Search Results (2,407)

This article presented a method for grading and visualising corrosion in steel pedestrian bridges using Building Information Modelling (BIM). Traditional inspection methods are often manual and subjective, which reduces their reliability and repeatability. To enhance the recording and reporting of inspection results, a five-level corrosion severity grading system was developed using matched photographic data from two inspection campaigns conducted in February 2024 and April 2025. The grades were assigned based on visual signs, including surface rust, coating damage, and flaking. A Dynamo script was used to link each grade to the corresponding elements in a Revit model using colour overrides. The proposed approach enables corrosion data to be integrated into the BIM environment in a clear, structured manner. This helps engineers assess the structure’s condition, monitor changes over time, and make informed maintenance decisions. The workflow was demonstrated using case studies from a steel pedestrian bridge in Aveiro, Portugal. The method is adaptable for future digital twin applications and supports the development of BIM-based tools for bridge asset management. The workflow was applied to over 2600 elements, with 75 visually degraded cases identified and classified into five grades, demonstrating the method’s feasibility for systematic corrosion tracking. The proposed workflow was tested on a coastal steel bridge and could be generalised to other bridges with similar environmental conditions. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article

The housing deficit in developing countries is a common challenge, primarily impacting low-income populations. This paper investigated interinstitutional workflows using Building Information Modelling (BIM) as a social technology to improve the efficiency of design and construction stages in social housing projects. Following a systematic literature review, process maps were developed and applied in a case study within a Brazilian urban community, located in a coastal city with a demographic density of 3602 inhabitants per square kilometre, involving a collaboration framework between a university and municipal authorities. Based on the party’s collaboration and precise cost estimation, the results indicate that this BIM-enabled collaboration supports the governmental decision-making process and leads to more effective resource management and optimised design costs, mainly during the design and construction phases. Therefore, this study concludes that digital modelling workflows are a powerful strategy for developing social housing projects because they facilitate the inclusion of families in the design and decision-making processes. Expanding this approach through integration with geospatial and public agency data is a promising area for future research, using such models in risk assessment policies and city urban planning. Full article

The objective of this article is to evaluate the viability of implementing the Design for Manufacturing and Assembly (DfMA) methodology in the design and construction of complex wooden structures with non-standard geometry. The present study incorporates an analysis of scientific literature from 2011 to 2024, in addition to selected case studies of buildings constructed using glued laminated timber and engineered wood prefabrication technology. The selection of examples was based on a range of criteria, including geometric complexity, the level of integration of digital tools (BIM, CAM, parametric design), and the efficiency of assembly processes. The implementation of DfMA principles has been shown to result in a reduction in material waste by 15–25% and a reduction in assembly time by approximately 30% when compared to traditional construction methods. The findings of the present study demonstrate that the concurrent integration of design, production, and assembly in the timber construction process enhances energy efficiency, curtails embodied carbon emissions, and fosters the adoption of circular economy principles. The analysis also reveals key implementation barriers, such as insufficient digital skills, lack of standardization, and limited availability of prefabrication facilities. The article under scrutiny places significant emphasis on the pivotal role of DfMA in facilitating the digital transformation of timber architecture and propelling sustainable construction development in the context of the circular economy. The conclusions of the study indicate a necessity for further research to be conducted on quantitative life cycle assessment (LCA, LCC) and on the implementation of DfMA on both a national and international scale. Full article

The construction industry is responsible for nearly one-third of global greenhouse gas emissions and consumes over 50% of the planet’s natural resources. As population growth continues, the demand for these resources is expected to rise. Within this context, where business models are still largely based on the Linear Economy (LE), the Circular Economy (CE) emerges as a strategy for promoting economic development while reducing dependence on natural resource consumption. To enable the transition from LE to CE, digital tools such as Material Passports (MP) are essential. An MP compiles data and information describing the characteristics of materials to facilitate their recovery and reuse. This study aims to model the MP of a wood-frame panel commercially produced by Tecverde in Brazil. The panel was designed for a building project using 2024 version of Autodesk Revit software. The proposed MP contains 49 parameters grouped into nine categories, and the data were obtained from open databases provided by the company. The results highlight existing challenges related to sustainability parameters, as well as opportunities to incorporate circular value principles into the construction industry. Full article

