Building Information Modeling Model Research Articles

Integrating Data from Terrestrial Laser Scanning and Unmanned Aerial Vehicle with LiDAR for BIM Developing

Abstract. The use of Building Information Modeling (BIM) in building construction and management is becoming increasingly common. Nevertheless, the generation of BIM models for already existing buildings is still an operation requiring a significant human effort. The generation of a geometrically reliable and complete BIM model requires geometric information on all the building parts. Since acquiring such information with a unique acquisition tool is quite hard, integration of data acquired with different acquisition tools and platforms is strongly recommended in order to obtain a geometrically complete 3D description of the building. This work presents a procedure for integrating data acquired with Terrestrial Laser Scanning (TLS), UAV (Unmanned Aerial Vehicle) LiDAR (Light Detection and Ranging) and Smartphone with LiDAR, showing the obtained results on two case studies, two buildings in the campus of the University of Warmia and Mazury in Olsztyn. Finally, a BIM model have been successfully generated in both the case studies by using the Blender software.

Terrestrial Laser Scanning (TLS) Survey and Building Information Modeling (BIM) of The Edmund Pettus Bridge: A Case Study

Abstract. The comprehensive digital documentation of the Edmund Pettus Bridge, a symbol of the American Civil Rights Movement located in Selma, Alabama, serves as an example of integrating Terrestrial Laser Scanning (TLS) and Building Information Modeling (BIM) technologies for the conservation of heritage infrastructure. This study aimed to employ advanced TLS and other Reality Capture (RC) techniques to generate a high-resolution, three-dimensional representation of the bridge, thereby aiding in its structural assessment and preservation. Utilizing TLS alongside 360-degree photography and UAV surveys, the project achieved detailed coverage of the bridge's architectural and structural features. The data collected was processed into a BIM model using Autodesk Revit, offering comprehensive analysis and preservation planning. The findings from this research highlighted previously undetected areas of material degradation and structural vulnerabilities, emphasizing the value of TLS and BIM in revealing critical insights into the condition of heritage structures. This project preserved the digital memory of an iconic Civil Rights landmark and set a framework for applying digital documentation technologies in heritage infrastructure conservation. The success of this methodology offers a valuable precedent for future conservation efforts, showcasing how RC technologies can enhance the preservation of historical sites and ensure their legacy is maintained for future generations.

Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.

Automatic creation of personalised virtual construction safety training using digital twins

The construction industry faces persistent safety challenges, with many occupational accidents and fatalities. Virtual safety training methods often fall short in terms of realism. This article introduces a pioneering approach that exploits digital twins to create Virtual Training Environments (VTEs) tailored for construction safety. The Digital Twin for Construction Safety (DTCS) framework seamlessly integrates Building Information Modelling (BIM) models, schedules, resources, and hazard data into a prototype using the game engine Unity. It enables the generation of VTEs with minimal user interaction, empowering safety trainers to define personalised scenarios based on specific project parameters and training requirements tailored to the trainees’ needs. The evaluation of this approach through three distinct real-world projects and a case study with six trainees demonstrates its efficacy. Results indicate the DTCS framework’s ability to efficiently create VTEs independently of the project once a BIM model is available. Moreover, the framework provides meaningful feedback to the trainee by evaluating the performance using run-time data collection. The collected data promises use for several applications, but future research must validate its effectiveness. Beyond training, the study highlights the utility of VTEs in improving safety design and hazard mitigation strategies through data-driven insights.

Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems

The primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts in the field with the appropriate tools. By utilizing operational data from an operational roof-mounted BAPV system and incorporating Building Information Modeling (BIM) to improve its design and smooth integration into built settings, the study offers novel insights.A three-phase research methodology is applied, encompassing data collection and performance assessment, BIM modeling, and machine learning algorithms for energy forecasting. The case study involves a 160 kWp photovoltaic system in Abruzzo, Italy, over a three-year period. The research employs different performance metrics recommended by IEC 61724 to compare experimental and theoretical data. BIM simulations are exploited as they are crucial for identifying the correct design process. Furthermore, four cutting-edge machine learning algorithms are used to forecast daily energy production. The random forest is identified as the best model for forecasting and the effect of tuning of hyperparameters on the efficiency of all models is also reported.The research adds to the larger conversation on sustainable energy management and solutions by providing researchers involved in the design and improvement of BAPV systems with a solid foundation for future developments.

Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran

The pre-tender cost estimation serves as the foundation for determining the project cost in the early stages and is crucial for all parties involved in the tendering process. It is expected to be highly accurate. However, industry surveys have advocated that in the Iranian construction industry, the pre-tender estimated costs of construction projects are not sufficiently accurate during the bidding stage. Building information modelling (BIM) technology is a modern digital tool deployed in the construction industry that has seen substantial growth of application. It employs useful tools in different sectors and has extended its involvement in various stages of the project lifecycle, including feasibility studies, planning, design, construction, and operation and maintenance. This study examines and assesses the status of BIM development in Iran for its applicability in the pre-tender cost estimating process in construction projects. The findings of this study indicate that while there is a limited number of Iranian building projects’ BIM models that possess the required quality and capabilities for BIM-based cost estimation, the results obtained are more precise when compared to existing manual methods. Additionally, utilising BIM-based cost estimation significantly reduces the time required for this process by enhancing the speed of cost estimation operations. An innovative evaluation framework for assessing the quality and functionalities of BIM models is presented, which improves the accuracy of cost estimation before the bidding process in Iran’s construction industry. The research findings demonstrate the enhanced accuracy and effectiveness of BIM as compared to conventional approaches. It also suggests the incorporation of AI technology for model evaluation, which has the potential to become an established global industry standard.

BIM to BREEAM: A workflow for automated daylighting assessment of existing buildings

By conducting an assessment of the existing building stock from the perspective of the Green Building Certification System (GBCS), it is possible to contribute to the improvement of energy utilization and reduction of greenhouse gas emissions as promoted through the Renovation Wave strategy by the European Union. These aspirations will simultaneously contribute to enhancing human well-being, indoor environmental quality, and comfort. This paper describes a novel methodology for enhancing the renovation of existing buildings at the design stage using automation within the Building Information Modeling (BIM) environment. By parameterizing the requirements of GBCS using a visual programming tool, real-time insight into the number of achieved certain issue credits in the certification process is enabled. It was also created to enable designers in the Architecture, Engineering, Construction, and Operations (AECO) sector to continuously monitor the development of design and make modifications as early as possible in the design process. Thus, cost overruns in design projects, as well as later renovations and building maintenance, are prevented. This research has utilized the capability of BIM to perform rapid compliance simulations with Building Research Establishment Environmental Assessment Method (BREEAM) requirements without the need for manual calculations and/or computer simulations in other software, which would increase the time required for analysis due to additional modeling requirements. The methodology is demonstrated using the automation of the Daylighting section of the issue Hea01 Visual Comfort from the Technical Manual SD225 1.4 − BREEAM International Non-Domestic Refurbishment 2015, and the validation was performed on a BIM model of an existing building belonging to the educational sector. The application of the described methodology in the case study reduced the time required for assessing the number of achieved daylighting credits by 40%, with potential savings extending up to 80% depending on the chosen criteria for analysis. The tool is designed to be applicable to other GBCS, building types, and newly designed buildings as well.

