3D Building Information Modeling Research Articles

Data models in ship design and construction – insights from 4D BIM

The lack of a cohesive understanding of a ship product data model, from design to operations, is currently a limiting factor in realizing more efficient ship lifecycle management and design processes. The paper sheds light on the history and gaps in realizing an integrated, interoperable, and multi-domain ship product data model. It also explores practices from BIM (Building Information Modelling) as an inspiration for solutions to overcome challenges related to information modeling, integrated design environment, and 4D engineering and planning.

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.

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

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.

Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

Overview of the level of 4D BIM integration in the construction industry in Slovakia

AbstractBuilding information modeling is one of the trends in the modern construction industry. The use of 4D building information modeling in addition to 3D building information modeling improves project planning and leads to higher quality of the final product with minimal wastage of resources. In addition, 4D building information modeling allows creating visual effects that can be shown to clients before construction begins. The study describes the ways in which 4D building information modeling can be used, as well as some of the notable benefits. The level of awareness of this technology and the level of its use in construction practice are also described. The final part describes the main problems and tasks of this field and possible ways to solve them.

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.

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.

Integration of Augmented Reality and Building Information Modeling for Enhanced Construction Inspection—A Case Study

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.

Minimizing Cost Overrun in Rail Projects through 5D-BIM: A Conceptual Governance Framework

Integration of 5D Building Information Modeling (BIM) into large rail projects has the potential to significantly enhance cost management and control. Nevertheless, 5D-BIM implementation has encountered difficulties stemming from technical, functional, and governance-related factors. This paper builds a conceptual framework to support financial decision making, enhances project management, and promotes efficient project delivery. The framework encompasses a set of interrelated elements that include project governance, BIM policies and standards, digital platforms, BIM LOD, cost-estimation classification, and continuous improvement. The proposed framework acknowledges the significance of project governance in guiding and organizing the implementation of 5D-BIM. Additionally, BIM policies and standards ensure the adherence to quality standards for the produced BIM models. Digital platforms serve as the basis for multiple users to generate, access, share, and exchange project information. BIM LOD promotes collaboration and coordination among all project stakeholders. Cost-estimation classification aligns the estimation process with the development of project scope and financial decision making. Continuous improvement plays a vital role in optimizing processes, enhancing efficiency, and achieving higher-quality outcomes. Moreover, it fosters stakeholder satisfaction, improves project performance, and nurtures a conducive environment for innovation and learning. The study analyzes the framework utilization in Victorian rail projects and identifies key implementation challenges. The main technical hurdles encountered were the lack of current horizontal infrastructure standards for data exchange and the lack of compatibility with current cost-management standards. Increased project complexity and the absence of clear project governance strategies and processes also posed organizational challenges. A further validation of the framework in real-world rail projects was recommended to achieve the implementation goals.

BIM-based multi-objective optimization framework for volumetric analysis of building projects

Purpose The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings Design/methodology/approach The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project. Findings The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%. Practical implications The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project. Originality/value The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

Penerapan Building Information Modeling (BIM) untuk Pengestimasian Biaya Proyek Pembangunan Pos Jaga di SD dan SMP Terpadu Balikpapan Selatan

With the development of the AEC (Architecture, Engineering and Construction) industry, currently a lot of software has been developed to meet the needs of the construction industry with the aim of minimizing human error due to conventional data processing. This study aims to analyze the results of volume and cost comparisons using the 5D Building Information Modeling (BIM) concept with conventional methods in the New School Unit construction project for integrated Elemntary School and Junior High School development in the South Balikpapan sub-district. By using Revit, the 2D image obtained from the consultant is modeled back into 3D form so that the material volume requirements become more detailed. It can be seen from the consultant's calculation of 961.46kg from the Autodeks Revit 621.34kg software that there is a difference of 340.12kg or 35%, for the volume of concrete work from The consultant's calculation has a volume of 17.65m3 from the Autodeks Revit software which has a volume of 12.01m3, a difference of 5.64m3 or 32%. For concrete work costs from consultants amounting to Rp14,787,292.87 from Autodeks Revit software Rp9,549,377.76, which has a difference of Rp5,237,915.11 or 35%, for concrete work costs from consultants amounting to Rp19,373,381 from Autodeks Revit software, the cost is Rp13,078,977, which has a difference of Rp6,294,404.07 or 32%. This illustrates that using the 3D BIM concept supported by Revit software is able to provide detailed material take-off results so as to reduce wasted material and support the 5D BIM concept in supporting cost estimation calculations.