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

The subject of this article is the information model of a building, the data of which can be used in the phase of operation and use. The aim of the article is to define the requirements for building information models for the purpose of data collection for spatial evidence. The article describes the method of sorting data according to the IFC standard developer for the international non-profit organization buildingSMART, or a classification system, and it describes the method of sorting the relevant data. Spatial evidence describes using the data from building information models. The conclusions of the article provide the information on how the users can collect the data from the information model and create spatial evidence in this case. Spatial evidence was selected because it is the one of the essential documents used by facility managers. Full article

Safe evacuation in high-occupancy buildings during extreme disaster events is a complex systems problem involving dynamic interactions among multiple factors. Conventional static evaluation methods, however, are limited in capturing the underlying evolution mechanisms. To address this gap, this study develops an integrated framework that combines static multi-criteria evaluation with dynamic evacuation simulation. From a “human–facility–environment” perspective, a multidimensional indicator system is established, encompassing building physical features, equipment configuration, and management performance. The entropy weight method is employed to objectively determine indicator weights, and the TOPSIS method is applied to conduct a comprehensive static assessment. On this basis, BIM and Pathfinder are used to perform microscopic evacuation simulations, and the dynamic performance data obtained are fed back into the evaluation system to verify and adjust the static results. The results show that dynamic simulation not only validates the reliability of the static evaluation but also uncovers nonlinear mechanisms and coupling effects among safety indicators during evacuation. By integrating digital simulation techniques with multi-criteria decision-making methods, this study improves the scientific rigor of safety evaluation and provides new insights for research and practice in building safety. Full article

The Extra-Long Weir Construction method for deep foundation pit construction is crucial for urban underground development. However, as excavation projects become deeper and more complex, construction safety risks increase markedly. Existing monitoring technologies and numerical simulation models face persistent challenges: high uncertainty in risk occurrence, complex environmental interactions, and difficulties in extracting effective warning signals from multi-source data. To address these challenges, this study establishes a systematic risk evaluation framework comprising 6 primary and 29 secondary indicators through Fault Tree Analysis and develops a novel DL-MSD (Deep Learning and Multi-Source Data Prediction) model integrating CNN, ResUnit, and LSTM networks for spatiotemporal sequence analysis and multi-source data fusion. Validated using 6524 samples from the Jinji Lake Tunnel project, the model employs single-factor prediction for hazard source tracing and multi-factor fusion for comprehensive risk assessment. Results demonstrate exceptional performance: 90.2% average accuracy for single-factor warnings and 77.1% for multi-factor fusion, with, critically, all severe warnings (Level I risks) identified with zero omissions. Comparative analysis with T-S fuzzy neural networks, EWT-NARX, and Random Forest confirmed superior accuracy and computational efficiency. An integrated platform incorporating BIM and IoT technologies enables automated monitoring, intelligent prediction, and adaptive control. This study establishes a data-driven intelligent early warning framework that significantly improves prediction accuracy, timeliness, and reliability in deep foundation pit construction, marking a paradigm shift from reactive response to proactive prevention. The findings provide theoretical and methodological support for safety management in ultra-deep excavation projects, offering reliable decision-making evidence for enhancing construction safety and risk management. Full article

As one of the most technically and managerially complex types of construction projects, super-high-rise buildings require deep multidisciplinary integration and intensive collaboration throughout their lifecycle. Conventional stage-based delivery models, such as the EPC, are often inadequate for handling this complexity. In recent years, the integrated Financing–Engineering, Procurement and Construction–Operation (F+EPC+O) model has emerged to address lifecycle governance challenges in building projects. This study explores how an investment-led F+EPC+O model builds dynamic capabilities to enable lifecycle collaboration in complex projects. It is based on a case study of the Xiamen Hemei Center and employs a qualitative case study approach to examine the operation of an internal F+EPC+O in the project. Drawing on multi-source data, including internal archives, BIM/CIM logs, and interviews, the findings identify three elements—lifecycle incentive alignment, internal power symmetry, and extended operation duration—that shape the Sensing–Seizing–Reconfiguring (SSR) capabilities of the approach. Specifically, Sensing is achieved through NPV-based decision frameworks and cross-stage trade-off lists; Seizing is achieved through BIM/CIM issue closure and joint rapid-cycle decision-making; and Reconfiguring is achieved through performance feedback and institutionalized knowledge repositories. The findings indicate that the SSR dynamic cycle transforms institutional integration into value co-creation, turning project complexity into a source of collaborative advantage. Full article

