As-built Building Information Modeling Research Articles

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

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.

A framework of attributes for as-built BIM models: A systematic review

Accurate digital twins are crucial in improving facility management. However, facility owners often encounter inaccurate digital twins due to incorrect as-built Building Information Modeling (BIM) models. By understanding the attributes of correct as-built BIM models, it is possible to reduce the production of inaccurate digital twins. This study aims to establish a framework of attributes for developing as-built BIM models. To achieve this, a systematic review of published articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique was conducted to identify relevant attributes for developing as-built BIM models. Next, a comprehensive conceptual framework was established for the development of as-built BIM models. Through the analysis of 50 articles, nine attributes of as-built BIM models were identified and grouped into three categories: data quality, data interoperability, and data security. Data quality encompasses accuracy, completeness, ease of understanding, coordination, consistency, and up-to-date. Data interoperability includes accessibility and compatibility, and data security pertains to security. The study findings could provide valuable guidance to industry practitioners and policymakers in developing strategies for producing correct as-built BIM models, ultimately improving the production of accurate digital twins and enhancing overall facility management.

Digital reconstruction of substation equipment and facility layout via LiDAR point cloud registration

Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations.

Online as-Built Building Information Model Update for Robotic Monitoring in Construction Sites

Today, automated techniques for the update of as-built Building Information Models (BIM) make use of offline algorithms restricting the update frequency to an extent where continuous monitoring becomes nearly impossible. To address this problem, we propose a new method for robotic monitoring that updates an as-built BIM in real-time by solving a Simultaneous Localization and Mapping (SLAM) problem where the map is represented as a collection of elements from the as-planned BIM. The suggested approach is based on the Rao-Blackwellized Particle Filter (RBPF) which enables explicit injection of prior knowledge from the building’s construction schedule, i.e., from a 4D BIM, or its elements’ spatial relations. In the methods section we describe the benefits of using an exact inverse sensor model that provides a measure for the existence probability of elements while considering the entire probabilistic existence belief map. We continue by outlining robustification techniques that include both geometrical and temporal dimensions and present how we account for common pose and shape mistakes in constructed elements. Additionally, we show that our method reduces to the standard Monte Carlo Localization (MCL) in known areas. We conclude by presenting simulation results of the proposed method and comparing it to adjacent alternatives.

Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes

Mechanical, electrical, and plumbing (MEP) systems play a crucial role in providing various services and creating comfortable environments for urban residents. In order to enhance the management efficiency of these highly complex MEP systems, as-built building information models (BIMs) are being increasingly adopted worldwide. As-built BIMs accurately represent the actual conditions of facilities, making as-built BIM reconstruction significantly important for construction progress tracking, quality assurance, subsequent facility management, and renewal. To create as-built BIMs for MEP systems, laser scanners are widely utilized to capture high-resolution images and dense 3D measurements of the environment in a fast and highly accurate manner. Despite research efforts to automatically achieve “Scan-to-BIM,” there are still gaps in applying current solutions to real-world scenarios. One of the major challenges are the limited generalization of existing methods to unseen scenarios without proper training or fine-tuning on custom-designed datasets. To address this issue, this study introduces Omni-Scan2BIM, a novel approach powered by large-scale pre-trained vision foundation models. Omni-Scan2BIM enables the recognition of MEP-related components with a single shot by integrating an all-purpose feature extraction model and a class-agnostic segmentation model. Firstly, given only a single image with a reference mask, the visual features are extracted for both the target component and the collected on-site images using the vision foundation model DINOv2. Secondly, through comparing pixel features, similarity maps are generated for the on-site images. The prior points for the class-agnostic segmentation model are sampled from the local maxima of the similarity map. Thirdly, Segment Anything Model (SAM) is leveraged to sequentially segment the target component. Finally, the target component is segmented out in 3D space based on the transformation matrix describing the spatial relationship between the 2D images and the 3D point clouds. Shape analysis and label fusion are conducted for as-built BIM modeling purpose. To validate the feasibility of the proposed technique, experiments were conducted using data collected from a real construction site in Hong Kong. The results demonstrate that the proposed Omni-Scan2BIM approach can easily generalize to unseen components with significantly improved accuracy and efficiency.

From Design to Management: Exploring BIM’s Role across Project Lifecycles, Dimensions, Data, and Uses, with Emphasis on Facility Management

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.