4D BIM integrated optimization of construction steel bar procurement plan for limited storage capacity

Purpose Due to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes challenging. Lack of storage space in crowded places prompts the need for building steel bar storage choice optimization. Therefore, this study aims to optimize the construction steel bar procurement plan by providing when and how much rebar to order and how to stack different sizes of rebar considering limited storage capacity. Design/methodology/approach A novel approach has been presented in this paper by integrating 4D building information modelling (BIM) and mixed-integer linear programming (MILP). This technique uses BIM to retrieve material quantities, including rebar, during the design phase. Following that, activities are scheduled depending on the duration determined by crew productivity data and material quantity. Then, based on the prior price, the price of each unit of rebar is projected for the duration of construction using the exponential smoothing method. After that, the MILP approach is used to generate an optimal steel bar procurement plan for limited storage space following the scheduled rebar-related operations. Findings The developed strategy minimizes overall procurement costs and ensures the storage of rebar as per standard guidelines. An optimal rebar procurement and storage plan to construct a six-storied RC frame has been presented in this paper as a demonstrative example to show the effectiveness of the proposed method. Originality/value This work partially satisfies a long-sought research need for establishing a comprehensive construction steel bar procurement system, making it a very useful source of information for practitioners and researchers. The proposed method can be used to minimize a key performance limitation that the conventional rebar procurement practice for crowded building sites may experience.

Digitalisation of asset management in Chinese temples in Hong Kong

This paper presents the efforts to digitalise the asset management of Chinese temples in Hong Kong, which is a new trial in the industry. To revitalise these unique heritage buildings and empower the temples’ administration with digital tools, three Chinese temples in Ap Lei Chau and Aberdeen directly administrated by the Chinese Temples Committee (CTC) were selected as pilot projects where a digital twin was developed to enable digital asset management. Laser scanning technologies were encompassed, integrated with close-range terrestrial photogrammetry to generate precise 3D Building Information Modelling (BIM). An asset information management platform was built to consolidate the operation data and establish a common data environment to support data-driven management, with parameters collected from Internet-of-Things (IoT) devices and Digital Works Supervision System (DWSS) statistics consolidated. Moreover, Augmented Reality (AR) was leveraged for tourism and education, and different phases of historic temples were modelled to present the evolution and culture over time. Through this project, in addition to the higher operation efficiency enabled by the digitalised workflow and consolidated data source, a digital blueprint was built through the formulation of a digitalisation roadmap, which contains customised recommendations on cutting-edge digital tools’ implementation in relation to heritage monuments. This can be extended to 24 Chinese temples in the future as well.

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.

Dual-Level Framework for OpenBIM-Enabled Design Collaboration

Design coordination and collaboration are crucial in the architecture, engineering, and construction (AEC) industries, necessitating the integration of diverse disciplines and expertise to achieve unified and functional designs. Traditionally, these disciplines operate in isolation, leading to a fragmented design process. Building Information Modeling (BIM), recognized for its collaborative capabilities, presents an opportunity to revolutionize traditional design practices. However, existing research on BIM primarily assumes an ideal environment where all major participants use BIM models, overlooking the dynamics of mixed 2D Computer-aided Design (CAD) and 3D BIM environments. Addressing this research gap, this study aims to establish a dual-level OpenBIM-enabled collaborative design framework, enhancing the design process across various disciplines. The study employs a case study approach, applying this framework to an airport project in Chengdu, China. The airport includes different corridors that are similar in scope and scale but distinct enough to allow for a comparative study. The results demonstrate a 27% faster completion rate, a 98% reduction in design errors, and improved user satisfaction with the proposed method. The paper concludes by discussing the limitations of the study and suggesting avenues for future research.

Adaptation 4D and 5D BIM for BIM/GIS data integration in construction project management

Construction project management contains a sequence of activities such as planning, completing, scheduling and cost estimation. Building Information Modelling (BIM) is one of the technologies that can be used to manage the complexity of construction documentation. BIM provides a digital visualization of building structures in three dimensional (3D) and store all the geometric and semantic data likes construction element, material properties, construction details, and schedules but lacking feature like geospatial analyses. Geographic Information System (GIS) can apply in construction project to overcome the limitation because GIS can offer advantages in route design, cost estimation, site layout, material layout design and construction planning. Therefore, integration BIM/GIS is recommended to produce an operational of construction project management. This paper presents the advantages of BIM-based in term of scheduling (4D BIM) and cost estimation (5D BIM) in the scope of project management to control the time efficiency and cost saving during the construction process. 4D and 5D BIM data were examine during data integration between BIM and GIS by using Feature Manipulation Engine to bring the gap in construction project management. 4D BIM data develop using Naviswork while 5D BIM developed using Autodesk Revit. GIS data management was used to maintain and update the construction database to assist the project planning.

