Search Results (12,752)

Interoperability remains a central issue in multi-performance building simulation, where heterogeneous domain-specific tools must be combined despite differences in modeling formalisms, numerical solvers, and execution schemes. Existing approaches, including data exchange standards and component-based frameworks such as the Functional Mock-up Interface (FMI), address specific levels of interoperability but often require model-level access, component wrapping, Functional Mock-up Unit (FMU) packaging, or framework-specific integration. This paper examines service-level interoperability, where domain-specific simulation tools are exposed as autonomous web services coordinated through an external orchestration mechanism. A structured, JSON-based Pivot DataSet (PDS) organizes data exchange between services, while coupling strategies are implemented at the orchestration level to manage interactions without accessing internal model structures. The approach is evaluated using a classroom case study from the Agence Nationale de la Recherche (ANR) COSIMPHI research project, focusing on communication overhead, synchronization constraints, and coupling behavior in distributed co-simulation. Under the investigated weak-coupling conditions, the waveform relaxation method (WRM) reduces synchronization iterations by 144× over one day and by approximately 3319× over one month compared with minute-by-minute sequential chaining. These results, obtained under weak thermal–acoustic coupling conditions, highlight the relevance of service-level interoperability and orchestration-level coupling for distributed building-performance simulation workflows involving independently developed domain tools. Their generalization to stronger coupling regimes, however, remains a direction for future work. Full article

The 1991 Gulf War contaminated more than 49 km² of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km² case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly. Full article

In the current Architecture, Engineering, Construction, and Operations (AECO) sector, the demand for a skilled workforce capable of responding to rapidly changing needs is increasing. However, academic programs are struggling to keep up with this transformation. The integration of Building Information Modeling (BIM) tools into design studios and the objective evaluation of the pedagogical outcomes of this process are not yet fully clear. This study develops a pedagogical evaluation framework to integrate professional BIM competencies into architectural design studio curricula. This framework aims to measure student competency development and guide the restructuring of academic programs for BIM-supported education. A mixed methodology was adopted in the research; utilizing a combination of purposive and convenience sampling techniques, the studio performances, submission processes, and survey data of 409 students studying in architecture and interior architecture departments over a four-year period were analyzed longitudinally using the developed measurement-evaluation model. The proposed framework serves to pedagogically grade students’ in-studio performance and to measure acquired competencies with structured criteria. The qualitative data obtained from the surveys were analyzed through thematic and content analysis. The research revealed that students possessed limited technical skills in BIM projects and experienced deficiencies in collaboration and data management. Furthermore, it determined that instructors’ lack of knowledge regarding integrating BIM into the curriculum negatively impacted students’ learning processes. This study recommended enhancing teacher training for BIM-supported education, improving collaboration and coordination skills, and aligning the curriculum with professional requirements. The findings provide a framework that not only better prepares students for professional life but also helps bridge the gap between education and industry. Through this framework, students’ competencies can be measured at the pedagogical level. Full article

Resilience is critical for construction projects to cope with diverse risks and uncertainties. Inter-organizational relationship governance has been widely recognized as an important means of strengthening project resilience. However, existing research has paid limited attention to how different governance mechanisms interact and combine to enhance resilience in construction projects. Drawing on a configurational perspective, this study examines how contractual, hierarchical, and network governance jointly contribute to construction project resilience. Based on survey data from 289 practitioners, fuzzy-set qualitative comparative analysis (fsQCA) is employed to identify the governance configurations associated with high project resilience. The results reveal three configurational pathways leading to high resilience: (1) relational–structural network governance coupled with contractual governance; (2) a combination of contractual, hierarchical, and network governance; (3) relational–cognitive network governance coupled with contractual governance. These findings offer important theoretical and practical implications for understanding the role of hybrid governance in the resilience of construction projects. Theoretically, this study extends resilience research by demonstrating that contractual, hierarchical, and network governance do not operate in isolation but jointly enhance project resilience through distinct configurations. Practically, these findings offer guidance for project stakeholders to optimize and integrate governance mechanisms, thereby improving their capacity to anticipate, respond to, and manage internal and external crises. Full article

Universities are increasingly expected to contribute not only to teaching and research, but also to public-sector innovation, regional development, and digitally enabled governance. This article examines how higher education institutions organize that contribution by comparing two university-based transfer architectures: Smart-EDU Hub @ SNSPA in Bucharest and the distributed transfer portfolio of RheinMain University of Applied Sciences and Arts (HSRM). Using a qualitative comparative case-study design based on the document analysis of internal strategy and regulatory documents, institutional webpages, and European policy frameworks, the study analyzes the mission framing, organizational form, program architecture, trust infra-structure, and scaling logic. The documentary analysis indicates that Smart-EDU Hub is formally presented and institutionally organized as a centralized, branded, mission-led platform that bundles conferences, courses, projects, visiting scholars, and publication channels under a recognizable public-facing identity. HSRM, by contrast, is documented as a distributed transfer portfolio linking transfer strategy, dialogue formats, digitally supported teaching, administrative digitalization, continuing education, and AI support services. The comparison should therefore be read as an analysis of formal and publicly documented transfer architectures, not as an evaluation of actual institutional performance, stakeholder experience, or societal impact. The article contributes to Administrative Sciences by conceptualizing university transfer for smart governance as a public-management and governance-design problem. It develops an analytical hybrid transfer-architecture framework in which a visible hub is combined with distributed specialist nodes, shared quality assurance, and explicit safeguards for ethics, cybersecurity, and trustworthy AI. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

