Search Results (1,670)

The integration of smartphone-powered Virtual Reality (VR) into architectural practice is transforming how unbuilt spaces are perceived. The presented study is based on ten single-family house projects in which immersive visualization was introduced through mobile VR headsets connected to Building Information Modeling (BIM). It should be treated as a pilot study, preceding further comprehensive research on the subject. A total of 23 participants (investors and future users of the buildings at the same time) were actively involved in the design process supported by VR technology. Field of view adjustment was implemented within the BIM + VR model to align the virtual perception with the natural human visual range, improving the realism of the experience. Preliminary findings indicated that VR walkthroughs enhanced the future users’ understanding of spatial arrangements and supported informed decision-making. Over 80% of participants reported that it helped them better assess room sizes, placement of windows and doors, and furniture layout. This improved communication between investors and designers, and reduced the number of revisions required at further design stages. The use of VR to merge architecture with interior design enabled a human-scale perspective, cost optimization, and the exploitation of BIM + VR visualization potential for sustainable residential design. Full article

Power outages during extreme heat events threaten occupant safety by exposing buildings to rapid indoor overheating. However, current building thermal resilience assessments rely mainly on physics-based simulations or IoT sensor data, which are computationally expensive and slow to scale. This study develops an Artificial Intelligence (AI)-driven workflow that integrates Building Information Modeling (BIM)-based residential models, automated EnergyPlus simulations, and supervised Machine Learning (ML) algorithms to predict indoor thermal trajectories and calculate thermal resilience against power failure events in hot seasons. Four representative U.S. residential building typologies were simulated across fourteen ASHRAE climate zones to generate 16,856 scenarios over 45.8 h of runtime. The resulting dataset spans diverse climates and envelopes and enables systematic AI training for energy performance and resilience assessment. It included both time-series of indoor thermal conditions and static thermal resilience metrics such as Passive Survivability Index (PSI) and Weighted Unmet Thermal Performance (WUMTP). Trained on this dataset, ensemble boosting models, notably XGBoost, achieved near-perfect accuracy with an average R² of 0.9994 and nMAE of 1.10% across time-series (indoor temperature, humidity, and cooling energy) recorded every 3 min for a 5-day simulation period with 72 h of outage. It also showed strong performance for predicting static resilience metrics, including WUMTP (R² = 0.9521) and PSI (R² = 0.9375), and required only 1148 s for training. Feature importance analysis revealed that windows contribute 74.3% of the envelope-related influence on passive thermal response. This study demonstrates that the novelty lies not in the algorithm itself, but in applying the model to resilience context of power outages, to reduce computations from days to seconds. The proposed workflow serves as a scalable and accurate tool not only to support resilience planning, but also to guide retrofit prioritization and inform building codes. Full article

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

Fires remain a critical threat to the resilience and safety of the built environment, yet current research is often fragmented across building types, technologies, and functions. This study investigates typology-specific gaps in fire safety by conducting a scientometric review of peer-reviewed articles published between 2010 and 2025. Following a PRISMA-guided protocol, a total of 83 studies indexed in the Web of Science were systematically screened and analyzed using VOSviewer (v1.6.19) and the R-based Bibliometrix package (version 4.2.1). The dataset was classified according to building typologies, fire safety functions—detection, suppression, and evacuation—and applied technologies such as BIM, simulation platforms, and AI-based models. The results show a strong research bias toward evacuation modeling in high-rise and general-purpose buildings, while critical typologies including healthcare facilities, heritage structures, and informal settlements remain underexplored. Suppression systems and real-time detection technologies are rarely integrated, and technological applications are often fragmented rather than interoperable. A conceptual matrix is proposed to align tools with typology-specific risk profiles, highlighting mismatches between research priorities and building functions. These findings emphasize the need for integrated, data-driven, and context-sensitive fire safety strategies that bridge methodological innovation with practical application, offering a roadmap for advancing resilient and adaptive fire safety in diverse urban settings. Full article

