Search Results (206)

Building Information Modelling (BIM) is a key component of the digital transformation of the construction industry. The government of Bulgaria has accepted a National Strategy for The Digital Transformation of the Construction Industry which defines a set of goals and measures to achieve these goals. In this paper, we examine the goals and measures of the strategy related to adoption of BIM in the government sector and purpose a model for the adoption, emphasizing the use of existing standards and open-source solutions. Full article

Building Information Modelling (BIM) has digitized construction, yet automated cost estimation still suffers from fragmented data and deterministic forecasts that ignore uncertainty. To address this gap, this study introduces a novel framework integrating agentic artificial intelligence (AI) with large language models (LLMs) to enable probabilistic cost estimation from disparate BIM data. The system employs four specialized collaborative agents operating via a shared memory module centered on an LLM with natural language understanding, code generation, and chain-of-thought reasoning. A prototype using GPT-4 Turbo, AutoGen, and Monte Carlo simulation was tested on three real-world structures. Compared to three baselines, the framework reduced processing time (4.2 vs. 18.5–68.0 min), manual interventions (0.8 vs. 9–14), and improved entity resolution accuracy (86.5% vs. 46–62%) with well-calibrated probabilistic forecasts, achieving 86.0% empirical coverage for nominal 90% prediction intervals (Prediction Interval Coverage Probability [PICP] = 86.0%, Prediction Interval Width [PIW] = 0.28; p < 0.01). Qualitative analysis confirmed effective semantic conflict resolution and actionable risk visualization via tornado diagrams. The framework tackles long-standing BIM estimation challenges by delivering probabilistic, transparent outputs. Future work includes digital twin integration, open-source LLM deployment, and during-construction forecasting. Full article

In recent years, an essential approach for promoting and implementing efficient sustainable construction practices has been considered through the integration of Building Information Modeling (BIM) and Life-Cycle Assessment (LCA). The introduction of OpenBIM, which is characterized by its collaborative and interoperable nature, offers an ideal framework to enhance this integration. This paper conducts a systematic review of the literature concerning the practices applied to integrate BIM and LCA, focusing on the present trends, challenges, and opportunities as well as on how the concept of OpenBIM can be applied to tackle the identified issues and gaps. Based on an intense review of the literature to identify the ways currently used to exchange data, this paper proposes a robust framework to create Information Delivery Specifications (IDS) as a solution to the identified gaps to attain an effective implementation, ultimately contributing to sustainable buildings’ practices and enhancing the integration of OpenBIM and LCA. OpenBIM emphasizes interoperability and collaboration by using open standards like Industry Foundation Classes (IFCs), which, when combined with LCA, offer a powerful method for the practice of sustainable building and provide a transparent evaluation of the environmental impacts of building materials and processes. This paper explores the definitions, key concepts, types of the exchanged data, and methods of integration and therefore provides insights into their potential in addressing the gaps that the construction industry is currently facing. The framework of integrating OpenBIM and LCA will be developed as a tool; therefore, it will combine an automated validation option by using IDS, create an enriched IFC file(s), dynamically map the data to an external LCA repositories, and incorporate feedback and reporting mechanisms. All those will be combined to address the most persistent shortcomings in the reviewed studies related to the integration of BIM and LCA. The framework will promote a holistic approach covering the early design benchmark to the detailed Whole Building LCA (WBLCA), including the operational and end-of-life phases. This next-generation workflow will align closely to the principles of OpenBIM, leading to improvement in the efficiency, accuracy, and deeper understanding of the environmental impacts by stakeholders over the construction lifecycle of buildings. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

Construction projects are inherently complex systems that require effective decision-making during the schematic and preliminary design stages, when major design alternatives are evaluated and a substantial proportion of project costs is determined. Therefore, rapid and reliable cost estimation is critical at this stage. However, traditional estimation methods based on two-dimensional drawings often yield inconsistent quantity take-off (QTO) results due to reliance on manual interpretation. In the context of this study, this stage refers to a schematic-to-preliminary design phase in which major building elements have been modeled at an LoD2 level and can provide approximate quantity information for cost evaluation. Although BIM-based QTO approaches have been proposed to address these limitations, existing studies predominantly focus on quantity extraction rather than the integration of quantity and cost information. Additionally, the reliability of BIM-based estimation depends heavily on BIM model quality and structured data preparation. To address these issues, this study proposes an openBIM-based integrated framework that systematically links BIM, model quality verification, QTO, and schematic cost estimation within a unified workflow. The proposed method integrates IFC-based quantity data with a structured cost database to support systematic cost estimation during the early design stage. This study focuses on building frame elements because of their significant impact on project cost. The methodology comprises three main steps: (1) BIM and quality preparation based on LoD2-level requirements, (2) IFC-based automated QTO, and (3) model-based schematic cost estimation through QTO–cost DB linkage. A prototype system was developed to validate the proposed framework. The results indicate that the proposed approach improves estimation reliability by reducing risks associated with model quality deficiencies and manual processes. Furthermore, the integrated workflow enables faster estimation and supports more consistent cost evaluation for schematic and preliminary design decision-making. Full article

