Search Results (668)

Change orders in construction projects frequently lead to disputes, schedule delays, and cost overruns, particularly in the rapidly expanding construction sector of Saudi Arabia. Traditional techniques for resolving claims like litigation and arbitration are predominantly reactive in nature and do not facilitate proactive assessments of the impact of change orders before disputes materialize. A BIM-based framework is developed in this study to assess change orders in terms of time and cost with visualization functions through an integration of Autodesk Revit, Primavera and Navisworks, combined in a 5D virtual environment. The framework utilizes 3D modelling, scheduling and cost management (3D/4D/5D) supported by virtual reality (VR) visualization to create an interactive decision support platform for the project stakeholders. A real residential building case study was utilized to validate the framework, and a design modification was developed and analyzed using the BIM environment. The project cost has increased as a result of change order from SAR 411,437.26 to SAR 428,280.16, which is 4.1% of the total project cost. Also, there are deviations in the schedule which occurred from Month 4. The study results show that the suggested integrated BIM-based framework assesses the impact of change order, physically and visually, on the project time and cost that was required by the industry. The study is innovative in bringing together BIM layout, scheduling, and cost management with VR-supported visualization in a single decision support environment, allowing stakeholders to take into account the implications of change orders before they become real claims and disputes. The proposed framework allows transparent communication, collective decision making with stakeholders and early impact assessment, in contrast to existing approaches whose focus is primarily on claim resolution or improving coordination. This will improve project performance and how change orders are managed. Full article

Intelligent cost control in engineering, procurement, and construction (EPC) projects depends on the continuous transmission, updating, warning, correction, and reuse of cost data across multiple project stages. To analyse the resilience of this process, this study constructs an EPC project cost-data transmission network using complex network theory and Monte Carlo simulation. Eighteen core nodes and 27 directed weighted edges are identified according to EPC cost-management logic and expert evaluation. Node importance is analysed using weighted degree centrality, betweenness centrality, and PageRank, while network efficiency is used to evaluate cost-data reachability and transmission-path efficiency. Node failure, edge-weight perturbation, random edge failure, random failure and targeted attack, feedback enhancement, critical-node failure–recovery, and robustness checks are then conducted. The results show that Dynamic cost, Cost deviation warning, and Historical cost database are the three most critical nodes. Their failures reduce network efficiency by 44.54%, 37.43%, and 45.27%, respectively. Random edge failure has a stronger effect on network efficiency than edge-weight perturbation; when the edge failure probability increases from 5% to 20%, the average efficiency loss rate rises from 10.54% to 37.30%. Feedback-link enhancement increases network efficiency from 0.1858 to 0.2009 and produces a larger improvement than forward-link enhancement and random seven-edge enhancement. Robustness checks under alternative network assumptions indicate the relative stability of the critical-node identification results within the proposed network structure. The findings provide a scenario-based network perspective for identifying structurally critical nodes, vulnerable transmission links, and feedback-improvement priorities in EPC cost-data transmission. They also offer a methodological basis for future project-level calibration using BIM/5D BIM records, procurement data, cost-management platform logs, and settlement audit data. Full article

Unmanned aerial vehicles (UAVs) and deep learning (DL) have introduced a new framework for intelligent building façade defect detection, yet existing studies often focus on isolated technical components and lack a systematic evaluation of the entire pipeline. To address this gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles retrieved from the Web of Science database over the period 2021–2026. This review investigates four key domains: (1) UAV inspection path planning and data acquisition; (2) multi-modal data fusion; (3) DL-driven defect detection algorithms; and (4) 3D reconstruction and digital twin integration. Our analysis reveals the following main findings. Real-time perception-aware planning is central to UAV path planning, yet most studies lack robustness evaluations under real-world deployment conditions. Multi-modal data fusion improves detection across multiple defect types, yet edge deployment requires balancing lightweight design with recognition stability. Defect recognition algorithms increasingly adopt task-driven architectures, but limited edge-device resources demand joint optimization of efficiency and accuracy. In digital twins, systematic research is still lacking on semantically integrating recognition results into BIM for O&M decision-making, leaving the closed loop from defect detection to maintenance unresolved. This review aims to help researchers and practitioners advance UAV-based inspection from an auxiliary tool to a fully autonomous, reliable intelligent agent for refined management of the urban built environment. Full article

