensitive evaluations aligned with local goals [10]. Recent models incorporate expert-weighted social indicators to address vulnerabilities in aggressive or sensitive environments [15]. In data-scarce regions, fuzzy logic and VIKOR methods have been applied to manage uncertainty and imprecision [11]. Advanced models, such as weighted TOPSIS, assess bridge fire vulnerability using hierarchical indicator structures [19], while hybrid methods that combine entropy-based and expert-derived weights enhance assessment reliability [18]. Bi-objective fuzzy MCDM approaches have also emerged, aiming to optimize maintenance strategies by balancing sustainability targets with cost and performance considerations [20]. These developments reflect the increasing sophistication of MCDM tools, which now support enhanced uncertainty handling, stakeholder integration, and project-specific adaptability in sustainable infrastructure planning.

2.4. Socially Integrated and Contextual Models

Socially integrated models highlight the human and contextual dimensions of bridge sustainability, extending beyond the traditional emphasis on environmental and economic outcomes. Early cradle-to-grave LCA applications, such as those focusing on material efficiency and structural design, hinted at broader social impacts on communities and labor conditions [26]. The adoption of Social Life Cycle Assessment (S-LCA), particularly in sensitive environments, has since deepened this perspective by addressing social risks such as occupational health and community well-being [14]. However, challenges persist, including the lack of standardized indicators and difficulties in integrating qualitative social data into quantitative models [16,28]. Recent studies promote the integration of LCA and S-LCA within transparent, inclusive, and lifecycle-wide frameworks [35]. This study builds on these developments by quantifying key social indicators, including worker safety, stakeholder engagement, and public accessibility, using OpenLCA, in accordance with ISO 26000 and UNEP/SETAC guidelines [36,37]. Researchers also advocate for feedback-driven infrastructure models that enhance adaptive planning [38], while others emphasize the need to broaden economic assessments in LCA to encompass resilience, equity, and long-term value, beyond direct costs [39]. Together, these advancements demonstrate growing efforts to embed social responsibility and contextual sensitivity into sustainability assessments, thereby enhancing their relevance for complex, real-world bridge projects.

2.5. Summary and Critical Reflection

Table 1. Comparative Summary of Sustainability Assessment Models for Bridge Projects.

Study	Model Type	Scope/Application	Key Limitation	Related Section
Zhou et al. (2021) [23]	Hybrid LCA (Bayesian + Fuzzy)	Environmental LCA for cable-stayed bridges	Limited social integration	2.1
Sabatino et al. (2015) [21]	Risk-Informed MCDM	Maintenance optimization under uncertainty	Focused only on maintenance phase	2.3
Lee et al. (2025) [22]	Risk-Based LCA (Climate Adaptive)	Lifecycle environmental & cost impact under climate change	Assumes predefined hazard parameters	2.1/2.2
Zwirglmaier et al. (2024) [24]	Hybrid Bayesian Network	Infrastructure resilience and reliability under failure modes	Computational complexity in large-scale systems	2.1
Khan et al. (2024) [25]	Bayesian Belief Network (BBN)	Seismic risk and resilience evaluation	Relies on expert-defined probability input	2.1
Du et al. (2014) [8]	Comparative LCA	Procurement-stage design comparison	No social or risk integration	2.2
Penadés-Plà et al. (2020) [7]	Dual-Pillar LCA + S-LCA	Environmental & social assessment of concrete bridges	Lifecycle risk not considered	2.2/2.4
Hadizadeh-Bazaz et al. (2023) [32]	Degradation-Aware LCA	Condition-based impact assessment for coastal bridges	Focused on degradation, lacks social depth	2.2
Milić & Bleiziffer (2024) [28]	LCA Meta-Review	Review of LCA methods, gaps, and integration issues	No application model proposed	2.2/2.4
Arya et al. (2015) [17]	Conceptual Multi-Pillar	Early framework for bridge sustainability	Conceptual only, no computational model	2.1/2.3
Yadollahi et al. (2015) [10]	AHP + Envision/Greenroads	Bridge sustainability evaluation in Malaysia	Context-specific, limited generalizability	2.3
Navarro et al. (2021) [15]	MCDM with Expert Social Weighting	Bridge design in aggressive environments (social)	Limited to harsh environments, subjective weighting	2.3/2.4
Bansal et al. (2017) [11]	Fuzzy VIKOR	Sustainability ranking in developing bridge corridors	Simplifies stakeholder input	2.3
Li et al. (2022) [19]	Weighted TOPSIS	Fire vulnerability assessment for bridges	Focused on fire resilience only	2.3
Xu et al. (2022) [18]	Empowered TOPSIS	Bridge reliability performance ranking	Complexity in weighting schemes	2.3
Shojaei et al. (2024) [20]	Fuzzy Bi-Objective MCDM	Bridge maintenance strategy optimization	Fuzzy calibration needed for broader use	2.3
Du & Karoumi (2013) [26]	Cradle-to-Grave LCA	Material & design impact for railway bridges	No social or risk-based layers	2.2/2.4
Navarro et al. (2018) [14]	Social LCA (S-LCA)	Marine-exposed deck social assessment	Focused on social pillar only	2.4
Backes & Traverso (2023) [16]	Systematic S-LCA Review	Social indicators for carbon-reinforced concrete	No implementation in bridge case study	2.4
Cheng & Gao (2023) [34]	Bayesian Network-Based LCA	Lifecycle risk assessment for bridge networks	Requires high-quality input data; limited social integration	2.1
Khan, Arif & Khan (2025) [35]	Qualitative Best Practice Synthesis	Best practices in social sustainability for infrastructure	Qualitative synthesis, lacks empirical model	2.4

compares twenty-one key models used in bridge sustainability assessments, ranging from LCA and MCDM to fuzzy logic and hybrid Bayesian frameworks. These models illustrate a clear shift from single-pillar assessments toward more integrated and adaptive approaches.

Despite recent advances, many sustainability assessment models remain limited in practical applicability and adaptability. Risk is often addressed through supplementary tools such as Bayesian networks and fuzzy logic, rather than being embedded as a core component of sustainability evaluations. Moreover, most frameworks focus on isolated lifecycle phases, typically design or maintenance, without encompassing the entire project timeline. Social sustainability remains underdeveloped in many models, often relying on qualitative or context-specific indicators that lack standardization and comparability. Economic performance assessments are frequently reduced to direct cost analyses, overlooking broader aspects such as affordability, resilience, and long-term value. These recurring methodological limitations not only compromise the comprehensiveness of existing approaches but also introduce significant inaccuracies in evaluating sustainability performance across real-world bridge projects. This highlights the urgent need for a fully integrated, risk-informed, and lifecycle-spanning framework that rigorously addresses all three pillars, environmental, economic, and social, with equal emphasis. Such a model must also align with international standards and remain compatible with accessible tools to support practical, data-driven decision-making.

2.6. Integration of BIM and LCSA in Bridge Sustainability Assessment

The integration of Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA) has emerged as a critical strategy for enhancing decision-making in sustainable bridge projects. BIM enables the creation of detailed digital representations of physical and functional characteristics across all bridge lifecycle phases, while LCSA provides a structured evaluation of environmental, economic, and social impacts through standardized inventory data and impact assessment methods [1,2,3]. Several studies have highlighted the mutual benefits of linking BIM with LCSA. For instance, a framework combining BIM, LCSA, and ISO-based risk analysis has been proposed to evaluate sustainability trade-offs across different lifecycle stages [1,40]. The use of 5D and 6D BIM platforms to support LCA workflows, such as bill-of-materials extraction, construction scheduling, and cost estimation, has been identified as a vital step toward bridging the gap between digital modeling and sustainability analytics [3,33]. BIM-based approaches have also been applied to model bridge deterioration, maintenance activities, and environmental burdens, providing critical inputs for both LCA and Life Cycle Cost (LCC) evaluations [29,30]. This integration is increasingly facilitated by LCA software such as OpenLCA, which, although not always explicitly referenced in earlier studies, enables the import of BIM-derived inventories and connects with validated databases such as ecoinvent, as utilized in this study [1,40]. Case studies comparing bridge design alternatives demonstrate that BIM–LCA interoperability can streamline inventory modeling and enhance the quality of comparative environmental assessments [8,27]. While LCA methods are becoming more prevalent in bridge sustainability evaluations, their integration with BIM remains only partially developed and requires further standardization [28]. To ensure methodological rigor and practical compatibility, internationally recognized frameworks for life cycle assessment, cost evaluation, and BIM-based information management offer essential guidance [1,2,40]. These standards underpin the consistent integration of sustainability considerations across project phases. Embedding sustainability indicators within BIM environments also enables early-stage design optimization and supports proactive, data-informed decision-making [2,12]. Ultimately, integrating BIM and LCSA into bridge sustainability assessments provides a robust mechanism for tracking real-time sustainability performance throughout the design, construction, and operational phases. This dual approach enhances accuracy, promotes transparency, and aligns infrastructure decisions with risk-informed and ISO-compliant sustainability objectives.

3. Methodology

This study presents a structured, risk-based lifecycle methodology for evaluating the sustainability of bridge projects by integrating expert-validated risks with a tailored set of LCSA-based indicators aligned with ISO standards and OpenLCA categories. The approach operationalizes this integration using Tekla Structures and OpenLCA, enabling project-specific, data-driven assessments of environmental, economic, and material performance across the entire bridge lifecycle. The full methodological workflow is illustrated in Figure 1, which outlines six sequential steps that link risk prioritization with sustainability evaluation through BIM–LCSA integration as follows:

• Reusing and reorganizing validated survey data on bridge project risks: The methodology begins by reanalyzing previously validated expert survey data to identify and structure the most critical risks affecting bridge projects. This step ensures that the model is grounded in real-world concerns and reflects expert consensus on risk relevance across the project lifecycle.
• Selecting and classifying sustainability indicators by pillar and data availability: A tailored set of sustainability indicators is selected and categorized under the environmental, economic, and social pillars, based on their availability in OpenLCA and alignment with standardized assessment frameworks. This ensures that only measurable and context-appropriate indicators are included in the evaluation.
• Mapping risks to the corresponding indicators and calculating weighted scores: Each identified risk is mapped to one or more sustainability indicators, and a normalized weight is assigned to reflect its relative influence. This allows the model to prioritize indicators based on actual risk exposure rather than assigning equal importance across all factors.
• Developing an algorithm that quantifies sustainability performance across lifecycle phases: A custom algorithm is developed to calculate the overall sustainability performance by combining BIM- and LCA-derived indicator values with the risk-informed weights. This ensures a transparent, repeatable, and lifecycle-based evaluation of bridge projects.
• Validation through real-world bridge case studies: To test the applicability and robustness of the methodology, it is applied to five Hungarian bridge case studies. Using Tekla Structures and OpenLCA, sustainability indicators are analyzed to demonstrate the practicality of the framework in real-world conditions.
• Prioritizing sustainability indicators based on delta-based composite impact analysis under a risk-informed weighting scheme: A delta-based impact analysis is conducted to determine how each indicator’s influence changes between equal-weight and risk-informed scenarios. This final step highlights which indicators contribute most to sustainability performance and helps target improvements where they matter most.