Accurate identification of modal properties in a steel cantilever beam is crucial for enhancing numerical models and supporting structural health monitoring, particularly when numerical and experimental data are combined. This study investigates the modal system identification of a steel cantilever beam using finite element method (FEM) simulations, which are validated by experimental testing. The beam was bolted to a reinforced concrete block and subjected to dynamic testing, where natural frequencies and mode shapes were extracted through Frequency Domain Decomposition (FDD). The experimental outcomes were compared with FEM predictions from SAP2000, and discrepancies were analysed using the Modal Assurance Criterion (MAC). A model updating procedure was applied, refining boundary conditions and considering sensor mass effects, which improved model accuracy. The updated FEM achieved closer agreement with frequency deviations reduced to less than 4% and MAC values above 0.9 for the first three modes. Beyond validation, the research links the updated FEM results with a Building Information Modelling (BIM) framework to enable the development of a digital twin of the beam. A workflow was designed to connect vibration monitoring data with BIM, providing visualisation of structural performance through colour-coded alerts. The findings confirm the effectiveness of FEM updating in generating reliable modal representations and demonstrate the potential of BIM-based digital twins for advancing structural condition assessment, maintenance planning and decision-making in civil engineering practice. Full article

Building Information Modeling (BIM) has established itself as a strategic and indispensable tool for designing and implementing smart buildings within the context of smart cities. This study explores the potentialities and challenges of using BIM across the main stages of the smart building lifecycle: design, construction, and operation and maintenance. We conducted comprehensive, detailed, and interpretative literature research to extract the main concepts and knowledge, enabling us to identify the main potentialities and challenges and classify them by life-cycle phase for smart buildings. Potentialities and challenges were prioritized based on the number of projects that cited them. The inclusion criteria for identifying potentialities and challenges were based on their key attributes: significant impact, information modeling potential, integration capability with other tools and methods, and improved performance in processes and services across all life cycle phases and BIM dimensions. The findings reveal that the main potentials include optimizing information management, reducing operating costs, enhancing environmental sustainability, and enhancing decision-making processes. Furthermore, the study highlights BIM’s role in integrating technologies such as IoT, augmented reality, and energy simulations, contributing to the development of more sustainable and functional buildings. However, challenges to its full adoption persist, including financial constraints, interoperability issues between systems, a lack of specialized technical skills, and organizational resistance to change. The dependence on advanced technological infrastructure and robust connectivity poses an additional challenge, especially in developing countries, where such resources may be scarce or inconsistent. Finally, this study suggests that future research should explore the integration of BIM with emerging technologies, such as artificial intelligence and digital twins, further expanding its applicability in the smart urban context. Full article

Building Information Modeling (BIM) is a transformative tool in Sustainable Design (SD), providing measurable benefits for efficiency, collaboration, and performance in architectural, engineering, and construction (AEC) practices. Despite its growing presence in academic curricula, a gap persists between students’ recognition of BIM’s sustainability potential and their confidence or ability to apply these concepts in real-world practice. This study examines students’ understanding and perceptions of BIM and Sustainable Design education, offering insights for enhancing curriculum integration and pedagogical strategies. The objectives are to: (1) assess students’ current understanding of BIM and Sustainable Design; (2) identify gaps and misconceptions in applying BIM to sustainability; (3) evaluate the effectiveness of existing teaching methods and curricula to inform future improvements; and (4) explore the alignment between students’ theoretical knowledge and practical abilities in using BIM for Sustainable Design. The research methodology includes a comprehensive literature review and a survey of 213 students from architecture and construction management programs. Results reveal that while most students recognize the value of BIM for early-stage sustainable design analysis, many lack confidence in their practical skills, highlighting a perception–practice gap. The paper examines current educational practices, identifies curriculum shortcomings, and proposes strategies, such as integrated, hands-on learning experiences, to better align academic instruction with industry needs. Distinct from previous studies that focused primarily on single-discipline or software-based training, this research provides an empirical, cross-program analysis of students’ perception–practice gaps and offers curriculum-level insights for sustainability-driven practice. These findings provide practical recommendations for enhancing BIM and sustainability education, thereby better preparing students to meet the demands of the evolving AEC sector. Full article