Analog, paper-based workflows remain the norm in the condition assessment of reinforced concrete infrastructure, limiting the efficiency with which diagnostic data can be used in maintenance planning. In this research, a pre-processing procedure and BIM implementation workflow for half-cell potential measurement data are proposed, implemented in open-source software, to enable data-efficient integration of diagnostic information into a BIM model. By interpreting the potential mappings as point clouds, areas relevant for maintenance planning are automatically identified through the analysis of geometric features. These identified areas are subsequently transformed into BIM objects that carry the relevant diagnostic information. The results demonstrate that the pre-processing procedure and BIM implementation workflow reduce the number of required BIM objects by over 98% (from 27,040 to 434 elements) and the IFC file size by 97% (from 77.8 MB to 2.3 MB), enabling a lightweight BIM implementation with substantially improved rendering performance. Full article

The construction (AEC) industry has consolidated Building Information Modelling (BIM) as the standard for producing and managing project information, yet its analytical exploitation in Business Intelligence (BI) environments remains manual, ad hoc and dependent on proprietary platforms. Existing literature addresses partial aspects of the problem—IFC extraction, dashboards, semantic approaches, and data quality—without articulating a coherent architecture that integrates dimensional modelling, open-standard-based ETL, granular lineage and pre-ingestion validation. This work proposes BIM2BI, a BIM-BI integration architecture organised into four functional layers (data sources, transformation and orchestration, analytical storage and exploitation) that formalises a separation of responsibilities, explicit data contracts between layers and an extensibility-without-redesign principle. The architecture is grounded in the openBIM standards IFC, IDS and BCF, adopts the IfcGlobalId as a technical key for end-to-end lineage and uses IDS as a pre-ETL quality gate with a staged three-level validation strategy. The proposal is validated empirically through an open-source reference implementation (MIT licence) applied to ten representative real-world use cases from projects in Spain and Chile, comprising 69 IFC files and approximately 4.5 GB of input data grouped in three complexity profiles, with end-to-end execution times ranging from under a minute for single-discipline deliveries to under fifteen minutes for the most demanding infrastructure case. The results demonstrate the viability of the architecture in terms of data quality, traceability, reproducibility and scalability, and document empirical findings on the real behaviour of openBIM standards within automated analytical workflows. The proposal targets BIM Managers, AEC consultants and contractors, and Data Engineers seeking auditable, vendor-independent BIM analytics aligned with ISO 19650. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

The development of Building Information Modeling (BIM) and Generative Design (GD) methods supported by artificial intelligence opens up new possibilities in the field of urban planning analyses and the verification of land-use compliance with local regulations. The aim of this study was to assess the potential of the Autodesk Forma Site Design environment, which utilizes BIM technology and generative methods, in streamlining planning processes, using the example of an analysis of the compliance of existing development with local regulations. The research was conducted based on a case study of selected plots located in the Polish city of Włocławek, covered by the 2004 Local Spatial Development Plan (LDSP). The scope of work included the analysis of planning documents, parametric modeling in Autodesk Forma Site Design, and the generation of development variants using the Archistar and One Click LCA Generative Design plugins. The results indicate that Generative Design tools can effectively support the early stages of urban planning analyses, enabling the rapid generation and comparison of land-use variants as well as a preliminary assessment of their compliance with planning regulations. At the same time, significant technological limitations were identified, including the lack of full determinism of parameters, difficulties in the automatic interpretation of complex planning regulations, and the need for manual correction of results. Consequently, the current level of development of generative tools allows for a partial streamlining of planning processes, but does not yet provide a basis for the full automation of verifying the compliance of land-use plans with local regulations. Full article