Data-driven integration framework for four-dimensional building information modeling simulation in modular construction: a case study approach

Abstract Modular construction is becoming more popular because of its efficiency, cost-saving, and environmental benefits, but its successful implementation necessitates detailed planning, scheduling, and coordination. Building information modeling (BIM) and four-dimensional (4D) simulation techniques have emerged as invaluable tools for visualizing and analyzing the construction process in order to meet these requirements. However, integrating distinctive data sources and developing comprehensive 4D BIM simulations tailored to modular construction projects present significant challenges. Case studies are used in this paper to define precise data needs and to design a robust data integration framework for improving 4D BIM simulations in modular construction. The validation of the framework in a real-world project demonstrates its efficacy in integrating data, promoting cooperation, detecting risks, and supporting informed decision-making, ultimately enhancing modular building results through more realistic simulations. By solving data integration difficulties, this research provides useful insights for industry practitioners and researchers, enabling informed decision-making and optimization of modular building projects.

Exploring the rate of adoption and implementation depth of building information modeling (BIM): A case of Kuwait

The Architecture, Engineering, and Construction (AEC) industry in the State of Kuwait has witnessed a decline in efficiency with most projects going above budget and past project deadlines. Building Information Modeling (BIM) creates a collaborative common-data working environment between various stakeholders leading to improved productivity and efficiency while also minimizing technical challenges, and reducing design errors. Moreover, adding the concept of time to the model (4D BIM) for the purposes of planning and scheduling further optimizes inter-disciplinary collaboration. The first phase of this research aims to evaluate current BIM adoption rates and implementation depth in Kuwait through a survey distributed to organizations in the Union of Kuwait Engineering Offices and Consultant Houses. The results indicated that the rate of BIM adoption, represented by whether or not organizations have used BIM in previous/current projects is relatively high. Implementation depth, represented by the percentage of projects for which BIM is being used, is concluded to be low. In terms of willingness to adopt BIM, most organizations use and are considering using BIM as a visualization tool. The second phase aims to examine the applicability of generating predefined process templates for Tract Houses in the northwest Sulibikhat area constructed by the Public Authority of Housing Welfare (PAHW) used for 4D BIM. The Process Pattern Recognition approach is applied to a BIM-based construction schedule where the schedule is extracted, decomposed, processed, and filtered. Relevant and potentially relevant processes are identified and verified leading to defined process templates. This research contributes to the AEC industry by aiding in transitioning from current Computer-Aided-Design (CAD) practices to BIM technology.

The Impacts of 5D Building Information Modelling towards Cost Management in the Construction Industry

Cost management is one of the most essential tasks in engineering construction. It is a crucial factor in the success of the project. The fifth dimension (5D) of building information modeling (BIM) has a different impact on cost management in the construction industry. Whereas, stakeholders are not eager to adopt procedures that are unrelated to their business. Therefore, this study determines the link between 5D BIM practices and cost management in the China construction industry to guarantee that the most suitable practice in 5D BIM is adopted. A systematic review method was adopted to assess the 5D BIM approaches to conduct the relationship between 5D BIM practices and cost management. The research involves references to the science journal databases Emerald Insight, Science Direct, and Google Scholar to find the pertinent journal publications in the data collection phases. The findings of the research indicate that the implementation of 5D BIM is agreed as beneficial, and a thorough conceptual framework is developed from this research to demonstrate the connections between 5D BIM practices and cost management in China construction sector. Cost management is optimized in various ways for 5D BIM implementation. To accomplish effective cost management and maintain their competitiveness in the market, construction industry need to comprehend the 5D BIM relevant solution and relationships. A throughout conceptual framework that demonstrates the connections between 5D BIM and its impact on the cost managment of construction industry is established through a thorough literature review, enabling stakeholders to comprehend potential benefits in cost management. The research provides new awareness and information about the 5D BIM technologies that are available concerning the demands for cost management. It address the value-added of the construction industry output and improve the industrial competitiveness of enterprises.