Developing a Blockchain-Based Framework for Digital Archiving of BIM Using Axiomatic Design

Building information modeling (BIM) has been attracting increasing attention in the architecture, engineering, and construction (AEC) industry in recent years. The wide spread of BIM marks the transition from 2D and paper-based archiving to 3D and digital archiving of project information. However, the commonly used centralized managing approaches for BIM data have high risks of data loss and data tampering. With cryptographic algorithms and distributed databases, blockchain has the potential to address the limitations of data loss and trust in conventional BIM management methods. Therefore, this paper proposes a blockchain-based framework for digital archiving of BIM data. In this paper, the axiomatic design approach is utilized to design the archiving framework in order to systematically map the design parameters with the functional requirements and minimize the information contents. The proposed framework contains five modules, including the BIM module, building plan approval (BPA) module, building data simplification (BDS) module, distributed data storage (DDS) module, and digital document verification (DDV) module. Hyperledger Fabric is utilized to develop the blockchain system based on the proposed framework. In the end, a simple BIM model with 240 components and around 11,000 lines of content in the Industry Foundation Classes (IFC) file is taken as an illustrative example to validate the proposed blockchain approach. By storing the BIM model on to the developed blockchain, it is found that each transaction of a blockchain can store over 5000 lines of IFC contents with 0.09 s of uploading latency. The results show that the proposed blockchain-based approach can effectively and securely archive BIM data.

Integrating Drone Imagery and AI for Improved Construction Site Management through Building Information Modeling

In the rapidly advancing field of construction, digital site management and Building Information Modeling (BIM) are pivotal. This study explores the integration of drone imagery into the digital construction site management process, aiming to create BIM models with enhanced object recognition capabilities. Initially, the research sought to achieve photorealistic rendering of point cloud models (PCMs) using blur/sharpen filters and generative adversarial network (GAN) models. However, these techniques did not fully meet the desired outcomes for photorealistic rendering. The research then shifted to investigating additional methods, such as fine-tuning object recognition algorithms with real-world datasets, to improve object recognition accuracy. The study’s findings present a nuanced understanding of the limitations and potential pathways for achieving photorealistic rendering in PCM, underscoring the complexity of the task and laying the groundwork for future innovations in this area. Although the study faced challenges in attaining the original goal of photorealistic rendering for object detection, it contributes valuable insights that may inform future research and technological development in digital construction site management.

A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage

Chinese cultural heritage architecture, a blend of traditional and modern construction methods, showcases the country's technological, artistic, and cultural advancements, yet its preservation faces challenges due to structural decay and aesthetic degeneration. Using Microtrans Maryland 4-1000, Dazu Rock Carvings, Nanchan Temple and Foguang Temple smokescreen were captured; surface images and ground photos were captured with smokescreen resolution of 5192 x 4153 pixels. The goal of the research is to use an Ensemble Ant Colony Fused Convolutional Capsule Neural Network (EAC-CCNN) to enhance fault analysis in Chinese cultural heritage structures images, then the combination of Augmented Reality (AR), and Building Information Modeling (BIM) to enhance the designing model for the safety management and decision making. The process entails gathering and annotating a variety of information, creating a hybrid EAC-CCNN model to investigate the architectural building's problem, training it, integrating it with BIM, doing on-site inspections, and utilizing AR-enhanced BIM models to analyze the flaws found. The findings demonstrate how this integrated method improves the precision of flaw diagnosis, fosters teamwork, and aids in the upkeep and preservation of cultural heritage assets. Metrics including accuracy and F1 score are used to evaluate the machine learning model recognizes and categorizes flaws in Chinese cultural heritage architecture. The defect identifying and safety management model of architectural designing outcome of the accuracy is 93.29% and F1 Score is 95.47%. During the training, validation, and testing phases, performance is evaluated by project goals. With the help of this method, machine learning models can be trained to identify patterns, identify flaws, and generate well-informed predictions in a variety of circumstances.