Mechanical, electrical, and plumbing (MEP) pipe installation discrepancies can increase rework, complicate inspection, and affect subsequent operation and maintenance. This study presents a 3D laser scanning and Building Information Modeling (BIM)-based workflow for localizing and preliminarily classifying MEP pipe installation discrepancies in a building project. Preprocessed scanned pipe point clouds are registered with BIM-derived pipe point clouds through a coarse-to-fine Scan-BIM registration process. Individual pipe instances are extracted using distance-threshold-based growing, and scan-to-BIM pipe correspondence is established using nearest-neighbor root mean square error (RMSE). Pipes with relatively large overall RMSE values are further divided into slices to identify local high-discrepancy intervals. A slice-level discrepancy distribution function $R\left(s\right)$, together with derivative-magnitude and derivative-fluctuation thresholds, is used to support preliminary Type 1/Type 2 interpretation of representative discrepancy patterns. In a student dormitory case, the workflow screened local pipes with relatively large discrepancies, localized maximum-RMSE regions, and distinguished representative connection-related discrepancies from overall offset or inclination cases. A threshold perturbation check showed consistent Type 1/Type 2 labels for the four representative cases within the tested range. The workflow provides case-study evidence for localized MEP pipe inspection, while broader validation across projects and pipe systems remains necessary. Full article

Preserving cultural heritage sites requires an interoperable digital framework capable of integrating heterogeneous spatial data and supporting immersive interaction for inspection and management. This study investigates the integration of multiple heritage data representations—including IFC-based Heritage Building Information Modeling (HBIM), terrestrial and UAV LiDAR point clouds, and 3D Gaussian Splatting reconstructions—into a unified digital management environment for the East Hall (Aula Timur) heritage site within the Bandung Institute of Technology (ITB) campus. A semantic–spatial interoperability workflow is proposed to harmonize BIM, point cloud, and landscape-scale data within a common georeferenced context, supported by a CityGML-based base map of the surrounding site. An immersive virtual environment was implemented using a head-mounted display to enable walkthrough-based inspection and damage annotation. All datasets were georeferenced within a unified coordinate system, allowing spatial registration between digital objects and the physical heritage site. The results demonstrate that multi-source heritage datasets can be integrated with high geometric accuracy, achieving TLS registration errors of approximately 2 mm and georeferencing residuals within 11.1 cm (horizontal) and 0.95 cm (vertical), while preserving semantic information and ensuring spatial coherence across HBIM, GIS, and immersive environments. The system is implemented in VR, with an architecture designed to support future MR-based on-site annotation and visualization. The proposed framework establishes a foundation for future heritage digital twin deployments and supports informed conservation decisions. Full article

Accurate tooth segmentation plays a crucial role in computer-aided dental diagnosis and treatment planning, particularly in applications such as tooth detection, lesion localization, orthodontic analysis, and implant surgery. However, panoramic dental X-ray images often suffer from tooth adhesion, low contrast, and blurred boundaries, making precise delineation difficult and potentially compromising downstream clinical analysis. To address these challenges, we propose a boundary-aware segmentation framework, termed Boundary-Aware ResU-Net (BA-ResUNet), which is built upon a ResU-Net backbone and enhanced with Coordinate Attention (CA) and explicit boundary modeling mechanisms. Specifically, CA modules are introduced into the encoder to improve spatial representation and positional awareness. In addition, a Boundary Extraction Module (BEM) is designed to capture boundary priors from shallow and deep features, while a Boundary Injection Module (BIM) progressively incorporates these cues into the decoder through foreground enhancement and background suppression. This design enables the network to better preserve inter-tooth gaps and improve boundary delineation. Experiments on the MICCAI STS-2D dental dataset demonstrate that the proposed method achieves superior performance in terms of Dice and IoU compared with representative existing methods. Ablation and qualitative analyses further show that CA and BEM/BIM play synergistic roles in improving regional overlap and boundary localization, particularly in challenging cases involving adhesion, low contrast, and indistinct contours. These results indicate that the proposed framework provides a reliable and effective solution for panoramic tooth segmentation and has promising potential for computer-aided dental applications. Full article

