Search Results (67)

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

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article

Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article

The present study focuses on the preservation of historic timber constructions, crucial cultural heritage assets that demand effective structural health monitoring (SHM) to ensure safety and integrity. SHM aims to detect and evaluate potential structural deviations that may compromise performance, requiring both detailed geometric data acquisition and 3D modeling. For this purpose, contactless tools such as photogrammetry, laser scanning, and other topographic methods are employed to gather point cloud data. This research utilizes a terrestrial laser scanner (TLS) to generate 3D models of the historic timber tower of St. Michaeler church in Vienna. A novel automated modeling method is compared with two manual modeling approaches. The first is a traditional as-designed structural model created in Dlubal RSTAB software, and the second is a manually generated as-built model created using a scan-to-BIM application in Revit. While the first model is based on 2D plan documents created from the TLS point cloud, the second and automated models use the point cloud as direct input. The findings demonstrate that this automated model significantly enhances early-stage structural assessment efficiency, providing reliable insights into structural conditions with minimal processing time. This research underscores the potential of automated 3D modeling in preliminary structural assessments of historic timber structures. Full article

This paper presents a deep learning-based Scan-vs-BIM methodology for evaluating structural integrity through the extraction of features from As-Built scan and As-Planned Building Information Modeling (BIM) comparison data. Traditional Scan-vs-BIM frameworks often rely on Scan-to-BIM processes to generate point cloud-based mesh models for comparison, which significantly impairs computational efficiency. In contrast, the proposed streamlined Scan-vs-BIM framework incorporates a deep neural network (DNN) model consisting of two neural networks: one for structural integrity assessment and another for error type analysis. The model evaluates the structural integrity of individual components in a sequential manner, repeating the process across all elements to comprehensively assess the entire structure. Rather than converting point cloud data into mesh models for comparison, this approach directly measures the spatial discrepancies between the As-Built point cloud and As-Planned BIM, analyzing the distribution tendencies of these distance values. Experimental validation on actual steel structures demonstrated that the proposed method effectively predicts structural integrity, providing significant improvements in both accuracy and computational performance. Full article

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems. Full article

In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been applied to the hydraulic work of the “Sostegno del Battiferro” in Bologna, Italy, an example of built industrial heritage whose construction began in 1439 and remains in active use nowadays to control the Navile canal water flow rate. The initial step was the definition of a 3D topographic frame, including geodetic measurements, which served as a reference for the complete 3D survey integrating Terrestrial Laser Scanning (TLS), Structured Light Projection scanning, and the photogrammetric processing of Unmanned Aircraft System (UAS) imagery through a Structure from Motion (SfM) approach. The resulting 3D point cloud has supported as-built parametric modelling (Scan-to-BIM) with the consequent extraction of plans and sections. Finally, the Heritage/Historic Building Information Modelling (HBIM) model generated was rendered and tested for a VR-based immersive experience. Building Information Modelling (BIM) and virtual reality (VR) applications were tested as a support for the management of the building, the maintenance of the hydraulic system, and the training of qualified technicians. In addition, considering the historical value of the surveyed building, the methodology was also applied for dissemination purposes. Full article

With the rapid advancement in Building Information Modelling (BIM) technology to strengthen the Building and Construction (B&C) industry, effective methods are required for the analysis and improvement of as-built BIM, which reflects the completed building project and captures all deviations and updates from the initial design. However, most existing studies are focused on as-designed BIM, while the analysis and inspection of as-built BIM rely on labour-intensive visual and manual approaches that overlook interdependent relationships among components. To address these issues, we propose a network analysis-based approach for managing and improving as-built BIM. Networks are generated from geometric attributes extracted from Industry Foundation Classes (IFC) documents, and network analytical techniques are applied to facilitate BIM analysis. In addition, a practical dataset is utilised to verify the feasibility of the proposed approach. The results demonstrate that our method significantly enhances the analysis and comparison of as-built BIM from model analysis and matching. Specifically, the innovative contribution leverages global information and interdependent relations, providing a more comprehensive understanding of the as-built BIM for effective management and optimisation. Our findings suggest that network analysis can serve as a powerful tool for structure and asset management in the B&C industry, offering new perspectives and methodologies for as-built BIM analysis and comparison. Finally, detailed discussion and future suggestions are presented. Full article

Building Information Modeling (BIM) has recently been widely applied in the Architecture, Engineering, and Construction Industry (AEC). BIM graphical information can provide a more intuitive display of the building and its contents. However, during the Operation and Maintenance (O&M) stage of the building lifecycle, changes may occur in the building’s contents and cause inaccuracies in the BIM model, which could lead to inappropriate decisions. This study aims to address this issue by proposing a novel approach to creating 3D point clouds for updating as-built BIM models. The proposed approach is based on Pedestrian Dead Reckoning (PDR) for an Inertial Measurement Unit (IMU) integrated with a Mobile Laser Scanner (MLS) to create room-based 3D point clouds. Unlike conventional methods previously undertaken where a Terrestrial Laser Scanner (TLS) is used, the proposed approach utilizes low-cost MLS in combination with IMU to replace the TLS for indoor scanning. The approach eliminates the process of selecting scanning points and leveling of the TLS, enabling a more efficient and cost-effective creation of the point clouds. Scanning of three buildings with varying sizes and shapes was conducted. The results indicated that the proposed approach created room-based 3D point clouds with centimeter-level accuracy; it also proved to be more efficient than the TLS in updating the BIM models. Full article