The architectural heritage of the 20th century has proved to be highly vulnerable to the test of time, with slight variations in different geographical contexts. The lack of value recognition, restrictions imposition, and resulting protection has led to the loss of memory of material and immaterial values. Restoring dignity has been the primary goal of those who have given substance and vitality to the theme of Modern Restoration, inheriting from the past the method that requires, in order to catalogue each work, the essential stages of knowledge and documentation, preliminary to conservation and enhancement. It is precisely in this scenario, after analysing the experiences of institutions, bodies and associations in the field of filing and cataloguing, that the needs brought about by the digital transition were taken on board; the aim is to define, within the PRIN 2022 DIMHENSION project, an innovative operative protocol that is economically, socially and technically sustainable, aimed at the computerised management of 20th century architectural heritage. The steps are the identification of the global description of the history of the building, translation of the entire body of data into information assets (H-BIR), and the possibility of consultation using parametric models (H-BIM). A Digital Register has therefore been designed, initially for an international sample of late 20th century façade systems, which goes well beyond their dynamic documentation, creating the conditions for a platform for consulting the complex of information, structured in an H-BIR archive interfaced with an H-BIM object library. Full article

Digital transformation powered by Building Information Modeling (BIM) and Artificial Intelligence (AI) is reshaping renovation practices by addressing persistent challenges such as fragmented records, scheduling disruptions, regulatory delays, and inefficiencies in stakeholder coordination. This study explores the integration of these technologies through a case study of a Catholic church renovation (2022–2023) in Hong Kong, supplemented by insights from 10 comparable projects. The research proposes a practical framework for incorporating digital tools into renovation workflows that focuses on diagnosing challenges, defining objectives, selecting appropriate BIM/AI tools, designing an integrated system, and combining implementation, monitoring, and scaling into a cohesive iterative process. Key technologies include centralized BIM repositories, machine learning-based predictive analytics, Internet of Things (IoT) sensors, and robotic process automation (RPA). The findings show that these tools significantly improve data organization, proactive planning, regulatory compliance, stakeholder collaboration, and overall project efficiency. While qualitative in nature, this study offers globally relevant insights and actionable strategies for advancing digital transformation in renovation practices, with a focus on scalability, continuous improvement, and alignment with regulatory frameworks. Full article

This article evaluates both economic and non-economic benefits associated with the implementation of Building Information Modeling (BIM) and the digitization of processes within public sector construction projects of the Czech public sector organization (PSO). The study is based on empirical data from three real projects: the Children’s Sanatorium with Speleotherapy, the Air Rescue Services Base, and the Neurorehabilitation Center. For one of those projects, cost–benefit analysis (CBA) was applied to quantify direct financial savings and indirect operational improvements; however, other projects supported the investigation. Data sources include real project data such as BIM documentation and project budgets. The analysis reveals that BIM adoption led to measurable reductions in rework, improved coordination among stakeholders, and enhanced facility management efficiency, resulting in a cost–benefit ratio of more than 4.5. Results from this article support the results of other already made research and prove the consistency of those results. These findings demonstrate the practical value of BIM in public infrastructure delivery and provide actionable insights for decision-makers seeking to improve project outcomes through digital transformation. Full article

This paper introduces a novel BIM-based computational workflow that embeds spatial analysis directly within the Building Information Modelling (BIM) environment to support the evaluation and design of hospital emergency department (ED) layouts. Conventional analyses often depend on external software and repeated data exchange, which limit efficiency and integration with the design process. The proposed method integrates space syntax principles into Revit through Dynamo and custom Python scripts, enabling automated calculation of spatial measures linked to healthcare-specific performance indicators. The workflow was applied to two UK-based ED floor plans in a comparative case study, assessing patient-oriented aspects such as wayfinding, emergency access, and spatial privacy, alongside staff-oriented factors including workstation accessibility and visibility. Results were validated against DepthmapX to ensure consistency and reproducibility. The findings demonstrate that a BIM-native approach can streamline spatial analysis by eliminating import–export cycles, enhancing design iteration, and supporting post-occupancy evaluation. The significance of the study is in providing a decision-support framework for architects and healthcare planners in both designing new and evaluating existing ED layouts, where spatial configuration directly affects efficiency and user experience. Its main contribution is a reproducible workflow that enables real-time evaluation and strengthens the link between spatial analysis and evidence-based healthcare design. Full article