Accurate characterization of underground geological conditions is essential for gas control, geological hazard assessment, and safe coal mining operations. However, conventional geological interpretation methods often suffer from limited spatial accuracy due to borehole deviation, sparse geological control, and insufficient integration of multi-source borehole data. To address these limitations, this study proposes an integrated geological characterization framework combining resistivity-based image logging, borehole trajectory correction, and BIM-based three-dimensional geological modeling using 135 gas extraction boreholes from the Coal Seam 15-21050 working face of Pingdingshan No. 8 Coal Mine, China. Multi-parameter logging data, including natural gamma, apparent resistivity, natural potential, and borehole image observations, were used to identify coal seam lithology, stratigraphic interfaces, and structural characteristics. Borehole trajectory analysis revealed systematic deviation patterns controlled by borehole inclination, lithological heterogeneity, and drilling conditions, highlighting the necessity of trajectory correction for accurate spatial positioning. Trajectory-corrected borehole coordinates were subsequently integrated into a BIM-based three-dimensional geological reconstruction workflow using spatial interpolation methods. The resulting model successfully reproduced coal seam geometry, interburden distribution, and localized concealed structural anomalies. Coal Seam 15 exhibited thicknesses ranging from 2.69 to 3.47 m, while Coal Seam 16–17 ranged from 1.51 to 2.38 m. The proposed workflow improved the reliability of geological interpretation and the accuracy of spatial characterization, providing an effective technical basis for gas drainage optimization, geological hazard assessment, and intelligent underground coal mining. Full article

Seismic codes in high-risk earthquake zones magnify the embodied environmental impact of buildings by increasing structural mass. While the existing literature evaluates this burden holistically, this study isolates the environmental penalty of seismic design at the component level using building information modeling (BIM). Within this scope, an eight-story reinforced concrete residential building was modeled at LOD 300 and comparatively analyzed under TBDY-2018 (seismic) and a strictly theoretical TS-500 (gravity-only) baseline scenario. This gravity-only model acts solely as a mathematical isolation tool rather than a buildable design option. Using the CML 2001 methodology and Türkiye-specific environmental product declarations (EPDs), calculations covered the production (A1–A3), end-of-life (C1–C4), and recovery (Module D) stages of the building. Findings reveal that seismic mass increases create a nonlinear, asymmetric effect on environmental indicators. Increased concrete volume dictates the global warming potential (GWP), whereas steel reinforcement—driven by ductility demands—elevates the photochemical ozone creation potential (POCP) and acidification potential (AP) much more aggressively than concrete. Conversely, while seismic reinforcement provides a negative emission credit during the recovery stage (Module D), quantitative analysis reveals that this circular benefit is marginally small (offsetting approximately 2% of the steel-related GWP), proving mathematically insufficient to neutralize the massive upfront ecological debt. Consequently, the additional environmental penalty necessitated by seismic safety must be managed through early-stage BIM optimization and alternative mitigation strategies, such as seismic isolation. Full article

Precise dimensional conformity of precast concrete members is critical for structural performance and on-site assembly accuracy, yet conventional manual inspection remains labor-intensive and unable to scale with modern production-line throughput. Existing scan-vs-BIM approaches address geometric verification in principle but are constrained by manual registration dependencies, the absence of machine-readable IFC-linked tolerance criteria, and limited validation under real factory yard conditions. This study presents a production-scale automated shape quality control (SQC) framework that closes all three gaps simultaneously. A purpose-designed two-point target device enables fully automated, repeatable registration seed-point extraction. A formal IFC property-set-linked rule engine architecture—comprising entity extraction, deviation computation, rule interpretation, and pass/fail decision stages—replaces ad hoc script-based tolerance checking with an interoperable, auditable compliance pipeline. Factory-scale validation on precast arch segments (n = 10) and wall panels (n = 12) achieved registration RMSE of 1.25–1.95 mm, pass rates exceeding 91%, and a 37.1% reduction in inspection time versus manual methods (95% CI: 34.5–39.6%; p < 0.001; Cohen’s d = 3.89). Repeatability testing yielded ICC = 0.971 and Bland–Altman limits of agreement of [−0.45, +1.07] mm. The framework represents a substantive step toward fully digital, production-integrated quality management for industrialized precast construction. 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

City Information Modelling (CIM) and Urban Digital Twins (UDT) are pivotal for advancing smart urban planning and city management, yet empirical evidence on their real-world implementation is scarce. Following a sequential mixed-methods design, this study addresses this gap through a global investigation analyzing 33 projects across diverse geographic contexts. Findings reveal that these technologies are predominantly applied in 3D visualization (60.6%) and urban planning (48.5%), with significant underutilization in climate adaptation (9.1%) and AI-driven robotics (3.0%). A pronounced physical–social data divide exists, with infrastructure data prioritized over human-centric inputs. Technology stacks converge on GIS, IoT, and BIM. However, an interoperability paradox persists, as internal integration outpaces cross-organizational connectivity. Governance is predominantly public-sector-led, but multi-actor ecosystems are also involved. The study concludes with actionable recommendations to rebalance implementation portfolios, integrate socio-economic data, and advance both technical and institutional interoperability, thereby harnessing CIM and UDT for transformative urban planning and city management. Full article