Figure 1. Six-step methodology for risk-informed BIM–LCSA integration in bridge sustainability assessment.

Figure 1. Six-step methodology for risk-informed BIM–LCSA integration in bridge sustainability assessment.

4. Reusing and Reorganizing Validated Survey Data for Algorithm Development

This study builds upon the previously published survey by Ahmad et al. [40], which evaluated 55 risk factors occurring across the bridge project lifecycle and their potential impact on the three pillars of sustainability: environmental, economic, and social. For the purposes of this model, a subset of 38 risks was selected using the Pareto Principle, representing the top 80% of total sustainability impact as identified in the original dataset. The application of the Pareto Principle (80/20 rule) serves as a prioritization mechanism to focus on the “vital” risks that contribute most significantly to sustainability outcomes, ensuring that model calibration and resource allocation remain efficient and impact-driven. Since the remaining 20% of risks inherently represent marginal contributors by design of Pareto analysis, a formal sensitivity analysis for these lower-impact risks was deemed unnecessary. This prioritization approach aligns with established risk management practices, which emphasize addressing high-impact risks as the foundation for robust decision-making models. The original survey was designed to systematically identify and prioritize risks that could undermine the sustainability performance of bridge projects. It applied a dual evaluation method: experts were asked to rate each risk’s impact on the three sustainability pillars (Low, Moderate, High) and to assess the probability and severity of each risk using a five-point Likert scale. These inputs were then combined to calculate a composite Risk Impact (RI), using the formula: RI = Severity × Probability. As this calculation involves a direct multiplication of two numerical values, no specialized software was required. All RI scores were computed using Microsoft Excel to ensure consistency, traceability, and transparency across the selected dataset. The survey was pilot-tested and validated, collecting responses from 41 professionals specializing in bridge project management, BIM, LCSA, and ISO-compliant risk practices across various global regions. To ensure consistency across expert ratings, the final scores were derived by averaging Likert values across all responses. While no formal inter-rater reliability test was conducted, this consensus-based approach aligns with established practice in lifecycle risk assessment studies. In this study, the validated risk data were reorganized and repurposed as core input for a risk-informed sustainability evaluation model. Each selected risk was explicitly linked to its dominant sustainability pillar and mapped to the specific lifecycle phase(s) where its impact is most critical. This transformation enabled a structured and context-sensitive interpretation of risks, both by sustainability domain and project stage, which became foundational to the development of the model presented herein. To support targeted analysis and maintain clarity, a single consolidated table—

Table 2. Reorganized Risk Impact Scores and Sustainability Pillar Ratings Derived from Validated Survey Data [40].

Risks	S	P	RI	Impact on Sustainability Pillars
				EN	EC	SO
R20—Financial Risks	5	5	25	Low	High	Moderate
R7—Cost and Schedule Planning Issues	4.8	5	24	Low	High	Moderate
R9—Design and Construction Errors	5	4.5	22.5	High	High	Moderate
R5—Information Management Issues	4.8	4.6	22.08	Low	High	Moderate
R45—Financial Impacts from Design Changes	4.5	4.6	20.7	Low	High	Moderate
R6—Resource Planning Issues	4.7	4.4	20.68	Moderate	High	Moderate
R48—Conflicts in Contract Documents	4.6	4.1	18.86	Low	High	Moderate
R27—Weather and Geological Risks	4.5	4.1	18.45	High	High	High
R25—Natural Disaster Risks	4.4	4.1	18.04	High	High	High
R31—Material Quality and Supply Risks	4.2	4.1	17.22	High	High	Moderate
R12—Construction Quality Issues	4.3	4	17.2	High	High	Moderate
R13—Contractor Management Problems	4.2	4	16.8	Moderate	High	Moderate
R24—Worker Safety and Health Risks	4	4.2	16.8	Low	High	High
R19—Environmental Impact Assessments	4.1	4	16.4	High	Moderate	Moderate
R3—Poor Communication and Stakeholder Engagement	4.1	3.9	15.99	Low	High	High
R29—Supply Chain Disruptions	3.9	4.1	15.99	Moderate	High	Moderate
R40—Impact of Climate Change	4.1	3.9	15.99	High	Moderate	Low
R28—Data Management Challenges	4.3	3.7	15.91	Low	High	Moderate
R18—Data Use in BIM Processes	4	3.9	15.6	Moderate	High	Moderate
R37—Budgeting for Future Maintenance	3.9	4	15.6	Low	High	Moderate
R22—Pollution and Waste Management Issues	3.8	4	15.2	High	Moderate	Moderate
R38—Inefficient Resource Utilization	3.8	4	15.2	Moderate	High	Moderate
R36—Inflexible Project Management	4	3.7	14.8	Low	High	Moderate
R41—Data Integrity Issues	4.1	3.6	14.76	Low	High	Moderate
R43—Environmental Compliance Risks	3.7	3.9	14.43	High	Moderate	Moderate
R46—Limited Access to Advanced Construction Technologies	3.7	3.9	14.43	Moderate	High	Moderate
R4—Maintenance Challenges	3.8	3.7	14.06	Moderate	High	Moderate
R17—Equipment Quality and Maintenance Issues	3.8	3.7	14.06	Moderate	High	Moderate
R50—Risks from Energy-Intensive Processes	3.5	4	14	High	Moderate	Low
R52—Political Factors	3.7	3.7	13.69	Low	High	High
R32—Technological Integration Issues	3.9	3.5	13.65	Moderate	High	Moderate
R21—Regulatory Compliance Delays	4.4	3	13.2	Low	High	Moderate
R33—Project Scope Creep	3.7	3.5	12.95	Low	High	Moderate
R39—Risk of Current Technologies Becoming Outdated	3.7	3.5	12.95	Low	High	Moderate
R51—Regulatory Changes	3.7	3.4	12.58	Low	High	Moderate
R34—Reputational Risks	3.1	4	12.4	Low	High	High
R8—Progress Tracking Difficulties	4	3	12	Low	High	Moderate
R30—Lifecycle Emission Assessment Issues	3.8	3.1	11.78	High	Moderate	Low

Note: This table was developed by reorganizing and combining validated data from Ahmad et al. (2025) [40].

—is introduced. This table outlines each risk’s unique ID and description, severity and probability scores, the resulting Risk Impact (RI), and the assessed influence across the three pillars. This streamlined format eliminates redundancy and provides a comprehensive input set for the subsequent weighting of indicators and model calculation steps.

5. Selection and Classification of Sustainability Indicators

To ensure both rigor and real-world relevance, the sustainability indicators used in this study were selected through a structured, multi-step process grounded in international best practices. The objective was to identify indicators that are not only conceptually sound but also measurable within existing lifecycle assessment tools and bridge project workflows. A tailored set of sustainability indicators was selected and categorized under the environmental, economic, and social pillars, based on their availability in OpenLCA and alignment with standardized assessment frameworks. This ensures that only measurable and context-appropriate indicators are included in the evaluation. The selection process was informed by four main sources:

• Recent literature on sustainable construction, infrastructure, and bridge projects, which has introduced empirically validated, context-specific sustainability indicators.
• International LCSA standards, including ISO 14040, ISO 14044, and ISO 15686, which offer clear frameworks for evaluating environmental, economic, and lifecycle performance.
• OpenLCA categories and Environmental Product Declaration (EPD) databases, ensuring that the selected indicators are backed by high-quality, verifiable data for lifecycle modeling.
• Sector-specific expertise and digital outputs from Tekla Structures, supported by expert survey results, which provided practical insights and semi-quantitative weightings, particularly useful for capturing social and operational aspects.

All indicator values were quantitatively calculated in OpenLCA, using inventory data derived from BIM models and standard LCA datasets. The resulting indicator set, grounded in literature, standards, software, and expert input, offers a robust and balanced foundation for assessing the sustainability of bridge projects, fully compatible with lifecycle modeling tools and practical project requirements.

5.1. Foundations for Identifying Sustainability Indicators in Bridge Projects: A Multi-Domain Literature Review

This section reviews existing literature to guide the selection and classification of sustainability indicators. It begins with a general overview of indicators used in construction projects, followed by a focus on bridge-specific indicators, and concludes with a critical review of sustainability assessment models in bridge management.

5.1.1. Overview of Sustainability Indicators in Construction Projects

Sustainability indicators have become essential in construction management to achieve environmentally sound, economically viable, and socially inclusive outcomes across the project lifecycle. Many studies apply the Triple Bottom Line (TBL) framework to structure indicators and align delivery strategies with sustainability goals [41,42]. Economically, key indicators include lifecycle cost, affordability, and resource efficiency, which are particularly relevant in small-scale projects focused on local materials and cost control [43]. Some models integrate energy use, material efficiency, and costing [44], with BIM supporting better alignment between indicators and project outcomes [45]. Comparative studies evaluate embodied energy and constructability across different structural systems [46], while economic indicators are increasingly used in firm-level sustainability reporting [47]. Environmental indicators typically address emissions, energy and water use, pollution, and recyclability. Research in this domain includes sustainable materials selection [48], environmental prioritization through fuzzy MCDM techniques [49], and full lifecycle assessments in sectors such as petrochemical construction [50]. Broader models incorporate stakeholder perspectives and network-based approaches to assess large-scale environmental impacts [51,52]. Social indicators, which were once overlooked, now encompass aspects such as safety, equity, workforce training, and stakeholder engagement [53]. Housing and infrastructure projects increasingly assess livability, accessibility, and user satisfaction [54,55,56]. These metrics also guide contractor evaluations and reflect human-centered performance goals [55,57]. Despite notable progress, challenges persist in terms of standardization, context sensitivity, and addressing diverse stakeholder needs [58].

Table 3. Sustainability Indicators in Construction Projects Grouped by Pillar with Supporting References.