Flood risk is increasingly shaped by the combined effects of climate change and the vulnerability of built environments, while building-level flood damage severity analysis is often constrained by limited data availability. This study develops a transparent and reproducible framework for analyzing building-level flood damage severity under climate-adjusted hazard conditions in data-constrained environments. The framework integrates administrative post-event damage records, GIS-based terrain information, a terrain-based proxy flood-depth reconstruction procedure, and a standardized Rhine Atlas/ICPR depth–damage relationship. Representative terrain-based proxy flood depths are reconstructed using building locations, terrain elevation, and settlement-level exposure assumptions. Observed damage categories are not used to assign proxy flood depths directly, but serve exclusively as empirical ordinal reference information for ordinal consistency assessment of model-derived damage severity. Climate effects are incorporated through a simplified hazard adjustment based on projected changes in extreme precipitation intensity. The framework is applied to 413 residential buildings affected by flood events in Serbia during the period 2016–2021. Results show a consistent nonlinear relationship between terrain-based proxy flood depth and ICPR-derived structural damage severity, as well as a strong influence of terrain elevation on relative hazard intensity. Climate-adjusted sensitivity scenarios indicate that even moderate increases in extreme precipitation lead to measurable increases in structural damage severity and an upward shift in model-derived damage levels. The proposed framework provides a practical approach for flood damage severity analysis in data-constrained environments, supporting improved decision-making in sustainable flood risk management and climate adaptation planning. Full article

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg C ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg C ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

Climate change poses significant challenges to urban infrastructure, particularly semi-outdoor stadiums, which are highly susceptible to climate-related hazards. The current research community has gradually recognized this issue but lacks systematic insights into the capacity and methods for stadiums to cope with climate change. This review assesses the vulnerability and climate performance of semi-outdoor stadiums and identifies adaptation strategies to enhance resilience. A systematic literature review was conducted using Web of Science and Scopus databases. Key themes included thermal comfort, wind comfort, and rain protection. Thermal comfort and CFD emerged as the most dominant research focus. This review highlighted the importance of long-term climate adaptation strategies, including the use of sustainable materials, improved ventilation, and renewable energy systems. The results also indicate a lack of research on tropical climates and that more comprehensive adaptation strategies are needed. The core contribution is a structured vulnerability framework that transforms scattered evidence into an integrated knowledge structure, identifying not only dominant themes and missing links but also cross-cutting trade-offs. These findings provide actionable insights for urban planners, architects, and policymakers aiming to enhance stadium resilience and contribute to sustainable urban development goals. Full article

This study aimed to develop a stand yield table for Acacia mangium plantations in Vietnam and to analyze their economic feasibility. Field measurements were conducted across 12 temporary sample plots established in A. mangium plantations aged 1–7 years in northeastern Vietnam, using a chronosequence sampling design. Based on this, regression analyses were performed to develop stand growth models for DBH, H, SD, BA, and V. NPV measures the absolute value of the investment at a specified discount rate (4.0%, 5.5%, and 7.0%), while IRR identifies the break-even discount rate. At rotation ages of 5–7 years, IRR values are negative—a mathematically valid result arising from non-conventional cash flow structures in which Norstrom’s sign-change criterion is not satisfied; positive IRR values (~4%) are obtained from rotation age 8 onward. A sensitivity analysis confirmed that increasing yields is the most effective strategy for increasing plantation viability. This study serves as a fundamental resource for plantation projects in Vietnam and emphasizes the necessity of management improvements to create sustainable plantation operations. Full article

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence of climate change on its future distributional dynamics within this region remains poorly quantified. This study employed Maximum Entropy (MaxEnt) species distribution modelling to assess current and projected habitat suitability for R. ferrugineus across the Arabian Peninsula (~3.2 million km²) under two contrasting Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for the mid-century (2050) and late-century (2070). The model was calibrated using 52 spatially thinned occurrence records and six non-collinear environmental predictors selected following Variance Inflation Factor (VIF) analysis, with sampling bias corrected through a kernel density-based background weighting approach. Model performance was robust, with mean training and test AUC values of 0.921 ± 0.023 and 0.840 ± 0.052, respectively, and a mean TSS of 0.583 ± 0.046. Precipitation of the coldest quarter (Bio 19) and precipitation seasonality (Bio 15) emerged as the most influential predictors of habitat suitability, followed by elevation. Currently, approximately 727,589.8 km² (26.11%) of the peninsula is classified as suitable habitat, concentrated along the eastern Arabian Gulf coastline and the western Red Sea plain. Under SSP1-2.6, suitable habitat is projected to expand by 16.34% and 31.60% by 2050 and 2070, respectively. Under the high-emission SSP5-8.5 scenario, expansions are considerably more pronounced, reaching 34.11% by 2050 and 60.15% by 2070, with total suitable area approaching 1,158,474.8 km² (41.58%) by late-century. Habitat contraction was negligible across all scenarios, indicating a unidirectional range expansion dynamic. These findings highlight the substantial threat posed by climate-driven habitat expansion of R. ferrugineus and provide spatially explicit projections to inform proactive biosecurity planning and pest management strategies for date palm cultivation across the Arabian Peninsula. Full article