BIM model-based construction cost estimation requires reliable linkage between model-derived building information and estimator-facing cost records. However, BIM models and structured cost databases use different descriptive logics: BIM model data primarily describe what a building component is in the model, whereas cost records primarily describe how that component is constructed, measured, and priced. When BIM model names are non-standard or properties are incomplete, this mismatch may lead to ambiguous cost item selection, particularly when candidate records differ in unit basis, material assembly, thickness, finish, fire rating, or performance requirements. To address this problem, this study proposes a multimodal model- and retrieval-guided framework for BIM model-based cost estimation. The framework converts BIM model content into standardized estimator-readable descriptions, retrieves cost database candidate entries, applies rule-based checks for unit, material, thickness, finish, and fire rating consistency, and produces reviewable cost item selections for database-based cost calculation. The method uses a multimodal model to supplement and standardize component information, while cost records remain the authority for unit prices rather than being replaced by model-generated estimates. The framework was evaluated using a BIM example containing 7374 building elements across 21 model element types, together with a structured cost database containing approximately 11,500 pricing records. The full workflow reduced unmatched categories and improved pricing coverage relative to direct cost item retrieval. The results indicated that the proposed method can improve the technical appropriateness and coverage of cost item selection. The study contributes a reviewable workflow that integrates BIM model content, multimodal description standardization, cost database candidate retrieval, rule-based specification filtering, and database-grounded cost synthesis for selecting justified cost items under practical estimating ambiguity. Full article

Graph-based spatial analysis methods are widely used to evaluate accessibility, visibility, spatial hierarchy, and movement-related properties of architectural floor plans. However, these analyses are often conducted using standalone tools and separate simplified models, which can delay design feedback and introduce additional data preparation steps. This paper presents a BIM-embedded computational workflow for configuring, computing, and visualising spatial graph analyses within Autodesk Revit using Dynamo, Python scripting, and the Accessibility and Visibility Analysis (AVA) package. The contribution is not the development of new graph algorithms, but the documentation of a reproducible workflow that sequences existing tools, graph construction settings, metric configuration, spatial measure computation, and 2D/3D visual feedback within a modelling environment. The workflow is demonstrated through a two-storey residential case study and supports accessibility, visibility, centrality measures, visual step depth, shortest path, isovist, object visibility, and activity-based origin–destination analysis. Particular attention is given to incorporating vertical circulation connections into level-based accessibility graphs for selected cross-level movement analysis. Building on prior AVA–DepthmapX verification by the authors, the paper focuses on workflow transparency, reproducibility, and multi-level accessibility representation. The findings indicate that BIM-embedded spatial graph analysis can support iterative, performance-informed design evaluation. Full article

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric Detail–Component Complexity–Texture Appearance–Semantic Information (B-D-C-T-S) framework organizes model representations into five separately addressable and schedulable dimensions, covering spatial proxies, geometry, components, textures, and semantics. A compact 16-bit structured state word is used to represent runtime states and reduce dependence on repeated text-based state parsing, supporting fixed-offset bitwise decoding, exclusive-OR (XOR)-based differencing, constraint checking, and incremental updating. A centroid-assigned Home Tile strategy is further introduced to reduce redundant semantic payloads for cross-tile objects. The method was evaluated using a single-building BIM model and an urban-scale photogrammetric mesh dataset. Under the tested initial-view setting, staged decoupled loading reduced the first-screen requested payload by 93.1% compared with monolithic loading. State-word-based C-field extraction achieved an approximately 144-fold speedup over JSON deserialization and C-field lookup. The Home Tile strategy reduced the total semantic payload by 44.1% in the semantic-redundancy test. In the 1.12 GB first-screen memory test, state-word-driven D1 tile scheduling loaded only 22.7 MB of physical payload, with stable resident memory of approximately 88.1 MB. These results indicate that the proposed method supports object-level state representation, selective resource activation and scheduling, Home Tile semantic routing, incremental updating, and first-screen memory control within tiled Web3D pipelines. Full article