Pillar	Indicator	Key Supporting References
Environmental	Energy Consumption	Salah et al. (2023) [44]; Danso (2018) [48]
	Carbon Emissions/GHG	Zhong & Wu (2015) [46]; Hashemi et al. (2021) [49]
	Material Recyclability	Heravi et al. (2015) [50]; Ferreira (2016) [47]
	Waste Generation and Pollution Control	Wu et al. (2018) [51]; Stanitsas et al. (2021) [52]
	Water Usage	Rostamnezhad & Thaheem (2022) [53]; Wu et al. (2018) [51]
	Embodied Energy	Zhong & Wu (2015) [46]; Salah et al. (2023) [44]
Economic	Lifecycle Cost (CAPEX + OPEX)	Ahmad et al. (2023) [43]; Salah et al. (2023) [44]; Khanzadi et al. (2020) [45]
	Operational Cost/Affordability	Zhong & Wu (2015) [46]; Ahmad et al. (2023) [43]
	Resource Efficiency and Productivity	Salah et al. (2023) [44]; Stanitsas & Kirytopoulos (2023) [52]
	Cost Overrun Control	Jiang et al. (2023) [58]; Stanitsas & Kirytopoulos (2023) [52]
Social	Stakeholder Engagement	Kiani Mavi et al. (2021) [41]; Rostamnezhad & Thaheem (2022) [53]
	Health and Safety	Karji et al. (2019) [54]; Li et al. (2018) [56]
	Community Impact/Social Equity	Alnsour et al. (2023) [55]; Li et al. (2018) [56]
	Job Creation and Training	Karji et al. (2019) [54]; Ferreira (2016) [47]
	End-User Satisfaction	Li et al. (2018) [56]; Stanitsas & Kirytopoulos (2023) [52]
	Cultural Sensitivity	Alnsour et al. (2023) [55]; Rostamnezhad & Thaheem (2022) [53]

summarizes these findings and supports the selection of bridge-specific indicators discussed in the next section.

5.1.2. Overview of Sustainability Indicators in Bridge Projects

Bridge infrastructure poses unique sustainability challenges due to its long service life, structural complexity, and social importance. Sustainability assessments in this field have evolved from purely environmental evaluations to integrated frameworks addressing environmental, economic, and social pillars. Recent studies apply LCA, LCCA, S-LCA, and MCDM methods to assess long-term impacts across the lifecycle. Environmental assessments typically focus on Global Warming Potential (GWP), energy use, and material efficiency. Early models linked environmental impact with cost and user disruption [15], while later studies employed full lifecycle assessments to compare materials and maintenance strategies [23,26,27]. Broader tools such as ReCiPe, ILCD, and BEES+ expanded evaluations to include resource depletion, human health, and ecosystem impacts [59], covering secondary effects like acidification and toxicity [23,28]. The social pillar is gaining significance, especially in sensitive contexts such as marine bridges. Studies have examined labor risks, stakeholder engagement, and public health [12], with newer models incorporating user safety, gender equity, and community acceptance [13]. Stakeholder-based risk mapping and predictive maintenance strategies are increasingly applied to enhance social and environmental outcomes [7,32]. Economic indicators commonly include lifecycle cost, procurement efficiency, and maintenance planning. Probabilistic MCDM methods support cost-performance decisions under uncertainty [21], while fuzzy-Bayesian models enhance LCA accuracy for complex structures [23]. Cost comparisons also guide procurement by evaluating design alternatives [8,11]. Broader MCDM reviews emphasize the importance of context-specific indicators shaped by governance structures and regional priorities [19,20,60]. The indicators summarized in

Table 4. Sustainability Indicators Commonly Used in Bridge Projects, Grouped by Pillar with Supporting References.

Pillar	Indicator	Verified Supporting References
Environmental	Global Warming Potential (GWP)	Zhou et al. (2021) [23]; Du & Karoumi (2013) [26]; Hadizadeh-Bazaz et al. (2023) [32]; Hammervold et al. (2013) [27]
	Lifecycle Energy Demand (Embodied + Operational)	Arya et al. (2015) [17]; Du et al. (2014) [8]; Milani et al. (2020) [59]
	Material Consumption/Resource Depletion	Du & Karoumi (2013) [26]; Zhou et al. (2021) [23]; Milani et al. (2020) [59]; Hadizadeh-Bazaz et al. (2023) [32]
	Water Consumption	Hammervold et al. (2013) [27]; Zhou et al. (2021) [23]
	Waste Generation & Recyclability	Arya et al. (2015) [17]; Bansal et al. (2017) [11]; Zhou et al. (2021) [23]
	Acidification, Eutrophication, and Toxicity	Zhou et al. (2021) [23]; Milić & Bleiziffer (2024) [28]
	Particulate Matter & Air Pollution	Du & Karoumi (2013) [26]; Hammervold et al. (2013) [27]
Economic	Lifecycle Cost (CAPEX + OPEX)	Arya et al. (2015) [17]; Milani et al. (2020) [59]; Du et al. (2014) [8]; Zhou et al. [23]
	Maintenance Frequency and Cost	Sabatino et al. (2015) [21]; Arya et al. (2015) [17]
	Construction Duration/Delay Risk	Yadollahi et al. (2015) [10]; Sabatino et al. (2015) [21]
	Economic Return/Cost-Benefit Analysis	Du et al. (2014) [8]; Zhou et al. (2021) [23]; Sabatino et al. (2015) [21]
	Procurement & Transport Cost	Khanzadi et al. (2020) [45]
Social	User Safety and Comfort	Arya et al. (2015) [17]; Yadollahi et al. (2015) [10]
	Stakeholder Engagement & Public Acceptability	Navarro et al. (2018) [14]; Navarro et al. (2021) [15]; Yadollahi et al. (2015) [10]
	Labor Conditions/Worker Rights	Navarro et al. (2018) [14]; Penadés-Plà et al. (2020) [7]; Hadizadeh-Bazaz et al. (2023) [32]
	Job Creation and Employment Impact	Bansal et al. (2017) [11]; Yadollahi et al. (2015) [10]
	Gender Equality and Social Inclusion	Navarro et al. (2021) [15]; Penadés-Plà et al. (2020) [7]
	Accessibility and Community Impact	Milani et al. (2020) [59]; Navarro et al. (2018) [14]; Bansal et al. (2017) [11]
	Cultural Sensitivity/Heritage Protection	Penadés-Plà et al. (2020) [7]; Hadizadeh-Bazaz et al. (2023) [32]

were selected through a systematic review of 15 peer-reviewed studies (2013–2024), based on two criteria: frequency across the literature and alignment with the TBL framework. Core indicators such as GWP, lifecycle cost, and user safety appeared in over half the studies and were cross-validated using ISO standards (e.g., ISO 14040/14044), stakeholder weighting, and SOCA-based risk mapping. Together, they form a solid foundation for bridge-specific sustainability modeling and are presented in Table 4, organized by pillar and supporting references.

5.2. Identification of Core Sustainability Indicators Based on LCSA and ISO Standards

To build a comprehensive and lifecycle-oriented foundation for bridge sustainability assessment, this study synthesizes indicators from two key sources: the LCSA framework and internationally recognized ISO standards. LCSA, as defined by the UNEP/SETAC Life Cycle Initiative [61], integrates Environmental LCA, Life Cycle Costing (LCC), and Social LCA (S-LCA) to assess environmental impact, economic viability, and social performance across all project phases. It includes a mix of midpoint and endpoint indicators such as Global Warming Potential (GWP), resource depletion, water use, cost, maintenance needs, labor conditions, and stakeholder well-being [62]. To strengthen the framework, this study also incorporates ISO standards. ISO 14040 and ISO 14044 guide environmental LCA processes [63,64]; ISO 15686-5 covers lifecycle costing methodologies [65]; and ISO 19650-1 supports digital integration through BIM platforms [66]. For the social pillar, ISO 26000 addresses stakeholder engagement practices [36], ISO 45001 focuses on occupational health and safety [67], and ISO 37120 provides metrics for accessibility and user comfort [68]. By combining LCSA and ISO guidance, the selected indicators comprehensively cover all three sustainability pillars and address known gaps, particularly in the social domain, by including factors such as public accessibility, user satisfaction, and employment. These indicators were subsequently tested for measurability using OpenLCA and Environmental Product Declarations (EPDs) to ensure practical feasibility. The full set of indicators, aligned with both LCSA and ISO standards and categorized by sustainability pillar, is presented in

Table 5. Identified Sustainability Indicators Based on LCSA and ISO Standards.

Pillar	Indicator	Source
Environmental	Global Warming Potential (GWP)	UNEP [61]; ISO 14044 [64]
	Abiotic Resource Depletion	UNEP [61]; ISO 14044 [64]
	Water Consumption	UNEP [61]; ISO 14044 [64]
	Waste Generation	ISO 14040 [63]; Hauschild et al. [62]
	Human Toxicity/Air Pollution	ISO 14044 [64]
	Energy Demand (Embodied + Operational)	ISO 14044 [64]; Hauschild et al. [62]
Economic	Lifecycle Cost (CAPEX + OPEX)	ISO 15686-5 [65]; ISO 14040 [63]
	Maintenance Frequency and Cost	ISO 15686-5 [65]; UNEP [61]
	Delay Risk/Construction Time	ISO 9001:2015 [69]; ISO 15686 [65]
	Procurement and Transport Cost	ISO 15686-5 [65]; ISO 19650 [66]
Social	Worker Health and Safety	UNEP [61]; ISO 45001 [67]
	Stakeholder Engagement/Public Acceptance	UNEP [61]; ISO 26000 [36]
	Job Creation and Employment Impact	UNEP [61]; ISO 26000 [36]
	User Comfort and Accessibility	ISO 26000 [36]; ISO 37120 [68]
	Gender Equality and Social Inclusion	UNEP [61]; SDG Alignment [70]
	Cultural and Community Impact	UNEP [61]; ISO 26000 [36]

Note: ISO 9001:2015 and the Sustainable Development Goals (SDGs) are referenced implicitly through methodological alignment and indicator selection.

.

5.3. Multi-Source Validation and Filtering of Sustainability Indicators Using OpenLCA and Standardized Data Sources

Building on a core indicator set derived from a four-source validation process, comprising a systematic literature review, LCSA and ISO standards, OpenLCA modeling capabilities, and real-world BIM outputs, this stage focuses on operationalizing sustainability indicators through a multi-source evaluation of conceptual validity, data availability, and modeling compatibility. A cross-check was conducted to ensure that each indicator was both theoretically sound and practically measurable using OpenLCA and Environmental Product Declaration (EPD) databases. OpenLCA, an open-source sustainability assessment tool, was employed to model environmental, economic, and social impacts using established methods such as ReCiPe, ILCD, and EF 3.0 for environmental indicators, and LCC-based modeling for economic indicators. Social indicators were assessed through OpenLCA’s S-LCA module, based on ISO 26000 and UNEP/SETAC guidelines, supported by databases such as PSILCA and SHDB. In parallel, EPDs developed under ISO 14025 and EN 15804 [71,72] provided standardized lifecycle data for key construction materials, while material quantities and specifications were extracted from Tekla Structures via Bill of Materials (BoM) and Bill of Quantities (BoQ) to ensure project-specific accuracy. The filtering process involved consolidating all indicators into a master list, mapping them to OpenLCA-compatible impact categories (e.g., GWP, energy demand, lifecycle cost, job creation), and verifying data traceability using BIM outputs, EPDs, and standardized S-LCA metrics. Indicators that could not be directly modeled were retained if they could be operationalized using ISO-compliant databases or expert-informed methods. All retained indicators were evaluated for feasibility within OpenLCA’s LCA, LCC, and S-LCA modules. To ensure consistency and reproducibility, each indicator was assigned a unique alphanumeric code: E1 to E6 for environmental indicators, C1 to C6 for economic indicators, and S1 to S6 for social indicators. These codes are used consistently throughout the model, case studies, and results. A full summary of the indicators, including codes, data sources, and operational methods, is presented in

Table 6. Finalized Sustainability Indicators for Bridge Projects with Codes and Multi-Source Operationalization Methods.