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Automated detailing of exterior walls using NADIA: Natural-language-based architectural detailing through interaction with AI

Learning building information modeling (BIM) systems has always been a challenge for BIM adoption. Although groundbreaking performances of large language models (LLMs) have inspired many researchers to consider an LLM as a potential BIM control method using natural language, a specific method of utilizing LLMs for automated BIM model detailing has not yet been proposed. This paper proposes an LLM-BIM chaining framework to enable architectural design detailing using natural language, instead of using menu-based user interfaces, named “Natural-language-based Architectural Detailing through Interaction with AI (NADIA)”. The NADIA framework is based on three main approaches: 1) separating the specification of the wall layers from the creation of the wall layers; 2) appropriate instruction prompting to guide the LLM to minimize irrational responses and produce engineering rational details; and 3) LLM-BIM chaining to seamlessly link a BIM authoring tool and an LLM. The effectiveness of NADIA was validated based on two main aspects: its accuracy in generating details that adhere to specified design requirements from users—as a design assistant—and its compliance with general engineering requirements—as a design consultant. The validation was achieved through tasks that involved generating 240 and 1,920 exterior wall details, respectively. NADIA achieved an average accuracy of 83.33% in generating logically coherent details in line with the required design conditions. For thermal performance requirements, it demonstrated a mean accuracy of 98.54% in complying with the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) 20.1–2019 standard. Despite being in its early stages, NADIA’s potential for developing and refining architectural details through natural language-based interactions between architects and machines is promising.

Integration of Augmented Reality to BIM Model

Abstract: This study investigates how to seamlessly incorporate augmented reality (AR) with Building Information Modelling (BIM) and its impact on construction efficiency. By overlaying digital information onto physical environment, AR enhances onsite decision-making and spatial understanding. The project emphasizes the practical benefits, such as improves collaboration, reduced errors, and enhanced project outcomes, positioning the AR-BIM fusion as a key driver for construction excellence.

Innovative Point Cloud Segmentation of 3D Light Steel Framing System through Synthetic BIM and Mixed Reality Data: Advancing Construction Monitoring

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.

Automated compliance checking for BIM models based on Chinese-NLP and knowledge graph: an integrative conceptual framework

PurposeThe compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.Design/methodology/approachThis study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.FindingsCase validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.Originality/valueThis study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The Development of a Framework for the Automated Translation of Sketch-Based Data into BIM Models

At the foundational phase of architectural design, it is of the utmost importance to precisely capture and articulate the visions and requirements of stakeholders, including building owners. This critical step ensures that professionals, including architects, can effectively translate the initial concepts into actionable designs. This research was directed towards developing a framework to facilitate the decision-making process by efficiently depicting the client’s intentions. This study demonstrates a framework that leverages deep learning to automate the creation of Building Information Modeling (BIM) models from sketched data. The framework’s methodology includes defining the necessary processes, system requirements, and data for system development, followed by the actual system implementation. It involves several key phases: (1) developing a process model to outline the framework’s operational procedures and data flows, (2) implementing the framework to translate sketched data into a BIM model through system and user interface development, and, finally, (3) validating the framework’s ability to precisely convert sketched data into BIM models. Our findings demonstrate the framework’s capacity to automatically interpret sketched lines as architectural components, thereby accurately creating BIM models. In the present study, the methodology and framework proposed enable clients to represent their understanding of spatial configuration through Building Information Modeling (BIM) models. This approach is anticipated to enhance the efficiency of communication with professionals such as architects.

Streamlining FE and BIM Modeling for Historic Buildings with Point Cloud Transformation

ABSTRACT In this paper, a convenient workflow to obtain a simplified lightweight IFC description of a dense point cloud describing a historic building is proposed. The proposed procedure relies on slicing of the point cloud that aims at reconstructing the geometry of the solid. The resulting slices can then be easily employed in the generation of finite element (FE) and architectural building information modeling (BIM) models. In particular, the FE model generation procedure guarantees the obtainment of a conforming solid FE mesh ready to be used for structural purposes. Contextually, the architectural BIM model generation is achieved by slices extrusion to obtain the overall volume of the building. Then, the assembly of independent slice-based meshes is subjected to re-topology to obtain the overall watertight bounding surface of the building. A real historic structure is here used to show the effectiveness of the FE and BIM model generation procedures. Finally, the BIM model generation of an actual historic structure is carried out through the proposed workflow.