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development. Full article

The construction industry is responsible for a significant amount of greenhouse gas emissions. Therefore, buildings have the potential to play a central role in climate change mitigation. It is also known that building projects are unique and complex, which is why a high degree of process automation is necessary. Two key methods can be employed to calculate the environmental impacts of a construction process: Building Information Modeling (BIM) and Life Cycle Assessment (LCA). Currently, both methods are being considered as a part of advanced building projects. Database (BIM) models can be used as a precise inventory of materials and as an input for LCA. This study aims to (1) review the current status of published BIM-LCA workflows, (2) use a common case study among participants from various countries to compare the individual workflows and the calculated results, (3) identify potential sources of errors in all workflows on the common case study, and (4) provide recommendations and suggestions for developing BIM-LCA models based on the example of the common case study. The outcomes show that the main sources of differences emerge from not including all materials or from the inconsistencies in the exported material lists of the bill of quantities. The reasons for the missing materials stem primarily from the inadequate decomposition of composite materials, oversight of certain materials, and exclusion of thin materials such as foils. Inconsistencies arise from the incorrect handling of composite materials, the grouping of similar materials, and rounding inaccuracies. These issues highlight that errors occur early in the life cycle inventory phase, which forms the foundation of subsequent LCA phases, thereby impacting the final results and potentially leading to inaccurate assessments of environmental impacts. Ensuring accuracy at this stage is therefore critical for supporting reliable sustainability assessments. Consequently, recommendations are proposed to mitigate errors across various stages of the process to enhance the accuracy of LCA outcomes. Full article

This study proposes a 3D reconstruction method for LIDAR building point clouds using geometric primitive constrained optimization. It addresses challenges such as low accuracy, high complexity, and slow modeling. This new algorithm studies the reconstruction of point clouds at the level of geometric primitives and is an incremental joint optimization method based on the GPU rendering pipeline. Firstly, the building point cloud collected by the LIDAR laser scanner was preprocessed, and an initial building mesh model was constructed by the fast triangulation method. Secondly, based on the geometric characteristics of the building, geometric primitive constrained optimization rules were generated to optimize the initial mesh model (regular surface optimization, basis spline surface optimization, junction area optimization, etc.). And a view-dependent parallel algorithm was designed to optimize the calculation. Finally, the effectiveness of this approach was validated by comparing and analyzing the experimental results of different buildings’ point cloud data. This algorithm does not require data training and is suitable for outdoor surveying and mapping engineering operations. It has good controllability and adaptability, and the entire pipeline is interpretable. The obtained results can be used for serious applications, such as Building Information Modeling (BIM), Computer-Aided Design (CAD), etc. Full article

Spatial Allocation Problems (SAP) in multistory buildings present significant challenges, as they require the simultaneous satisfaction of complex geometric constraints and conflicting functional requirements. To address this problem, this study proposes an integrated pipeline utilizing Generative Design (GD) methodologies within a Building Information Modeling (BIM) environment to automate and optimize a 3.3 m modular multi-story spatial allocation. The core of the proposed methodology lies in the clear distinction and application of design requirements formalized as ‘Hard Constraints’ (mandatory conditions for feasibility) and ’Soft Objectives’ (metrics for performance evaluation). Hard constraints include the implementation of a boundary constraint, ensuring that all spaces remain within defined limits, and a vertical alignment constraint for fixed elements (e.g., cores), thereby ensuring geometric and structural validity. To quantify functional efficiency, three soft objectives were defined: positional preference, circulation efficiency, and functional cohesion. The methodology was validated using a four-story case study. The implemented system successfully generated numerous valid design alternatives that satisfied all hard constraints while simultaneously optimizing the three soft objectives. Aimed at architects, building designers, and computational specialists, this study offers significant practical value by providing a tool that automates the complex initial phases of space planning. This allows designers to rapidly explore and quantitatively evaluate a wide array of optimized and valid layouts. This study contributes to a systematic framework for balancing geometric validity and functional efficiency during the early design stages by presenting a concrete procedure for integrating GD and BIM to solve complex SAPs. Full article