Pillar	Code	Indicator	Operationalization Method/Data Source
Environmental	E1	Global Warming Potential (GWP)	OpenLCA (ReCiPe/EF 3.0) + EPD
	E2	Abiotic Resource Depletion	OpenLCA (ILCD/EF 3.0) + ecoinvent
	E3	Water Consumption	OpenLCA (ReCiPe midpoint) + EPD
	E4	Waste Generation	OpenLCA (ecoinvent) + BIM-derived BoM
	E5	Air Pollution/Human Toxicity	OpenLCA (Human Toxicity Potentials)
	E6	Energy Demand (Embodied + Operational)	OpenLCA (EPD) + BIM-based energy use assumptions
Economic	C1	Lifecycle Cost (CAPEX + OPEX)	OpenLCA (LCC plugin) + BIM BoQ
	C2	Maintenance Frequency and Cost	OpenLCA (custom inputs) + Expert Risk Scores
	C3	Construction Time/Delay Risk	BIM 4D Modeling + Expert Risk Scores
	C4	Transport and Procurement Cost	OpenLCA (Material transport modules) + BIM sourcing data
	C5	Resource Efficiency and Productivity	BIM Quantity Takeoff + OpenLCA (Efficiency data via EPDs)
	C6	Cost Overrun Control	Expert Risk Scores (validated dataset of 38 risks)
Social	S1	Worker Health and Safety	PSILCA + ISO 45001 + Risk-informed expert scoring
	S2	Stakeholder Engagement	ISO 26000 framework + expert evaluations
	S3	Job Creation and Employment	PSILCA + ISO 26000 + Survey-based risk scores
	S4	User Comfort and Accessibility	ISO 37120 + Expert Scores
	S5	Public Safety Measures	Risk Evaluation Scores from Validated Dataset
	S6	Bridge Accessibility During Emergencies	BIM (Modeling Accessibility Paths) + ISO Guidelines + Expert Scores

.

All sustainability indicators presented in Table 6 were quantitatively modeled using OpenLCA, which served as the central platform for environmental, economic, and social impact assessment. To ensure accurate and project-specific modeling, the framework incorporated multiple validated data sources: material inventories and construction quantities extracted from Tekla Structures (BoM and BoQ), environmental profiles from EPDs and ecoinvent, expert-informed risk scores, and ISO-aligned metrics. These inputs were mapped to OpenLCA’s LCA, LCC, and S-LCA modules using established impact assessment methods such as ReCiPe, EF 3.0, ILCD, and PSILCA. This comprehensive, multi-source approach ensured that all indicators identified through the LCSA–ISO synthesis were both conceptually grounded and computationally traceable. By integrating standardized sustainability frameworks with real-world data and expert insights, the model delivers a transparent and reproducible assessment of lifecycle performance across the environmental, economic, and social pillars of bridge infrastructure.

6. Mapping Risks to Sustainability Indicators and Weight Calculation

To move beyond generic sustainability evaluations, this study introduces a risk-informed weighting method that links each indicator’s importance to actual project vulnerabilities. Instead of assigning equal weights, indicators are prioritized based on how directly they are affected by specific risks identified across the bridge lifecycle.

6.1. Risk-to-Indicator Mapping and Expert Weighting Process

A key innovation in this study is the integration of risk into sustainability assessment at the indicator level, rather than solely at the broader pillar level. This approach was adopted to reflect the reality that different risks affect specific aspects of performance; for example, some risks influence lifecycle cost or emissions, while others impact safety or stakeholder acceptance. To guide this mapping, each of the 38 validated risks was analyzed based on its thematic category, lifecycle relevance, and the nature of its expected impact. The associations between risks and indicators were established using a logic-driven method grounded in risk typology, lifecycle phase alignment, and prior project experience. For instance, procurement and cost-related risks were linked to Lifecycle Cost (C1) and Construction Time (C3), while health and safety risks were associated with Worker Health and Safety (S1) or Stakeholder Engagement (S2). Only risks with a clear and justifiable influence were mapped to indicators, ensuring conceptual clarity and methodological rigor. The selection and prioritization of sustainability indicators within the risk-informed weighting framework were derived through a structured evaluation of risk influence patterns. Indicators were not classified as “high-risk” arbitrarily; rather, their prioritization was determined by analyzing:

• The cumulative Risk Impact Scores (RI) of the mapped risks across the three sustainability pillars.
• Expert-assigned scores (S_j) reflecting each indicator’s vulnerability to these risks.
• Aggregated Risk Contribution Scores (RCS) that quantify how frequently and severely each indicator is affected across the project lifecycle.

Indicators with extensive lifecycle exposure and a higher density of impactful risk interactions, such as Lifecycle Cost (C1), Worker Health and Safety (S1), and Global Warming Potential (E1), emerged as primary drivers of sustainability performance. Context-dependent indicators, including Traffic Delay (C3) or Corrosion Control (part of Maintenance Frequency C2), were included in the model but received lower normalized weights due to their more specific or localized risk impacts in the evaluated bridge projects. The weighting system is designed to remain flexible, enabling recalibration for projects where such context-specific risks play a more dominant role in sustainability outcomes. To avoid treating all indicators within a pillar as equally affected, a structured disaggregation method was applied. Initially, risks were evaluated at the pillar level using a three-point scale (High = 3, Moderate = 2, Low = 1). Then, expert judgment was used to allocate this impact across the indicators within each pillar. Experts assigned a score (S_j) from 1 to 4 to reflect each indicator’s vulnerability to project risks. These scores were used to distribute pillar-level impacts proportionally among indicators, producing more accurate and context-sensitive weights. The outcome of this two-stage process is a set of normalized, risk-informed weights (W_j) for each sustainability indicator. These weights are later applied in the Sustainability Level Change (SLC) calculation to ensure that performance improvements are measured in relation to the indicators most sensitive to risk. The scoring scale and its interpretation are summarized in

Table 7. Expert Scoring Scale for Indicator Impact Weighting.

Score	Meaning
4	The indicator is critically affected by most risks and represents a core vulnerability within the pillar.
3	Strongly affected and central to pillar sustainability.
2	Moderately affected and plays a supporting role.
1	Minimally affected or context-dependent.

below.

The expert scoring process was conducted with a carefully selected panel of 41 professionals specializing in bridge project management, sustainability assessment, risk analysis, and BIM implementation. This diverse group included academics, certified risk managers, and industry practitioners from multiple geographic regions. Input was gathered through structured surveys and a series of Zoom meetings, which facilitated in-depth discussions and consensus-building on indicator relevance and risk impact. This process ensured methodological consistency and enhanced the robustness of the risk-informed sustainability evaluation model.

These expert scores were then normalized to derive weights W_j for each indicator using the formula:

$${\mathrm{W}}_{\mathrm{j}}\left(\exp \right)={\mathrm{S}}_{\mathrm{j}} /{\displaystyle \sum }_{\mathrm{i}}{\mathrm{S}}_{\mathrm{j}}$$

(1)

The final indicator-level impact I_ij for each risk–indicator pair was subsequently calculated by multiplying the pillar-level impact by the corresponding weight:

I_ij = Pillar Impact × W

(2)

This disaggregation method was applied separately for each sustainability pillar.

Table 8. Expert Scores, Normalized Weights, and Calculated Indicator-Level Impacts for the Economic Pillar.

Indicator	Expert Score (Sj)	Normalized Weight Wj(exp)	Iij if EC = 3	Iij if EC = 2	Iij if EC = 1
Lifecycle Cost	4	0.2500	0.7500	0.5000	0.2500
Maintenance Frequency & Cost	3	0.1875	0.5625	0.3750	0.1875
Construction Delay	2	0.1250	0.3750	0.2500	0.1250
Procurement Cost	2	0.1250	0.3750	0.2500	0.1250
Resource Efficiency	2	0.1250	0.3750	0.2500	0.1250
Cost Overrun Control	3	0.1875	0.5625	0.3750	0.1875
Total	16	1.0000	3.0000	2.0000	1.0000

,

Table 9. Expert Scores, Normalized Weights, and Calculated Indicator-Level Impacts for the Environmental Pillar.

Indicator	Expert Score (Sj)	Normalized Weight Wj(exp)	Iij if EN = 3	Iij if EN = 2	Iij if EN = 1
Global Warming Potential (GWP)	4	0.3077	0.9231	0.6154	0.3077
Abiotic Resource Depletion	2	0.1538	0.4615	0.3077	0.1538
Water Consumption	1	0.0769	0.2308	0.1538	0.0769
Waste Generation	2	0.1538	0.4615	0.3077	0.1538
Air Pollution/Human Toxicity	2	0.1538	0.4615	0.3077	0.1538
Energy Demand (Embodied + Operational)	2	0.1538	0.4615	0.3077	0.1538
Total	13	1.0000	3.0000	2.0000	1.0000

and

Table 10. Expert Scores, Normalized Weights, and Calculated Indicator-Level Impacts for the Social Pillar.

Indicator	Expert Score (Sj)	Normalized Weight Wj(exp)	Iij if SO = 3	Iij if SO = 2	Iij if SO = 1
Worker Health and Safety	3	0.214	0.642	0.429	0.214
Stakeholder Engagement	2	0.143	0.429	0.286	0.143
Job Creation and Employment	2	0.143	0.429	0.286	0.143
User Comfort and Accessibility	2	0.143	0.429	0.286	0.143
Public Safety Measures	3	0.214	0.643	0.429	0.214
Bridge Accessibility During Emergencies	2	0.143	0.429	0.286	0.143
Total	14	1.000	3.00	2.00	1.000

present the expert scores, normalized weights, and resulting indicator-level impacts for the economic, environmental, and social pillars, respectively.

This structured disaggregation approach ensures that each pillar-level risk impact (EN, EC, SO) is transparently and proportionally assigned to the most relevant sustainability indicators, thereby enhancing the model’s precision and its alignment with real-world risk profiles in bridge projects.