BIMBot for Autonomous Laser Scanning in Built Environments

Accurate and complete 3D point clouds are essential in creating as-built building information modeling (BIM) models, although there are challenges in automating the process for 3D point cloud creation in complex environments. In this paper, an autonomous scanning system named BIMBot is introduced, which integrates advanced light detection and ranging (LiDAR) technology with robotics to create 3D point clouds. Using our specially developed algorithmic pipeline for point cloud processing, iterative registration refinement, and next best view (NBV) calculation, this system facilitates an efficient, accurate, and fully autonomous scanning process. The BIMBot’s performance was validated using a case study in a campus laboratory, featuring complex structural and mechanical, electrical, and plumbing (MEP) elements. The experimental results showed that the autonomous scanning system produced 3D point cloud mappings in fewer scans than the manual method while maintaining comparable detail and accuracy, demonstrating its potential for wider application in complex built environments.

Generating and Evaluating City Building Facades Using Artificial Intelligence

The present study examines the various methods involved in the generation of city building facades. Elements of the entire process are explored – from obtaining the necessary information to using it to create a harmonious urban environment. The methods considered – the recognition and generation of facade elements, the generation of as-built Building Information Model (BIM) and Digital Twins of buildings (DT) – are evaluated against the needs of the generation, as well as what their place is in the foreseeable future development of architecture in the metaverse. The purpose of the study is to present the best practices in the field depending on the needs of the model and to show their practical application. An example is given of using drone imagery to assess the predominant coloration of facades with a view to generating examples harmonious with surrounding buildings. The example uses the scripting language python and different libraries to evaluate the image information and presents the results as a figure displaying the three predominant colors of the given image.

A parametric approach towards semi-automated 3D as-built modeling

Building Information Modeling (BIM) has been developed in response to the growing complexity of construction projects. BIM implementation is beneficial throughout the entire building life cycle, and thus has been widely adopted in new projects. However, BIM implementation in existing buildings is impeded by the lack of as-built models. Conventionally, three-dimensional (3D) as-built BIMs are generated by experienced modelers, which is time-consuming and error-prone. To cater to the need, Scan-to-BIM is a solution for automation in as-built BIM generation. In the context of automated Scan-to-BIM, the parametric modeling process is worth investigating as it has the ability to not only reconstruct 3D objects from a variety of categories from point clouds but also offer flexibility to update objects simply by changing the values of the correlative parameters. Hence, this study proposes a semi-automated framework for assisting 3D as-built modeling through a parametric modeling approach. The presented methodology starts with wall boundary parameter extraction through 3D to 2D projection of the wall segments and line detection techniques, followed by retrieving geometric parameters of all other non-wall elements via CloudCompare. The extracted parameters are structured into a Microsoft® Excel® file and fed into Autodesk® Dynamo for 3D BIM creation using a series of designed logic. To substantiate the viability, the proposed framework is employed in two datasets containing structural, architectural, furniture, mechanical, and plumbing objects (further categorized into structural, hosted and non-hosted elements). The Intersection over Union (IoU) of structural elements was 96.35%, while the root-mean-square error (RMSE) of hosted and non-hosted elements was 3.634 and 2.607 mm, respectively. This study established a universal methodology for semi-automated 3D as-built modeling that can guide future research.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

Activity-level construction progress monitoring through semantic segmentation of 3D-informed orthographic images

Effective progress monitoring is crucial for successful construction project delivery. Visual data, comprising images and videos of construction operations, has emerged as a valuable source for gaining comprehensive insights into project status. While various vision-based methods have been developed for automated progress monitoring at the element level (e.g., columns, beams, floors, walls), challenges persist in achieving accurate schedule activity-level (e.g., formwork, reinforcement, concrete placement) progress tracking. Existing methods often struggle to report progress status beyond binary form (built/not built). To address these limitations, this research proposes a novel framework for precisely measuring the percent completion of activities associated with under-construction building elements. The framework is implemented through an Activity-level progress monitoring system (ALPMS). ALPMS takes the input of construction site images and a four-dimensional building information model (BIM) and outputs the activity-wise completion percentage required for updating project schedules. It creates as-built point clouds from images, compares them with the as-planned BIM, generates orthographic views of under-construction elements through projective transformation or neural radiance fields (NeRF), applies deep learning-based semantic segmentation for progress reasoning, and estimates completion percentages. Finally, the activity-level rich semantic information is transferred to the as-built point cloud and BIM for three-dimensional visualization of the progress status. When the framework was applied to two building construction projects, on average, the ALPMS could report the activity-wise completion percentages with <6% mean absolute error. Future research shall focus on automating the mapping of project schedule activities and activity-level progress details for timely schedule updates and prediction of project completion dates.