Despite the operational phase being the most cost-intensive in a building’s lifecycle, Facility Management (FM) resource optimization continues to face challenges due to fragmented and low-structured data. Building Information Modeling (BIM) offers a centralized data environment, but interoperability gaps persist between design-oriented BIM models and operational Computerized Maintenance Management Systems (CMMSs). This paper presents a scalable, standards-based methodology for BIM-CMMS integration based on the extension of Industry Foundation Classes (IFCs) and the enrichment of FM data. The proposed Python-based application leverages the open-source IfcOpenShell library to inject custom, FM-specific Property Sets (Psets), including asset condition, criticality, and maintenance schedules, directly into IFC entities. The approach transforms standard IFC files into data-rich Asset Information Models (AIMs) without relying on proprietary middleware. The methodology was validated through two residential building case studies. IFC models were successfully checked through the buildingSMART validation service, providing full interoperability across multiple IFC-compatible platforms. Integration with OpenMAINT automatically generates a complete asset database, minimizing manual data entry and reducing inconsistencies. The results confirm the feasibility of a repeatable open-standard workflow. The future development is the definition of a functional/cognitive DT, with the scope of improving the lifecycle BIM model quality and enhancing the efficiency of facility operations. Full article

Lightweight structures such as Schwedler domes offer high strength-to-weight ratios for large-span applications; however, their design typically involves time-consuming iterative processes. This study proposes an integrated parametric workflow combining geometry generation, structural analysis, and automated load application to improve both design efficiency and structural performance. The methodology is based on Python scripting within Grasshopper, enabling parametric control of dome geometry and direct interoperability with Autodesk Robot Structural Analysis Professional. Three open-apex Schwedler dome configurations were analyzed as a focused demonstration of the workflow, differing in cross-sectional typology and structural layout. The results show that the use of closed sections reduces structural mass by up to 31%, while hybrid configurations achieve significantly improved member utilization, reaching 0.87 for ribs and 0.63 for rings. Importantly, the parametric workflow enabled the rapid generation and evaluation of multiple design variants, significantly reducing modeling time and eliminating inconsistencies between geometric and analytical models. The study demonstrates that parametric modeling provides an effective framework for designing efficient dome structures, enabling both material optimization and accelerated design processes. The same parametric source is also suitable for extension into BIM and fabrication environments, as well as into life-cycle assessment, which are identified as planned continuations of this research. Full article

Saudi Vision 2030, a transformative national strategic framework, is being realized through unprecedented giga-projects such as NEOM, Qiddiya, and the Red Sea Project. These projects demand exceptional accuracy, efficiency, and sustainability, driving the need for advanced digital technologies. The integration of Digital Close-Range Photogrammetry (DCRP) and Building Information Modeling (BIM) is recognized as a pivotal solution, yet a standardized, context-aware implementation framework for the unique Saudi environment is lacking. This study develops a comprehensive, three-layer DCRP-BIM integration framework addressing technical workflows, managerial protocols, and contextual challenge mitigation specific to Saudi mega-projects. The framework was developed following Design Science Research (DSR) methodology and validated through a mixed-methods approach, incorporating a quantitative survey of 65 industry experts and qualitative thematic analysis of open-ended responses. Results demonstrated strong expert consensus on the framework’s comprehensiveness (Mean = 4.52/5) and practicality (Mean = 4.38). ‘Data Interoperability’ (RII = 0.94) and ‘Technical Expertise Shortage’ (RII = 0.90) were ranked as the most critical challenges, while ‘Enhanced Geometric Accuracy’ (RII = 0.96) and ‘Progress Monitoring Efficiency’ (RII = 0.93) were deemed the most significant benefits. Qualitative analysis highlighted the critical importance of top-down leadership support and targeted workforce development programs. The findings provide a validated, actionable roadmap for stakeholders, facilitating the adoption of this integrated technology stack to directly support the strategic objectives of Vision 2030. The study concludes that strategic focus on standardized protocols, education, and policy incentives is paramount for successful implementation. Full article

The preservation of cultural heritage presents persistent challenges due to the heterogeneity of methodologies, data structures, and information requirements involved in heritage projects. While conventional Building Information Modeling (BIM) workflows commonly rely on the Level of Development (LOD), heritage contexts require flexible and requirement-driven approaches to manage both geometric and semantic information according to stakeholder needs. To address these challenges, this study adopts a design-oriented approach that investigates the integration of the Level of Information Need (LOIN) within an OpenBIM-based Historic Building Information Modeling (HBIM) framework. The proposed methodology combines a systematic literature review with a practical case study to develop and implement an interoperable workflow articulating HBIM, Level of Information Need (LOIN), Industry Foundation Classes (IFC), and Information Delivery Specification (IDS). Within this framework, LOIN governs the relevance and granularity of information, while IFC and IDS ensure interoperability, data exchange, and conformity checking. The methodology is applied to a Moroccan heritage case study focusing on the documentation and management of building pathologies, including cracks, humidity, capillary rise, and material degradation. The results demonstrate that the proposed approach overcomes the limitations of LOD by enabling requirement-driven information management, thereby improving pathology documentation and supporting informed decision-making for cultural heritage conservation. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article