6.2. Risk Contribution and Weight Derivation of Sustainability Indicators

This phase assesses the cumulative risk burden across all indicators to select sustainability indicators based on real project vulnerabilities. The objective is to determine the extent to which each indicator is influenced by the overall risk landscape, ensuring that indicators with greater exposure to substantial risks are assigned higher weights in the sustainability assessment. This phase addresses the limitations of equal-weight assumptions by implementing a risk-informed prioritization strategy. The calculation integrates two key factors for each mapped risk–indicator pair: the probability of occurrence of the risk (P_i) and its disaggregated impact on the indicator (I_ij), derived from the expert-based scoring process detailed earlier. These components are combined to compute the Risk Contribution Score (RCS), a metric that quantifies the relative influence of each risk on each sustainability indicator, serving as the foundation for indicator-level weighting. The RCS is calculated as follows:

RCS_ij = P_i × I_ij

(3)

Once all individual RCS_ij values were calculated, they were aggregated for each indicator to determine its total cumulative risk impact across all mapped risks.

$${\mathrm{RCS}}_{\mathrm{j}}={\displaystyle \sum }_{\mathrm{i}}{\mathrm{RCS}}_{\mathrm{ij}}$$

(4)

These cumulative RCS_j values represent the total risk impact associated with each sustainability indicator. However, because the raw scores vary in scale across the three sustainability pillars, normalization (W_j, the normalized weight assigned to each sustainability indicator) was essential to enable meaningful comparison and integration. By applying proportional normalization within each pillar, the values were converted into relative weights that reflect each indicator’s share of the total risk impact within its category. This ensures that the final sustainability scoring model remains balanced across pillars and accurately prioritizes indicators based on their risk sensitivity.

$${\mathrm{W}}_{\mathrm{jr}}={\displaystyle \frac{{\mathrm{RCS}}_{\mathrm{j}}}{{{\displaystyle \sum }}_{\mathrm{J}}{\mathrm{RCS}}_{\mathrm{j}}}}$$

(5)

The resulting weights (W_jr), presented in

Table 11. Risk Contribution Scores and Normalized Weights for Sustainability Indicators.

Economic Indicators
Indicator	RCS Indicator	Normalized Weight (Wijr)
Lifecycle Cost	108.1	0.25
Maintenance Frequency & Cost	81.075	0.19
Construction Delay	54.05	0.13
Procurement Cost	54.05	0.13
Resource Efficiency	54.05	0.13
Cost Overrun Control	81.075	0.19
RCS pillar	432.4	1.00
Environmental Indicators
Indicator	RCS Indicator	Normalized Weight (Wjr)
Global Warming Potential (GWP)	81.8031	0.31
Abiotic Resource Depletion	40.9015	0.15
Water Consumption	20.458	0.08
Waste Generation	40.9015	0.15
Air Pollution/Human Toxicity	40.9015	0.15
Energy Demand (Embodied + Operational)	40.9015	0.15
RCS pillar	265.86	1.00
Social Indicators
Indicator	RCS Indicator	Normalized Weight (Wjr)
Worker Health and Safety	66.6251	0.21
Stakeholder Engagement	44.4444	0.14
Job Creation and Employment	44.4444	0.14
User Comfort and Accessibility	44.4444	0.14
Public Safety Measures	66.6491	0.21
Bridge Accessibility During Emergencies	44.4444	0.14
RCS pillar	310.8	1.00

, indicate each indicator’s relative vulnerability to risk within its respective pillar. These data-driven weights provide a robust foundation for prioritizing sustainability indicators based on the actual risk pressures they face in specific bridge project contexts.

6.3. Sensitivity Analysis and Pillar Weight Calibration

To validate and calibrate the indicator weights based on risk exposure, a scenario-based sensitivity analysis was conducted to examine how different combinations of low, moderate, and high risk-impact levels influence the contributions of the three sustainability pillars: Environmental (W₁c), Economic (W₂c), and Social (W₃c). Nine scenarios were developed, starting with a balanced baseline (Scenario 1) using equal weights, followed by eight alternative scenarios reflecting varied decision-making strategies. The full results are presented in

Table 12. Final Normalized Pillar Weights Across Scenarios.

Scenario	W1c (Environmental)	W2c (Economic)	W3c (Social)
Scenario 1—Baseline (Balanced)	0.333	0.333	0.333
Scenario 2—Moderate–High Emphasis	0.269	0.422	0.308
Scenario 3—Low–High Focus	0.241	0.478	0.281
Scenario 4—Moderate Priority	0.265	0.411	0.324
Scenario 5—High Impact Dominance	0.246	0.495	0.258
Scenario 6—High–Low Balance	0.304	0.449	0.247
Scenario 7—Moderate–High Balance	0.258	0.426	0.316
Scenario 8—Low–Moderate Balance	0.285	0.426	0.288
Scenario 9—High-Impact Focus	0.296	0.435	0.269

. Each scenario applied a distinct weighting scheme to the risk levels (Low, Moderate, High), which was propagated through the risk–indicator matrix to compute raw pillar contributions. These contributions were then normalized using the following formula:

$${\mathrm{W}}_{\mathrm{p}}={\displaystyle \frac{\mathrm{Total} \mathrm{Contribution} \mathrm{of} \mathrm{Pillar} \left(\mathrm{p}\right)}{{{\displaystyle \sum }}_{\mathrm{p}}\mathrm{Total} \mathrm{Contribution} \mathrm{of} \mathrm{Pillar} \left(\mathrm{p}\right)}}$$

(6)

Normalization ensured that pillar weights summed to 1 in every scenario, enabling consistent comparison. As shown in Table 12, scenarios emphasizing high or moderate risk impacts, such as Scenario 2 (Moderate–High Emphasis) and Scenario 5 (High Impact Dominance), produced higher weights for the economic pillar (W₂c = 0.422 and 0.495, respectively), reflecting the risk sensitivity of cost-related indicators. Scenario 3, which emphasized low and high risks while minimizing moderate ones, also elevated the economic contribution (W₂c = 0.478). In contrast, Scenario 7 (Moderate–High Balance) yielded a more even distribution (W₁c = 0.258, W₂c = 0.426, W₃c = 0.316), while the baseline Scenario 1 preserved equal weighting across all three pillars (0.333 each). Ultimately, Scenario 6 (High–Low Balance) was selected as the most appropriate calibration due to its alignment with observed risk exposure patterns across bridge projects. This scenario, with risk weights set at Low = 0.2, Moderate = 0.5, and High = 0.8, resulted in final normalized weights of W₁c = 0.304, W₂c = 0.449, and W₃c = 0.247. These values demonstrate that economic indicators are most affected by project risks, followed by environmental and social indicators. This distribution forms the basis for a risk-informed prioritization strategy within the sustainability model. While Scenario 6 provides a realistic risk-weighting baseline, the framework is designed for flexibility. Pillar weights can be recalibrated for specific contexts, for instance, public infrastructure agencies may prefer equal weighting (Scenario 1), while private-sector clients may prioritize economic outcomes (e.g., Scenario 5). This adaptability ensures the model’s utility without modifying its structural integrity.

With the finalized indicator weights, established through a multi-stage process involving risk-to-indicator mapping, expert scoring, and scenario-based sensitivity analysis, the next step is to apply these weights through a practical evaluation tool. This is operationalized via a quantitative algorithm designed to assess sustainability performance across all lifecycle phases of bridge projects. The following section introduces this algorithm and the core outcome metric: Sustainability Level Change (SLC), which quantifies the impact of applying risk-informed weights on the overall sustainability performance of a project.

7. Developing an Algorithm That Quantifies Sustainability Performance Across Lifecycle Phases

A custom algorithm was developed to evaluate overall sustainability performance by integrating BIM- and LCA-derived indicator values with risk-informed weights. This approach ensures a transparent, repeatable, and lifecycle-based assessment of bridge projects. Building upon the risk-weighting framework, the algorithm combines data from Tekla Structures and OpenLCA with prioritized indicator-level and pillar-level weights, enabling a comprehensive, risk-sensitive sustainability evaluation. At its core is the Sustainability Level Change (SLC), a composite metric developed in this study to quantify the difference in sustainability performance between a baseline scenario (SBL), which applies equal indicator weighting, and a risk-adjusted scenario (STL), reflecting how targeted improvements based on risk prioritization affect overall project sustainability. The SLC is calculated as follows:

SLC = STL − SBL= (W_TE. E_T + W_TC.C_T + W_TS.S_T) − (W_BE. E_B + W_BC.C_B + W_BS.S_B)
SLC = (0.304E_T + 0.449C_T + 0.247S_T) − (0.333⋅E_B + 0.333⋅C_B + 0.333⋅S_B)

(7)

where:

• E_T, C_T, S_T are the weighted scores for environmental, economic, and social indicators under the target (risk-informed) scenario.
• E_B, C_B, S_B are the corresponding scores under the baseline (equal weight) scenario.

The subcomponents of E_T, C_T, and S_T are computed as:

E_T = (0.31E_1T + 0.15E_2T + 0.08E_3T + 0.15E_4T + 0.15E_5T + 0.15E_6T)
C_T = (0.25C_1T + 0.19C_2T + 0.13C_3T + 0.13C_4T + 0.13C_5T + 0.19C_6T)
S_T = (0.21S_1T + 0.14S_2T + 0.14S_3T + 0.14S_4T + 0.21S_5T + 0.14S_6T)

(8)

For the baseline model, equal weights are applied across all indicators:

E_B = (0.167E_1B + 0.15E_2B + 0.167E_3B + 0.167E_4B + 0.167E_5B + 0.167E_6B)
C_B = (0.167C_1B + 0.167C_2B + 0.167C_3B + 0.167C_4B + 0.167C_5B + 0.167C_6B)
S_B = (0.167S_1B + 0.167S_2B + 0.167S_3B + 0.167S_4B + 0.67S_5B + 0.167S_6B)

(9)

In this structure, E, C, and S represent the three sustainability pillars, while the subscripts T and B refer to the target and baseline scenarios, respectively. Indicator performance values are extracted from OpenLCA and Tekla Structures, then multiplied by their corresponding normalized weights. The baseline model assumes equal weights across all indicators, reflecting conventional LCA practice where risk-specific variation is not considered. In contrast, the target model incorporates weights derived from Risk Contribution Scores (RCS) and pillar-level weights informed by the scenario-based sensitivity analysis, offering a more realistic, risk-aware profile of sustainability performance. The resulting Sustainability Level Change (SLC) quantifies both the actual performance outcome and the influence of risk prioritization. This delta enables a more nuanced understanding of sustainability gains across all three pillars. With the algorithm defined, it is applied to real-world case studies. Indicator values extracted from BIM and LCSA tools are used to compute both baseline and risk-informed sustainability scores. The results highlight the indicators most sensitive to risk exposure, providing actionable insights for targeted sustainability improvements throughout the bridge lifecycle.

8. Validation Through Real-World Bridge Case Studies

To evaluate the applicability and robustness of the proposed methodology, it was applied to five real-world Hungarian bridge projects located on the national secondary road network. Each bridge was modeled using Tekla Structures and analyzed in OpenLCA to assess environmental, economic, and social performance. Data from IFC, BoM, and BoQ exports supported the quantification of all 18 sustainability indicators. Environmental indicators, such as Global Warming Potential (GWP), energy demand, and waste generation, were modeled using OpenLCA with inventory data derived from Tekla Structures, complemented by standardized Environmental Product Declarations (EPDs) and the ecoinvent database. Economic indicators, including lifecycle cost, construction delay, and cost overrun risk, were assessed using OpenLCA’s LCC plugin, BIM-based quantity and scheduling data, and expert-informed scores derived from the validated risk dataset. Social indicators were evaluated through OpenLCA’s S-LCA framework, using the PSILCA database, ISO 26000–aligned metrics, and expert scoring for context-specific risks such as worker safety, stakeholder engagement, and accessibility. Some social indicators also relied on BIM outputs, such as emergency path modeling and spatial accessibility analysis. This integrated BIM–OpenLCA approach confirmed the model’s ability to deliver consistent, risk-informed sustainability assessments under real-world project conditions.