This study analyzes the complex relationship between Corporate Social Responsibility (CSR) and Brand Loyalty (BLy) among consumers of mobile phone services, particularly examining how Brand Image (BIm) and Brand Love (BLv) act as mediating factors in this relationship. Quantitative research with a correlational scope and cross-sectional design was conducted among 296 consumers of mobile phone services. A questionnaire comprising 26 questions was administered, with responses quantified using a five-point Likert scale. The results were processed using Confirmatory Factor Analysis and partial least squares structural equation modeling (PLS-SEM). This study revealed that CSR alone does not have a direct and significant association with BLy; however, it is associated with BIm and BLv. Furthermore, BIm and BLv were also found to be associated with BLy, demonstrating that the link between CSR and BLy is indirectly built by strengthening BIm and BLv, rather than directly. The originality of this research stems from its concurrent incorporation of BIm and BLv as mediating variables in the link between CSR and BLy. Full article

In the transition toward a more digital and data-driven construction industry, understanding how Artificial Intelligence (AI) and Building Information Modeling (BIM) are integrated is key to planning, delivering, and operating projects effectively. This review examines recent studies to identify usage patterns of AI and BIM. Searches were conducted on the Web of Science Core Collection from 2022 to 2025. After running a reproducible review protocol aligned with PRISMA 2020, which began with 1212 articles, and after a funneling process, 12 studies met the predefined eligibility criteria. In the present study, the synthesis was non-meta-analytic; instead, the information was analyzed by using standardized tabulation with a consistent format and compared using a two-level weighting scheme. The methodological approach combines full-text reading and descriptive coding with a reproducible weighting scheme that accounts for mentions per study and integrates them at the corpus level using open-source tools. The results show a strong focus on Deep Learning (DL), with a greater presence in Digital Twins (DT) and BIM Modeling (BIMM); Multidimensional BIM (4D/5D) appears as a secondary line, while the Common Data Environment (CDE) and Clash detection (CD) are sporadic. The coupling of DL-DT and DL-BIMM predominates. Simultaneously, Machine Learning (ML) provides explainable analysis on structured data, and Generative Adversarial Networks (GAN) and Automated Machine Learning (AutoML) with Machine Learning Operations (MLOps) act as enablers for data generation/adaptation and deployment with traceability. It is concluded that advancing metrics and shared datasets, especially for CDE and CD, along with developing reproducible workflows oriented toward MLOps, are key to scaling AI in real-world environments. Full article

Aligned with China’s “Dual Carbon” goals, this study addresses carbon emissions in the building sector. Existing research predominantly focuses on single-stage carbon emission assessment or separately examines the benefits of BIM applications and photovoltaic (PV) technology. There is a notable lack of studies that deeply integrate the BIM platform with dynamic assessment of building life cycle carbon emissions and PV carbon reduction strategies, particularly under the specific context of the hot-summer/cold-winter climate in Central China and a regional grid primarily reliant on thermal power. Moreover, localized and in-depth analyses targeting residential buildings in this region remain scarce. To address this gap, this study takes a residential building in Central China as a case study and establishes a BIM-based life cycle carbon emission assessment model to systematically quantify the carbon footprint across all stages. Results show total life cycle carbon emissions of 12600 tCO₂, with embodied carbon (4590 tCO₂, 36.6%) and the operational phase identified as the main emission sources. Through PV system integration and multi-scenario simulations, the study demonstrates significant carbon reduction potential: systems with 40–80 kW capacity can achieve annual carbon reductions ranging from 26 to 52 tCO₂. The 60 kW system shows the optimal balance with an annual reduction of 38.7 tCO₂ and a payback period of 3.53 years. The primary novelty of this work lies in its development of a dynamic BIM-LCA framework that enables real-time carbon footprint tracking, and the establishment of a first-of-its-kind quantitative model for PV strategy optimization under the specific climatic and grid conditions of Central China, providing a replicable pathway for region-specific decarbonization. Full article