The integration of laser scanning technology and Building Information Modelling (BIM) processes offers a transformative approach to managing the complexities in live construction projects. This paper aims to explore the significant impacts of incorporating laser scanning and BIM on construction projects in terms of as-built models, information management, and overall project performance utilising case study analysis of a building that was not BIM-based. The research scope is defined by the need to investigate the integration of laser scanning and BIM in live construction projects. It details the data acquisition process, challenges encountered due to site obstructions, and the methodologies employed for spatial modelling procedures. Key findings reveal that such integration can significantly enhance the accuracy of data collection and improve project outcomes. Results also identify the need for specialised equipment and skills for the effective implementation of such integrations. The research concludes by offering a practical approach to enhancing construction processes, from design to maintenance. This paper contributes to the body of knowledge by providing a detailed analysis of the practical application of laser scanning and BIM in a live construction project, offering insights into the benefits, challenges, and future directions for integrating these technologies in the construction industry. Full article

In recent years, mixed reality (MR) technology has gained popularity in construction management due to its real-time visualisation capability to facilitate on-site decision-making tasks. The semantic segmentation of building components provides an attractive solution towards digital construction monitoring, reducing workloads through automation techniques. Nevertheless, data shortages remain an issue in maximizing the performance potential of deep learning segmentation methods. The primary aim of this study is to address this issue through synthetic data generation using Building Information Modelling (BIM) models. This study presents a point-cloud-based deep learning segmentation approach to a 3D light steel framing (LSF) system through synthetic BIM models and as-built data captured using MR headsets. A standardisation workflow between BIM and MR models was introduced to enable seamless data exchange across both domains. A total of five different experiments were set up to identify the benefits of synthetic BIM data in supplementing actual as-built data for model training. The results showed that the average testing accuracy using solely as-built data stood at 82.88%. Meanwhile, the introduction of synthetic BIM data into the training dataset led to an improved testing accuracy of 86.15%. A hybrid dataset also enabled the model to segment both the BIM and as-built data captured using an MR headset at an average accuracy of 79.55%. These findings indicate that synthetic BIM data have the potential to supplement actual data, reducing the costs associated with data acquisition. In addition, this study demonstrates that deep learning has the potential to automate construction monitoring tasks, aiding in the digitization of the construction industry. Full article

Achieving efficiency success status inside an organization’s built environment and obtaining a positive return on investments need robust and comprehensive asset management and maintenance processes based on the efficiency of contract information documents within the built asset lifecycle. This paper aims to highlight the appropriate interactive approach for construction projects to build the information flow scope of asset facility management contracts based on GIS (Geographical Information System) and BIM (Building Information Modeling) integration processes and sustainability standards, and project as-built contractual documents to support owners and stakeholders with the intent of improving asset management processes. Expert interviews and contract information flow types in several facility management processes conducted in both local and international facility management organizations were used to assist the information flow scope method. The study classified and built significant integrated information and data flow models for a case study to serve as contract guidelines, including efficiency performance measures and indicators for monitoring procedures, technical evaluation, and financial issues in order to provide high-performance service quality in facility management applications. Full article

This research addresses a significant challenge in the construction industry: the traditional reliance on CAD drawings and manual methods for building inspection and monitoring. This article presents a transformative proposal utilizing Augmented Reality (AR) to revolutionize these processes. The proposed model is based on an innovative integration of AR technology with Building Information Modeling (BIM), aiming to enhance data life-cycle management and improve the efficiency of construction management practices. The main goal of the article is to demonstrate the practical feasibility and benefits of this AR-BIM integration in construction management, particularly in the realms of inspection and monitoring. This involves developing and testing an accessible, user-friendly, and affordable AR application prototype for mobile devices, employing multiple markers, as a potential replacement for traditional methods. The research methodology comprises five phases, starting with the conversion of 2D CAD drawings into 3D BIM, followed by the simulation of AR-BIM integration at a construction site using a commercial AR application. This is succeeded by assessing the applicability of the commercial AR app and developing a multiple-marker AR application prototype suitable for general platforms. The final phase encompasses the testing and evaluation of this prototype. The findings suggest that the integration of 3D BIM with AR technology is not only feasible, but also beneficial in replacing paper-based processes, thereby enhancing information sharing, communication, and overall project execution efficiency. However, the accuracy of the superimposition between virtual and actual objects needs improvement to reduce discrepancies between as-built and as-planned scenarios. These results hold significant potential for transforming construction project execution by replacing traditional methods with more efficient digital solutions. Future research directions include extensive testing in various construction scenarios, improving model complexity handling, exploring the application of machine learning algorithms for data analysis, and expanding the study to encompass other stages of the construction lifecycle. Full article

The importance of Building Information Modelling (BIM) in construction and facility operation is unquestionable, but there is a clear discrepancy between the data included in as-built BIM models and the expected use specified by customers. This disparity presents significant obstacles in properly using BIM for facility management and operational operations. The main goal of this research is to suggest inventive and pragmatic approaches that successfully address the discrepancy between the actual BIM model data, with a specific emphasis on COBie dataset, and the intended BIM applications outlined by stakeholders in the Employer’s Information Requirement (EIR) for facility management and operation. The study methodology is based on a comprehensive examination of current literature, demo case studies, as well as standards pertaining to BIM data, COBie.Type, and EIR requirements. The results of this study consist of a collection of standards, procedures, and suggested practices specifically designed to improve the utilization of as-built BIM model data for facility management and operation. These will closely correlate with the BIM applications stated by the client. Furthermore, the project seeks to enhance industry norms and practices, promoting enhanced cooperation and information sharing among stakeholders. This research has also investigated the efficiency of Solibri Model Checker (SMC) to validate the COBie type and component information provided by COBie. Full article