Case Study Overview

The five bridge projects selected for validation are located on Hungary’s secondary road network and exhibit a consistent design standard typical of regional infrastructure. Four of the bridges are single-span reinforced concrete slab structures, with reinforced concrete substructures and Class “B” load capacity. These bridges share similar geometric profiles and were constructed or reconstructed during the 1980s. In contrast, the Szeghalmi Berettyó Bridge differs from the others, featuring a three-span steel composite superstructure with a greater total length and a distinct span configuration. Although the structural ratings for substructure, pavement, and accessories are generally uniform across the sample, the inclusion of a multi-span steel composite bridge introduces meaningful variation in material composition and geometric complexity.

Table 13. Structural Characteristics of the Five Case Study Bridges.

Attribute	Szeghalmi Berettyó Bridge	Ecsegfalvai Hortobágy-Berettyó-Csatorna-Bridge	Gyomai Hármas-Körös Bridge	Szeghalmi Foki-Sebes-Körös Bridge	Nagyszénási Mágocs-ér Bridge
Bridge ID	1918	4183	4221	4223	4235
Year Built/Reconstructed	1978/2007	1987	1987	1987	1987
Total Length (m)	84.36	24.3	26.2	22.0	22.0
Number of Spans	3	1	1	1	1
Span Length (m)	28.12	24.3	26.2	22.0	22.0
Load Capacity	Class “B”	Class “B”	Class “B”	Class “B”	Class “B”
Superstructure Type	Steel composite beams	RC slab	RC slab	RC slab	RC slab
Substructure Type	Reinforced concrete	Reinforced concrete	Reinforced concrete	Reinforced concrete	Reinforced concrete
Structural Ratings (S/SS/A/P/E)	5/4/4/3/5	3/3/3/3/3	3/3/3/3/3	3/3/3/3/3	3/3/3/3/3

Note: S = Superstructure, SS = Substructure, A = Accessories, P = Pavement, E = Environment (Rating Scale: 1 = Excellent, 5 = Poor).

summarizes the structural and technical characteristics of each case study bridge.

The Szeghalmi Berettyó Bridge, a steel–concrete composite arch bridge located in Hungary, was selected as the first detailed case study. A real photograph of the bridge is presented in Figure 2, providing a visual reference of its structural configuration and context. A full BIM model was developed using Tekla Structures (Figure 3), serving as the foundation for extracting material quantities, geometric data, and component specifications. These data were subsequently integrated into the Life Cycle Sustainability Assessment (LCSA) model to evaluate the bridge’s sustainability performance across its lifecycle.

To integrate the BIM models with LCSA tools, structured data were exported from Tekla Structures as Bills of Materials (BoM), which detail the types and quantities of construction materials, Bills of Quantities (BoQ), which provide detailed measurements of construction work items, and Industry Foundation Classes (IFC) files, a standardized open-format for exchanging building information models across software platforms. An automated data exchange workflow was developed and applied to all five bridge case studies using a custom Python 3.8 script built on the IfcOpenShell library. This script processed IFC exports from Tekla, extracting essential data, such as material properties, quantities, and component relationships, into structured CSV files directly compatible with OpenLCA’s inventory datasets. This script-based approach replaced manual data handling, significantly reducing processing time and ensuring data consistency and traceability across all projects. The generated CSV inventory files for each bridge were cross validated with BoM and BoQ exports to ensure data accuracy and completeness. By using open standards (IFC) and automating key aspects of the data processing pipeline, this workflow enhanced transparency, minimized data entry errors, and provided a repeatable methodology for lifecycle sustainability assessments. Once imported into OpenLCA, these structured inventories enabled the calculation of environmental and economic indicators using standardized impact assessment methods, including ReCiPe, ILCD, and EF 3.0. Where direct project data were unavailable (e.g., transport distances, maintenance intervals), validated assumptions from comparable bridge projects were applied to ensure modeling consistency. All indicators were normalized to facilitate cross-pillar comparison. Environmental and economic indicators, such as Global Warming Potential (GWP), lifecycle cost, and energy demand, were normalized using Min–Max scaling based on European benchmarks (e.g., ecoinvent). For indicators where lower values reflect better performance (e.g., lifecycle cost), inverse Min–Max normalization was applied to maintain comparability across the dataset. The successful implementation of this automated data exchange workflow across five structurally diverse bridges demonstrates its scalability and robustness. Future developments will aim to extend this integration through API-based interoperability between Tekla Structures and OpenLCA, further streamlining and automating lifecycle assessments. For indicators like Lifecycle Cost (C1), where lower values indicate better performance, an inverse Min–Max formula was used:

Normalized ValueC1 = 1 − (Raw Cost − Min Cost)/(Max Cost − Min Cost)

(10)

Normalization boundaries (Min and Max) were defined based on the actual range of values observed across the five Hungarian bridge case studies. For example, Lifecycle Cost (C1) values ranged from approximately €1.87 million to €2.44 million, with no outliers excluded, as all values were considered realistic within the project context. The resulting normalized score for C1 in the Szeghalmi Berettyó Bridge case was 0.7520, indicating a relatively favorable lifecycle cost profile within the sample. For qualitative metrics—such as social indicators (S1–S6) and selected economic indicators (C2–C6), expert-validated Likert scores were linearly normalized to a 0–1 range using the transformation formula:

Normalized Score = Raw Score − 1/4

(11)

This ensured uniformity in weighting and aggregation across all sustainability dimensions, enabling a consistent comparison between quantitative and qualitative indicators. The normalized sustainability indicator values for the Szeghalmi Berettyó Bridge are presented in

Table 14. Sustainability Indicator Values (E1–E6, C1–C6, S1–S6) for the Szeghalmi Berettyó Bridge.

Pillar	Code	Indicator	Raw Value	Normalized Value
Environmental	E1	Global Warming Potential (GWP)	1,872,637.21 kg CO2-eq	0.4509
	E2	Abiotic Resource Depletion	71.84 kg Sb-eq	0.4816
	E3	Water Consumption	302,697.04 L	0.7090
	E4	Waste Generation	33,013.28 kg	0.7073
	E5	Air Pollution/Human Toxicity	1233.43 kg 1,4-DB eq	0.6193
	E6	Energy Demand (Embodied + Operational)	15,679,303.71 MJ	0.4628
Economic	C1	Lifecycle Cost (CAPEX + OPEX)	€2,239,756.50	0.7520
	C2	Maintenance Frequency and Cost	4.4/5	0.8500
	C3	Construction Time/Delay Risk	3.25/5	0.5625
	C4	Transport and Procurement Cost	4.0/5	0.7500
	C5	Resource Efficiency and Productivity	3.75/5	0.6875
	C6	Cost Overrun Control	4.3/5	0.8250
Social	S1	Worker Health and Safety	3.5/5	0.6250
	S2	Stakeholder Engagement	2.5/5	0.3750
	S3	Job Creation and Employment	3.0/5	0.5000
	S4	User Comfort and Accessibility	3.8/5	0.7000
	S5	Public Safety Measures	3.0/5	0.5000
	S6	Bridge Accessibility During Emergencies	2.8/5	0.4500

, encompassing 18 indicators across environmental, economic, and social domains.

Figure 4 presents the normalized (0–1 scale) values of the 18 sustainability indicators for the Szeghalmi Berettyó Bridge. Grouped by sustainability pillar—Environmental (E1–E6), Economic (C1–C6), and Social (S1–S6)—these values reflect the bridge’s relative sustainability performance, derived from lifecycle inventory data modeled in Tekla Structures and assessed using OpenLCA.

The application of the proposed model to the Szeghalmi Berettyó Bridge yielded a Sustainability Target Level (STL) of 0.6357, compared to a Baseline Level (SBL) of 0.6122, resulting in a positive Sustainability Level Change (SLC) of +0.0236. While this numerical improvement may appear modest, it reflects a meaningful shift in sustainability prioritization: high-risk, high-impact indicators, particularly Lifecycle Cost (C1), Maintenance Frequency and Cost (C2), and Worker Health and Safety (S1), gained increased influence under the STL scenario. This outcome demonstrates the model’s capacity to identify underperforming yet critical areas that might be overlooked by traditional equal-weight approaches. Similarly, the Ecsegfalvai Hortobágy-Berettyó Canal Bridge recorded an SLC of +0.0169 (STL = 0.6285; SBL = 0.6116), with improvements primarily driven by indicators such as Lifecycle Cost (C1), Global Warming Potential (E1), and Worker Health and Safety (S1). Indicators with comparatively lower risk relevance, such as Water Consumption (E3) and Transport and Procurement Cost (C4), were appropriately deprioritized. Across the remaining case studies, the calculated values for STL, SBL, and the resulting SLC are presented in

Table 15. Sustainability Results for All Five Bridge Case Studies.

Bridge Name	STL	SBL	SLC (STL − SBL)
Szeghalmi Berettyó	0.6357	0.6122	+0.0236
Ecsegfalvai Hortobágy-Berettyó-Csatorna	0.6285	0.6116	+0.0169
Gyomai Hármas-Körös	0.6138	0.6003	+0.0135
Szeghalmi Foki-Sebes-Körös	0.6558	0.6007	+0.0627
Nagyszénási Mágocs-ér	0.40188	0.4050	−0.0032

. These results provide a comparative overview of how risk-informed weighting influences sustainability performance across diverse bridge contexts.

The comparative results across the five bridge case studies demonstrate the model’s ability to capture sustainability performance under diverse structural and contextual conditions. Despite clear differences in typology, such as the Szeghalmi Berettyó Bridge’s three-span steel composite design versus the single-span reinforced concrete slab structures of the other bridges, their Sustainability Level Change (SLC) outcomes remain relatively close. The Szeghalmi Berettyó Bridge recorded an SLC of +0.0236, while the Ecsegfalvai and Gyomai bridges showed +0.0169 and +0.0135, respectively. This similarity suggests that sustainability performance is more closely influenced by alignment with high-risk indicators than by material quantity or structural complexity alone. In contrast, the Nagyszénási Mágocs-ér Bridge recorded a slightly negative SLC of −0.0032, primarily due to lower normalized performance in key environmental indicators such as Global Warming Potential (GWP) and resource depletion. Despite this bridge’s short span and consistent structural ratings across all elements, its underperformance in high-impact areas outweighed its moderate economic and social scores. This outcome reinforces the model’s diagnostic sensitivity and highlights its value in revealing areas that require targeted sustainability improvements.