Construction site fires caused by spatiotemporal overlaps between hot work (ignition sources) and combustible substances remain a critical concern. The traditional method identifies fire hazards based on the intersections among hot works and other works with combustible substances. However, the intersections between hot work and built elements containing combustible materials are ignored, which can also lead to fire accidents. In addition, the detection of such intersections relies on the computationally intensive proximity search from the ignition source to the potential combustible substances, resulting in a long-time calculation in large construction projects with the dynamic construction process. To address this limitation, this study proposes a voxel-based fire hazard identification method applicable to large 4D-BIM models, fast and accurately. By discretizing BIM into reusable LEGO voxels, both the construction activities and the building components can be mapped to the voxels, enabling a simultaneous intersection identification between ignition sources and both activities and BIM elements. In addition, voxel-based proximity searching is efficient, enabling a fast and accurate fire hazard identification. Validation tests demonstrate high accuracy with calculatable spatial error (maximum 0.57 m for 200 mm voxels) and superior efficiency (126–1368% faster than mesh-based methods). By reusing the voxelized BIM data, the speed can be enhanced by between 400% and 1975%. This method offers an efficient and reliable digital solution for proactive construction fire safety management in 4D-contexts. Full article

This study addresses the disconnect between environmental impact assessment (EIA) outputs and construction contract management, which limits the practical effectiveness of environmental decision-making in project delivery. To bridge this gap, ImpactPredict—a data-driven decision-support framework—is developed to integrate environmental impact data with environmental-based contractual risk assessment. The methodology combines: (1) severity–likelihood environmental scoring with contractual weighting to generate quantitative indicators of claim likelihood before and after mitigation; (2) developing the proposed framework using Microsoft Excel and Power BI; (3) validation using six case study energy projects in Egypt, enabling cross-case comparative analysis; and (4) statistical analysis to test the model’s sensitivity and uncertainty. The results show consistent reductions across all projects, with mitigation leading to an average 40% risk reduction across all case studies, and significant decreases in predicted claims. Linear regression analysis between initial contractual risk (CR) and residual contractual risk (RCR) produced the predictive equation R$\widehat{\mathrm{C}}$R = 4.93 + 0.351(CR). The regression coefficient and hypothesis testing (t = 3.367, p = 0.028 < 0.05) provide preliminary evidence that initial contractual risk is a statistically significant predictor of residual contractual risk. The coefficient of determination (R² = 0.758) indicates that approximately 75.8% of the variance in residual risk is explained by the initial risk conditions. In addition, low prediction error values (mean absolute error = 1.17; root mean square error = 1.28) demonstrate satisfactory predictive stability and model reliability. The sensitivity analysis indicates that the model exhibits proportional responsiveness to all input variables, with severity and likelihood identified as dominant drivers of risk magnitude, while contractual weighting governs risk translation into project performance outcomes. These findings confirm that environmental impacts can be operationalized as quantifiable contractual risk drivers. The study concludes that embedding contract-integrated environmental intelligence within accessible analytical platforms enhances decision-making, supports measurable performance improvement, and transforms EIA into a proactive risk management tool. Full article

Construction contracts are vital for governing responsibilities in large-scale infrastructure projects, but their increasing complexity often leads to interpretation difficulties, disputes, and delays. Despite advances in natural language processing (NLP), automated analysis of construction contract clauses remains limited in project management. This study proposes a text classification framework integrating transformer-based contextual embeddings (BERT, ALBERT, RoBERTa, and DistilBERT) with machine learning and deep learning models (RNN, GRU, and LSTM) to analyze FIDIC and JCT contract provisions. Two multi-class classification tasks were defined: Dataset 1 (DS1) focusing on obligations, operational actions, optional provisions, general statements, and Dataset 2 (DS2) covering cost, quality, and time dimensions. Experimental results show that deep learning models consistently outperform traditional machine learning algorithms. Specifically, LSTM combined with RoBERTa and DistilBERT achieved the highest accuracy levels of 98.06% and 98.33% for DS1. The framework may support transparent contract governance by enabling faster and more consistent identification of contractual clauses. From a sustainability perspective, the findings suggest potential process-level contributions to economic efficiency, administrative workload reduction, and decision-making support throughout the project lifecycle. Full article