As-built BIM reconstruction of piping systems using smartphone videogrammetry and terrestrial laser scanning

The increasing adoption of Building Information Modeling (BIM) in the construction industry has created a demand for methods to update existing facilities with accurate as-built BIM models. While terrestrial laser scanners (TLS) are commonly used for indoor surveys due to their speed and precision, they face limitations in areas with physical constraints like confined spaces and occlusions, with pipes being particularly affected. This paper presents a smartphone videogrammetry approach designed to enhance the accuracy and completeness of terrestrial laser scanner (TLS) point clouds for as-built BIM reconstruction. The method involves capturing missing areas within the TLS point cloud with smartphones and then seamlessly integrating these data into a more comprehensive multi-modal point cloud. This approach is validated using real scan data and offers convenience through the use of readily available and cost-effective smartphones, while its iterative nature facilitates progressive BIM reconstruction of pipe structures. Additionally, a compatible progressive BIM reconstruction framework is outlined, aiming to modernize scan-to-BIM practices in the construction industry, with a case study demonstrating its feasibility.

Knowledge-driven inference for automatic reconstruction of indoor detailed as-built BIMs from laser scanning data

In spite of recent advances, a significant disparity between automatically reconstructed building information models (BIMs) and real scenes persists, particularly within complex indoor environments. With this objective in mind, a knowledge-driven as-built BIM reconstruction method is proposed, which employs laser scanning point clouds and panorama images. Initiation of the method involves the segmentation of 3D data into individual rooms through the application of wall constraints. Subsequently, geometric regularization of the rooms is performed, accompanied by the establishment of topological relations through the maintenance of rigid consistency among rooms. Finally, building frames and components embedded within walls are reconstructed and assembled to formulate comprehensive BIMs. The method was evaluated using indoor point cloud datasets (ISPRS and WUT), showing that it outperforms state-of-the-art techniques with room segmentation accuracy and completeness of 0.98 and 0.88, respectively, and achieving millimetre level accuracy on average.

Skeleton-guided generation of synthetic noisy point clouds from as-built BIM to improve indoor scene understanding

The limited amount of high-quality training data available in indoor understanding with deep learning is a major problem. A possible solution to this problem is to use synthetic data to improve network training. In this study, a fully automatic method to generate synthetic noisy point clouds from as-built building information modeling (BIM) models is presented and it assesses the potential of these synthetic point clouds to improve deep neural network training. Based on a skeleton-guided strategy, all hypothetical scanning sites are located along the central axis of the buildings, which are obtained through equidistant sampling. Then, the synthetic labeled point cloud is generated station-by-station, and data augmentation is achieved using a random combination of data from different stations. The proposed approach involves generating over 44 sets of synthetic noisy point clouds based on BIM models. The performance of state-of-the-art (SOTA) deep learning methods in understanding indoor scenes enhanced by the synthetic point clouds is thoroughly assessed, and the effectiveness of various combinations of real and synthetic datasets is investigated. The experimental results demonstrate that leveraging synthetic point clouds generated from BIM models leads to a remarkable 5%–10% improvement in 3D semantic segmentation accuracy. The research signifies the value of synthetic point clouds as an effective tool for improving deep neural network training. All simulation datasets are publicly available, including original BIM models, full synthetic point clouds, and point clouds after IHPR processing, accessible via the BIMSyn Dataset link. In future research, an exploration of how synthetic point clouds will be further improved by considering specific characteristics of objects such as color, material reflectance, and illumination.

Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach

Point cloud data are used to create an as-built building information model (as-built BIM) that reflects the actual status of any building, whether being constructed or already completed. However, indoor clutter objects in the point cloud data, such as people, tools, and materials, should be effectively eliminated to create the as-built BIM. In this study, the authors proposed a novel method to automatically remove indoor clutter objects based on the Manhattan World assumption and object characteristics. Our method adopts a two-dimensional (2D) projection of a 3D point cloud approach and utilizes different properties of indoor clutter objects and structural elements in the point cloud. Voxel-grid downsampling, density-based spatial clustering (DBSCAN), the statistical outlier removal (SOR) filter, and the unsupervised radius-based nearest neighbor search algorithm were applied to our method. Based on the evaluation of our proposed method using six actual scan datasets, we found that our method achieved a higher mean accuracy (0.94), precision (0.97), recall (0.90), and F1 core (0.93) than the commercial point cloud processing software. Our method shows better results than commercial point cloud processing software in classifying and removing indoor clutter objects in complex indoor environments acquired from construction sites. As a result, assumptions about the different properties of indoor clutter objects and structural elements are being used to identify indoor clutter objects. Additionally, it is confirmed that the parameters used in the proposed method could be determined by the voxel size once it is decided during the downsampling process.

Integrating as-built BIM model from point cloud data in construction projects

PurposeAs laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.Design/methodology/approachASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.FindingsA case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).Originality/valueFirst, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

Labelled Indoor Point Cloud Dataset for BIM Related Applications

BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps.

Semi-Automated BIM Reconstruction of Full-Scale Space Frames with Spherical and Cylindrical Components Based on Terrestrial Laser Scanning

As-built building information modeling (BIM) model has gained more attention due to its increasing applications in construction, operation, and maintenance. Although methods for generating the as-built BIM model from laser scanning data have been proposed, few studies were focused on full-scale structures. To address this issue, this study proposes a semi-automated and effective approach to generate the as-built BIM model for a full-scale space frame structure with terrestrial laser scanning data, including the large-scale point cloud data (PCD) registration, large-scale PCD segmentation, and geometric parameters estimation. In particular, an effective coarse-to-fine data registration method was developed based on sphere targets and the oriented bounding box. Then, a novel method for extracting the sphere targets from full-scale structures was proposed based on the voxelization algorithm and random sample consensus (RANSAC) algorithm. Next, an efficient method for extracting cylindrical components was presented based on the detected sphere targets. The proposed approach is shown to be effective and reliable through the application of actual space frame structures.

FEASIBILITY AND ACCURACY OF AS-BUILT MODELLING FROM SLAM-BASED POINT CLOUDS: PRELIMINARY RESULTS

Abstract. Nowadays, portable Mobile Mapping Systems (MMSs) and robotic mapping platforms leveraging on Simultaneous Localization and Mapping (SLAM) methods are gaining increasing attention for architectural and construction surveying, representing an efficient solution for geometric data acquisition for scan-to-BIM purposes. However, the applicability of standard modelling workflows and the accuracy of Building Information Models (BIM) that can be obtained from SLAM-based point clouds is still an open question. In this paper, we propose a preliminary evaluation on the feasibility of extracting as-built BIM from (i) a point cloud acquired with a commercial portable MMS, and (ii) a point cloud obtained through an open-source SLAM algorithm, surveying the environment with an autonomous mobile robotic platform. In both cases, the main structural elements of the test site are accurately generated, thus showing promising results. On the other hand, the experiment highlights also the need for SLAM systems capable of providing less noisy point clouds, in order to capture and model architectural details.

Dynamic Progress Monitoring of Masonry Construction through Mobile SLAM Mapping and As-Built Modeling

Traditional progress monitoring can be inaccurate and time-consuming, potentially causing time delay and cost overrun in construction projects. With development in technology, tools such as cameras, laser scanners, and building information modelling (BIM) have been used to overcome existing problems in the traditional approach. However, noise mitigation, extracting objects of interest from laser point clouds, and detailed progress measurement are problems that still exist. In this study a novel method of construction progress monitoring to measure the progress percentage is presented. The study integrates the simultaneous localization and mapping (SLAM) technique with as-built BIM to gather quick and accurate construction site progress information. The Hausdorff distance is utilized to extract objects of interest and filter out noise from site-scan data. As-built and as-planned BIM models are compared using Python and Dynamo, to obtain progress percentage. A case study was conducted on a residential building located in Sydney, Australia, to validate the application of the developed method. The outcome demonstrates that utilizing the SLAM technique and Hausdorff distance are effective in mitigating noise and extracting objects of interest from site-scan data, respectively. In addition, with an accuracy of 94.67 percent in estimation, the progress percentage was obtained based on material quantities. The obtained progress percentage could also be used in updating construction schedules and assisting decision-making.