9. Prioritizing Sustainability Indicators Based on Delta-Based Composite Impact Analysis

To identify which sustainability indicators most significantly influence performance under a risk-informed model, a delta (Δ)-based impact analysis was conducted. This method was selected to quantify how each indicator’s weighted contribution changes when transitioning from an equal-weighted baseline (Sustainability Baseline Level, SBL) to a risk-adjusted scenario (Sustainability Target Level, STL), thereby revealing the impact of risk prioritization. Each delta (Δ_j) was calculated as follows:

Δj = (Normalized Valuej × WeightjSTL) − (Normalized Valuej × WeightjSBL)

(12)

Positive delta (Δ_j) values indicate increased importance under the risk-informed model, while negative values suggest a relative deprioritization due to lower risk sensitivity, not necessarily poor performance. As shown in

Table 16. Delta-Based Impact Analysis of Sustainability Indicators for the Szeghalmi Berettyó Bridge.

Rank	Code	Indicator Description	Δj (STL − SBL)	Impact Trend	Primary Pillar
1	E1	Global Warming Potential (GWP)	+0.0645	Positive	Environmental
2	C1	Lifecycle Cost (CAPEX + OPEX)	+0.0624	Positive	Economic
3	S1	Worker Health and Safety	+0.0269	Positive	Social
4	S5	Public Safety Measures	+0.0215	Positive	Social
5	C2	Maintenance Frequency and Cost	+0.0196	Positive	Economic
6	C6	Cost Overrun Control	+0.0190	Positive	Economic
7	E6	Energy Demand (Embodied + Operational)	−0.0079	Negative	Environmental
8	E2	Abiotic Resource Depletion	−0.0082	Negative	Environmental
9	E5	Air Pollution/Human Toxicity	−0.0105	Negative	Environmental
10	S2	Stakeholder Engagement	−0.0101	Negative	Social
11	E4	Waste Generation	−0.0120	Negative	Environmental
12	S6	Bridge Accessibility During Emergencies	−0.0122	Negative	Social
13	S3	Job Creation and Employment	−0.0135	Negative	Social
14	S4	User Comfort and Accessibility	−0.0189	Negative	Social
15	C3	Construction Time/Delay Risk	−0.0208	Negative	Economic
16	C5	Resource Efficiency and Productivity	−0.0254	Negative	Economic
17	C4	Transport and Procurement Cost	−0.0278	Negative	Economic
18	E3	Water Consumption	−0.0617	Negative	Environmental

, indicators such as Lifecycle Cost (C1), Global Warming Potential (E1), and Worker Health and Safety (S1) exhibited the highest positive shifts in weighted contribution. In contrast, indicators like Water Consumption (E3) and Transport and Procurement Cost (C4) decreased in relative importance, despite maintaining strong normalized performance values. This outcome reflects their lower alignment with project-critical risks. The delta analysis thus provides a diagnostic lens to pinpoint which indicators merit targeted improvement under real-world risk conditions, supporting more strategic and effective sustainability planning.

To provide a broader perspective,

Table 17. Ranked Sustainability Indicators Based on Average Δ Contribution (STL − SBL) Across Five Bridge Projects.

Rank	Code	Indicator Description	Average Δ (STL − SBL)	Impact Trend	Primary Pillar
1	C1	Lifecycle Cost (CAPEX + OPEX)	+0.06259	Positive	Economic
2	E1	Global Warming Potential (GWP)	+0.05677	Positive	Environmental
3	S1	Worker Health and Safety	+0.02609	Positive	Social
4	C2	Maintenance Frequency and Cost	+0.01816	Positive	Economic
5	C6	Cost Overrun Control	+0.01763	Positive	Economic
6	S5	Public Safety Measures	+0.02352	Positive	Social
7	E4	Waste Generation	−0.00641	Negative	Environmental
8	E5	Air Pollution/Human Toxicity	−0.00581	Negative	Environmental
9	E6	Energy Demand (Embodied + Operational)	−0.00642	Negative	Environmental
10	E2	Abiotic Resource Depletion	−0.00215	Negative	Environmental
11	C3	Construction Time/Delay Risk	−0.01924	Negative	Economic
12	C4	Transport and Procurement Cost	−0.02575	Negative	Economic
13	C5	Resource Efficiency and Productivity	−0.02403	Negative	Economic
14	S2	Stakeholder Engagement	−0.01152	Negative	Social
15	S3	Job Creation and Employment	−0.01391	Negative	Social
16	S6	Bridge Accessibility During Emergencies	−0.01321	Negative	Social
17	S4	User Comfort and Accessibility	−0.01922	Negative	Social
18	E3	Water Consumption	−0.04436	Negative	Environmental

presents the average delta (Δ_j) values across all five bridge case studies, enabling the identification of indicators that consistently gained or lost influence across diverse project contexts.

To visualize these shifts more effectively, Figure 5 presents a ranked bar chart highlighting the most and least impacted sustainability indicators. This visual representation complements the tabulated results by clearly illustrating which sustainability aspects gained or lost strategic importance under the risk-weighted evaluation.

The delta-based rankings offer several valuable insights into the sustainability dynamics of bridge projects under risk-informed conditions. Lifecycle Cost (C1) emerged as the most influential indicator, underscoring the central role of economic efficiency in sustainability planning. Global Warming Potential (E1) followed closely, reaffirming the importance of climate-related impacts in infrastructure evaluation. Strong positive deltas were also observed for Worker Health and Safety (S1) and Public Safety Measures (S5), highlighting the growing prominence of social sustainability in risk-sensitive decision-making. In contrast, indicators such as Water Consumption (E3), Transport and Procurement Cost (C4), and Resource Efficiency (C5) consistently exhibited negative delta values, suggesting these factors carry less strategic weight in smaller-scale or rural bridge projects where related risks may be lower. While still relevant, these indicators may benefit from more granular data or context-specific calibration to accurately capture their true lifecycle influence.

10. Implications of Risk-Informed Prioritization for Bridge Sustainability Planning

10.1. Effectiveness of Targeted Improvements

To evaluate the effectiveness of targeted sustainability improvements within a risk-informed framework, five scenarios were simulated for each of the five bridge case studies: (1) the original risk-in formed model (STL), (2–3) 5% and 10% improvements applied to the top six indicators only, and (4–5) uniform 5% and 10% improvements applied across all 18 indicators. These improvements refer to proportional increases in the normalized indicator values, capped at a maximum of 1.0, and re-evaluated using the Sustainability Level Change (SLC) algorithm. The top six indicators, C1 (Lifecycle Cost), E1 (Global Warming Potential), S1 (Worker Health and Safety), S5 (Public Safety Measures), C2 (Maintenance Frequency and Cost), and C6 (Cost Overrun Control), were selected based on their highest average delta (Δ_j) values across the case studies. Results demonstrated that targeted improvements consistently outperformed uniform enhancements. For instance, the Szeghalmi Berettyó Bridge (B1) achieved a +5.52% gain in SLC under a 10% improvement of the top six indicators, compared to +3.85% under a 10% uniform improvement. The Szeghalmi Foki-Sebes-Körös Bridge (B4) recorded the highest gain, at +12.02%, under the same targeted scenario. Even in more moderate cases, B2 (Ecsegfalvai Hortobágy-Berettyó Canal Bridge) and B3 (Gyomai Bridge), targeted enhancements led to 1.5–2.1% greater improvements compared to uniform strategies. In contrast, the Nagyszénási Mágocs-ér Bridge (B5) exhibited marginal or negative SLC values across all scenarios due to its smaller scale and minimal material complexity. However, even this bridge achieved a modest +0.40% gain under the 10% targeted improvement scenario, reflecting the model’s diagnostic capability in identifying specific underperformance areas. These findings confirm that risk-based prioritization enables more efficient sustainability gains by directing resources toward the most influential indicators. This target strategy promotes project-specific decision-making and offers a more strategic alternative to uniform improvement approaches. A full breakdown of the Sustainability Level Change (SLC) values under all scenarios is presented in

Table 18. Comparative SLC Outcomes Under Targeted and Uniform Improvement Scenarios for Each Bridge Case.

Szeghalmi Berettyó Bridge
Scenario Description	STL	SBL	SLC	Improvement%
Original (risk-informed only)	0.6357	0.6122	0.0236	+3.86%
5% on top 6 indicators	0.6521	0.6233	0.0288	+4.62%
10% on top 6 indicators	0.6733	0.6381	0.0352	+5.52%
5% on all indicators	0.6675	0.6428	0.0247	+3.84%
10% on all indicators	0.6993	0.6734	0.0259	+3.85%
Ecsegfalvai Hortobágy-Berettyó-Csatorna Bridge
Scenario Description	STL	SBL	SLC	Improvement%
Original (risk-informed only)	0.6285	0.6116	+0.0169	+2.76%
5% on top 6 indicators	0.6447	0.6226	+0.0221	+3.55%
10% on top 6 indicators	0.6609	0.6336	+0.0273	+4.31%
5% on all indicators	0.6599	0.6422	+0.0177	+2.76%
10% on all indicators	0.6914	0.6728	+0.0186	+2.77%
Gyomai Hármas-Körös Bridge
Scenario Description	STL	SBL	SLC	Improvement%
Original (risk-informed only)	0.6138	0.6003	+0.0135	+2.25%
5% on top 6 indicators	0.6293	0.611	+0.0184	+3.01%
10% on top 6 indicators	0.668	0.6216	+0.0233	+3.75%
5% on all indicators	0.6445	0.6304	+0.0142	+2.25%
10% on all indicators	0.6752	0.6604	+0.0149	+2.26%
Szeghalmi Foki-Sebes-Körös Bridge
Scenario Description	STL	SBL	SLC	Improvement%
Original (risk-informed only)	0.6558	0.6007	+0.0627	+10.44%
5% on top 6 indicators	0.6802	0.6115	+0.0687	+11.24%
10% on top 6 indicators	0.6971	0.6223	+0.0748	+12.02%
5% on all indicators	0.6966	0.6308	+0.0658	+10.43%
10% on all indicators	0.7297	0.6608	+0.0689	+10.43%
Nagyszénási Mágocs-ér Bridge
Scenario Description	STL	SBL	SLC	Improvement%
Original (risk-informed only)	0.4019	0.4050	-0.0032	−0.79%
5% on top 6 indicators	0.4121	0.4128	-0.0007	−0.17%
10% on top 6 indicators	0.4223	0.4207	+0.0017	+0.40%
5% on all indicators	0.4220	0.4252	-0.0033	−0.78%
10% on all indicators	0.4421	0.4455	-0.0035	−0.79%

.

To enhance comparative clarity, Figure 6 visualizes the Sustainability Level Change (SLC) outcomes across all scenarios and bridge case studies, highlighting the consistent advantages of targeted improvement strategies over uniform approaches.