Automated BIM generation for large-scale indoor complex environments based on deep learning

Large volumes of 3D parametric datasets, such as building information modeling (BIM), are the foundation for developing and applying smart city and digital twin technologies. Those datasets are also considered valuable tools for efficiently managing rebuilt structures during the operation and maintenance stages. Nevertheless, current approaches developed for the scan-to-BIM process rely on manual or semi-automatic procedures and insufficiently leverage semantic data in point clouds. These methods struggle to accurately represent large-scale indoor complex layouts and extract details from irregular-shaped unstructured elements, causing inefficiencies in BIM model generation. To address these issues, we propose an innovative scan-to-BIM framework based on deep learning algorithms and raw point cloud data, enabling the automatic generation of 3D models for both structured and unstructured indoor elements. Initially, we propose an enhanced deep learning neural network to improve the point clouds' semantic segmentation accuracy. Subsequently, an efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. The proposed workflow can reconstruct large-scale data with multiple room layouts of Manhattan or non-Manhattan structures and reconstruct 3D models automatically by using a BIM parametric algorithm implemented in Revit software. Moreover, we introduce a robust method for unstructured elements to automatically generate corresponding 3D BIM models, even when the incorporating semantic information is incomplete. The proposed approach was evaluated on synthetic and real data for different scales and complexities of indoor scenes. The results of the experiments demonstrate that the improved model significantly enhances the overall semantic segmentation accuracy compared to the baseline models. The proposed scan-to-BIM framework is efficient for indoor element 3D reconstruction, achieving precision, recall, and F-score values ranging from 96% to 99%. The generated BIM models are competitive with traditional methods regarding model completeness and geometric accuracy.

Automated Generation and Internal Force Visualization for Box Culvert Based on Building Information Modeling

Box culverts, as a commonly employed structural form for culverts, play a critical role in traversing topographic barriers, ensuring the safety and smooth operation of transportation means such as roads and railways. However, traditional design methodologies are often time-consuming and prone to inaccuracies, failing to achieve the efficiency and precision required by modern engineering construction. To address these challenges, using the Revit 2021 and Midas Civil 2021 software platforms, we developed a Building Information Modeling (BIM) parametric modeling method for box culverts using Dynamo’s visual programming capabilities. This method enables the rapid and accurate automated generation of box culvert BIM models. Furthermore, this study proposes an effective strategy for conversion between box culvert BIM models and Midas Civil finite element models, as well as internal force visualization within a BIM project. A case study involving a box culvert underpass beneath an expressway in an urban setting was modeled parametrically and structurally validated, demonstrating that the approach not only significantly enhances modeling efficiency but also strengthens computational capabilities through bidirectional data exchange between BIM and Finite Element Analysis (FEA) software. This research has effectively advanced the application and practical implementation of BIM technology in box culvert engineering.

Integration of BIM data and real-time game engine applications: Case studies in construction safety management

Building Information Modeling (BIM) has unique features that improve safety management in construction by visually identifying potential risks. Integrating BIM with a real-time game engine is a cutting-edge idea for more effective safety management. This study aims to conduct two case studies by integrating BIM data with game engines from two aspects: 1) Construction Safety Training and 2) Pre-construction Safety Management. A framework that covers techniques for extraction of safety ideas, managing the game engine, and character modeling tools and resources is used to carry out the case studies. In the first case study, a construction site was created by Revit, and a real-life scaffolding failure accident was simulated by Unity to warn workers to prevent similar future events. The second case study was conducted on the procedure of evacuation modeling in an emergency, integrating a BIM model and Unity following distinct pathways. This evacuation modeling can be used as a training platform for the occupants to acquaint themselves with the inside facility, show directions of the shortest evacuation path from specific points, and provide necessary information on emergency equipment. Finally, the study explains how the integration of the BIM model and game engine applications can be applied for effective, straightforward, and helpful safety management with the most efficient BIM data transition.