These findings confirm the model’s ability to support targeted sustainability planning under realistic improvement scenarios. Simulating 5% and 10% enhancements, applied either to top-ranked indicators or uniformly across all indicators, demonstrated that risk-informed prioritization consistently delivers more efficient sustainability gains. The model effectively directed attention toward a focused set of high-impact indicators, C1 (Lifecycle Cost), E1 (Global Warming Potential), and S1 (Worker Health and Safety), which drove measurable improvements across all three sustainability pillars. Even in cases with low material intensity or limited risk exposure, such as the Nagyszénási Mágocs-ér Bridge (B5), the model identified targeted pathways for moderate improvement, reinforcing its diagnostic value. These results underscore the practicality of the method for real-world projects operating under resource or time constraints. The use of 5% and 10% improvement thresholds aligns with achievable engineering interventions, such as optimized procurement strategies, material substitutions, or minor design refinements. While the model can accommodate more ambitious interventions, the selected improvement levels reflect what is currently feasible within industry practice. Importantly, this approach offers a scalable framework for strategic sustainability planning, enabling infrastructure managers to optimize performance by balancing lifecycle impacts with risk-informed priorities.

10.2. Structural and Material Influences on Sustainability Outcomes

To further explore how structural and material characteristics affect sustainability performance under the risk-informed model, a cross-case comparison was conducted across the five Hungarian bridge case studies. Normalized indicator scores reveal consistent patterns linked to each bridge’s structural configuration and material composition. The Szeghalmi Berettyó Bridge (B1), featuring a steel–concrete composite superstructure and a longer span (84.36 m), exhibited significantly higher environmental burdens, particularly in Global Warming Potential (E1 = 0.4509) and Energy Demand (E6 = 0.4628), reflecting the high embodied emissions associated with steel-intensive structures. In contrast, Bridges B2–B5, characterized by shorter-span reinforced concrete slab designs, demonstrated lower environmental impacts. Notably, Bridge B5 recorded the lowest scores (e.g., E1 = 0.0016; E4 = 0.0601), attributed to its reduced material intensity and minimal manufacturing-stage emissions. Economic indicators such as Lifecycle Cost (C1) and Cost Overrun Control (C6) tended to be more favorable in longer-span bridges, likely due to higher initial investments resulting in better long-term cost efficiency. Social performance also varied: Bridge B5 achieved the highest score in Worker Health and Safety (S1 = 0.7200), likely influenced by recent rehabilitation interventions that enhanced on-site safety measures. These findings confirm the model’s sensitivity to both lifecycle risks and structural-functional attributes, reinforcing its utility as a decision-support tool for sustainability planning across diverse bridge types, materials, and span configurations. Figure 7, Figure 8 and Figure 9 illustrate these performance variations across the environmental, economic, and social dimensions, respectively.

These findings confirm the model’s sensitivity to material variability and structural-functional attributes, demonstrating its capacity to dynamically adjust sustainability assessments based on project-specific configurations. Although the case studies are geographically concentrated, the model’s adaptability to diverse bridge typologies is ensured by its data-driven architecture, which integrates BIM-derived inventories (e.g., BoM, BoQ, IFC files) with recalibrated LCSA indicator weights. This mechanism allows the framework to accommodate differences in material composition, geometry, and construction methods, ensuring reliable sustainability evaluation across varying bridge types. The cross-type comparison, particularly between the steel–concrete composite bridge (B1) and the reinforced concrete slab bridges (B2–B5), illustrates the model’s ability to reflect these variations through measurable impacts on Sustainability Level Change (SLC) scores. Specifically, the steel-intensive B1 bridge exhibited higher Environmental SLC deltas due to elevated Global Warming Potential (E1) and Energy Demand (E6) scores. In contrast, the simpler RC slab bridges demonstrated more stable Economic SLC profiles, attributed to lower lifecycle cost variability and reduced maintenance frequency. The risk-informed weighting mechanism further amplifies these material-driven differences, ensuring that sustainability assessments are not solely based on raw material quantities but also reflect the risk exposure and lifecycle relevance of each material and process. As a result, material-intensive designs like B1 not only influence direct environmental burdens but also cascade into economic and operational sustainability components, highlighting the interconnected nature of material choices and long-term infrastructure performance.

11. Practical Application of the Risk-Informed BIM-LCSA Framework

The proposed Risk-Informed BIM-LCSA Framework is designed to provide practitioners with a structured, data-driven methodology for evaluating and enhancing the sustainability of bridge infrastructure projects. To ensure practical applicability, the framework can be implemented through the following five key stages:

• Risk Factor Identification and Mapping: Conduct a systematic assessment of project-specific risks across the bridge lifecycle. Utilize expert panels to evaluate each risk’s probability, severity, and sustainability impact across the environmental, economic, and social pillars. Map these risks to relevant sustainability indicators based on thematic relevance and lifecycle phase alignment, ensuring that risk–indicator relationships are clearly defined and context-specific.
• BIM-Enabled Inventory Extraction: Develop a detailed Building Information Model (BIM) of the bridge project using tools such as Tekla Structures. Export structured data files, including the Bill of Materials (BoM), Bill of Quantities (BoQ), and Industry Foundation Classes (IFC) models. Process IFC files using automated scripts (e.g., Python with IfcOpenShell) to extract material properties, quantities, and component relationships into structured CSV inventories compatible with lifecycle assessment platforms.
• Lifecycle Sustainability Assessment (LCSA) Modeling: Import the structured inventory data into OpenLCA or equivalent LCA software. Perform environmental and economic impact assessments using standardized methods such as ReCiPe, ILCD, and EF 3.0. For data gaps (e.g., transport distances, maintenance intervals), apply validated assumptions from comparable projects to maintain dataset consistency and modeling reliability.
• Risk-Informed Indicator Weight Calibration: Implement a dual-stage weighting process: first, evaluate risks at the pillar level using a scaled assessment (High, Moderate, Low). Then, apply expert scoring (S_j) to reflect each indicator’s sensitivity to the identified risks. Calculate cumulative Risk Contribution Scores (RCS) to derive normalized, risk-informed indicator weights (W_j), ensuring that the sustainability assessment is vulnerability-driven and context-aware.
• Sustainability Level Change (SLC) Evaluation and Scenario Testing: Calculate Sustainability Level Change (SLC) values by applying risk-informed weights to the normalized indicator scores under both baseline and improvement scenarios. Use these results to identify key performance gaps and prioritize targeted interventions that deliver measurable sustainability enhancements throughout the bridge lifecycle.

This structured process ensures that sustainability assessments are transparent, replicable, and aligned with actual project vulnerabilities. The modular design of the framework enables flexible adaptation across various bridge types, geographic contexts, and project scales, providing a robust decision-support tool for practitioners involved in bridge management and infrastructure sustainability planning.

12. Conclusions and Recommendations

Most current sustainability assessment models for bridges remain fragmented, focusing on isolated lifecycle stages, relying on limited criteria, and rarely integrating risk in a structured manner. This study addressed these limitations by developing and validating a BIM-enabled, risk-informed framework for evaluating sustainability across the full lifecycle of bridge infrastructure. By combining Building Information Modeling (BIM), Life Cycle Sustainability Assessment (LCSA), and ISO-aligned risk prioritization, the model enables a comprehensive assessment of environmental, economic, and social performance using real project data. Applied to five Hungarian bridge case studies, the framework demonstrated measurable improvements through risk-informed weighting alone, achieving Sustainability Level Change (SLC) gains between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked high-risk indicators, resulted in SLC gains of up to +12%. These results confirm the framework’s ability to guide actionable, lifecycle-based sustainability planning across diverse bridge types and project conditions. Even underperforming bridges showed notable improvements when targeted strategies were applied, demonstrating the model’s adaptability across varying baselines. Compared to earlier tools that often treat social factors qualitatively or neglect the dynamic nature of risks, this approach delivers a data-driven, lifecycle-wide assessment by integrating BIM-derived inventories (via Tekla Structures) and LCA outputs (via OpenLCA), while embedding ISO-based risk logic directly into the evaluation process. The custom SLC algorithm transforms sustainability assessment from a passive diagnostic tool into a dynamic planning mechanism, enabling effective comparisons between baseline and improved scenarios. Across the five case studies, bridge-specific characteristics, such as span length, superstructure material, and rehabilitation history, significantly influenced sustainability outcomes. For example, the steel–concrete composite Szeghalmi Berettyó Bridge exhibited the highest environmental burdens (E1 = 0.4509; E6 = 0.4628), consistent with the embodied impacts of steel structures. In contrast, the smaller RC-slab Nagyszénási Mágocs-ér Bridge recorded the lowest environmental loads (E1 = 0.0016), while outperforming in social dimensions such as Worker Health and Safety (S1 = 0.7200) due to recent rehabilitation. These cross-case results validate the model’s diagnostic sensitivity and its ability to account for design, material, and context-driven sustainability dynamics. From a practical perspective, the study recommends that project managers adopt risk-informed weighting to prioritize impactful indicators, especially under resource constraints. Regulators and consultants are encouraged to integrate BIM–LCSA workflows early in the project lifecycle for proactive sustainability alignment, while infrastructure owners should embed the framework into digital platforms to facilitate strategic, long-term sustainability tracking.

To support practical implementation, a decision-support tool is proposed to integrate the model into existing infrastructure management systems. This tool would accept BIM-derived inventories and risk scores, compute normalized sustainability indicators via a linked LCA engine, and apply the SLC algorithm to simulate baseline and improvement scenarios. Results could be visualized through a user-friendly interface displaying ranked indicators, sensitivity scores, and potential sustainability gains under different intervention strategies. Future research should focus on enhancing the model’s predictive capacity by incorporating dynamic, data-driven methodologies. The integration of machine learning algorithms would enable continuous adjustment of risk weightings based on evolving project data, allowing for adaptive and intelligent sustainability optimization across various infrastructure scenarios. Additionally, probabilistic tools such as Monte Carlo simulation could be employed to evaluate the model’s robustness under uncertainty. This would facilitate sensitivity analyses of expert-derived weightings and indicator variability, providing deeper insights into system stability and input reliability across a range of planning scenarios. While this study demonstrates the model’s applicability through five real-world bridge case studies, the limited sample size and geographic focus in Hungary may constrain broader generalization. Climate-related risks, such as flooding, freeze–thaw cycles, and material degradation linked to rising temperatures, were not explicitly integrated, despite their potential long-term impacts on sustainability performance. Addressing these factors in future studies will enhance the model’s robustness and applicability across different environmental and climatic contexts. Furthermore, broader implementation efforts should consider variations in regional regulatory standards, cultural expectations, and infrastructure maturity levels, all of which may influence sustainability priorities and performance outcomes. Finally, scenario-based sensitivity analyses confirmed the model’s responsiveness to shifts in pillar prioritization. Comparative assessments between balanced weighting (Scenario 1), high-impact dominance (Scenario 5), and high–low balance (Scenario 6) revealed that sustainability scores are sensitive to changes in pillar emphasis, particularly when economic indicators are prioritized. This confirms the model’s flexibility to support customized decision-making aligned with evolving stakeholder objectives and sustainability agendas.