3D Building Information Modeling Research Articles

The Potential of Building Information Modeling in Agricultural Operations

Building Information Modeling (BIM) has been widely adopted in the construction industry, providing a virtual representation of physical objects and systems. In the context of agriculture, BIM can be applied to monitor and manage the physical and operational characteristics of farms and agricultural facilities using BIM digital twins. A BIM digital twin is a virtual representation of a physical object or system that is based on data from BIM models and other sources. The integration of sensors is essential in creating a BIM digital twin as they provide real-time data that can be used to update and maintain the virtual representation. The use of BIM digital twins in agriculture has the potential to improve the management of agricultural facilities, reduce environmental pollution, and promote the sustainability of agricultural land. Sensors can be used in agriculture to monitor various parameters such as soil moisture, temperature, and nutrient levels. BIM digital twins can also be used to monitor and manage sources of pollution, such as the use of pesticides and fertilizers, and to develop strategies to minimize their impact on the environment. BIM digital twins provide a comprehensive view of the condition of agricultural land and the factors that influence it, such as weather patterns, industry activities, and human activities. 4D BIM is an extension of traditional 3D BIM that adds a time dimension to the model. In the context of agriculture, 4D BIM can be used to visualize and simulate various processes related to time and soil management, such as planting and harvesting cycles, and to evaluate the impact of different planting strategies on crop yields. 4D BIM can also be used to model the impact of weather patterns on crop growth and to develop contingency plans for weather- related events. The use of BIM in agriculture has the potential to transform the industry by providing a new level of insight and control over agricultural operations. BIM models can be used to simulate and visualize different scenarios for agricultural facilities, such as the use of water, fertilizer, and energy, leading to improved efficiency and reduced waste. By using BIM to optimize operations, agricultural companies can increase their productivity and profitability. The integration of BIM digital twins and 4D BIM in agriculture has the potential to revolutionize the industry by providing new tools for monitoring and managing agricultural operations. The use of BIM digital twins and 4D BIM can help reduce environmental pollution, promote sustainability, and improve the efficiency and productivity of agricultural operations. Testing is needed to fully understand the potential benefits of BIM and BIM digital twins in agriculture and to develop best practices for their implementation.

Enhancing Cost Management in Construction: The Role of 5D Building Information Modeling (BIM)

Enhancing Cost Management in Construction: The Role of 5D Building Information Modeling (BIM)

Analisis Volume dan Biaya pada Proyek Pembangunan Rumah Tinggal Berbasis BIM 5D (Studi Kasus Proyek Pembangunan Rumah Tinggal 3 Lantai Tipe 130 di Kota Balikpapan)

The development of information and communication technology in the construction industry has significantly advanced in recent years. One of the latest innovations is Building Information Modeling (BIM). This research compares the volume and cost of structural work between the BIM 5D concept and the consultant's results for a 3-storey residential building in Karang Rejo, Central Balikpapan. Using Autodesk Revit, 2D drawings from the consultant were transformed into 3D models. The consultant calculated the concrete volume at 45.26 m³, whereas Autodesk Revit showed 39.34 m³, a difference of 5.92 m³ or 13.08%. For reinforcement work, the consultant's volume was 7,047.95 kg, while Autodesk Revit calculated it at 6,076.28 kg, showing a difference of 971.67 kg or 13.79%. The cost of concrete work was Rp 67,954,242.07 according to the consultant, while Autodesk Revit showed Rp 59,096,016.16, a difference of Rp 8,858,225.91 or 13.04%. The cost of reinforcement work was Rp 125,881,320.57 according to the consultant, while Autodesk Revit calculated it at Rp 108,521,878.90, with a difference of Rp 17,359,441.67 or 13.79%. Using the BIM 3D concept with Autodesk Revit provides more detailed material takeoff results, reduces material waste, and supports the BIM 5D concept for more accurate cost estimation.

Enhancing Open BIM Interoperability: Automated Generation of a Structural Model from an Architectural Model

Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD.

AI-Driven BIM Integration for Optimizing Healthcare Facility Design

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design.

Comparing 5D BIM costs: stilt housing against conventional housing for flood management

Purpose Sustainable and resilient infrastructure projects aim to reduce flooding impacts and improve community adaptability. For instance, flood-resistant stilts elevate structures, mitigating flood damage. Comprehensive consideration is crucial when adding elements to housing projects, incurring costs for all involved parties. This study aims to assess the viability of concrete stilts for cost-effective flood mitigation in Malaysian terrace housing. Design/methodology/approach The study evaluates cost implications through a comparative 5D building information modeling (BIM) cost analysis of stilted and conventional (standard) housing models. This assesses the percentage increase in total cost. Furthermore, a survey of construction professionals was undertaken. The study used online convenience and stratified sampling techniques. Out of the 222 emails that were sent, 27 construction professionals located in Johor, Sabah and Selangor within Malaysia participated in the research survey. Their perspectives on stilt housing prospects and factors for costing such structures were analyzed through a descriptive analysis using SPSS. Findings The case study models revealed that the incorporation of stilts could lead to a 21.64% increase in the overall cost per unit. This cost increase was primarily attributed to the additional reinforcement required. However, the survey findings highlighted that a majority of construction professionals perceived the cost increment to fall within the range of 10%–20%. Consequently, it becomes imperative to meticulously consider cost factors such as foundational requirements, staircases, and the extended construction duration to effectively curtail expenditures. The prospect of heightened costs potentially posing a threat to profit margins and discouraging developers necessitates careful financial management. Notwithstanding these challenges, the survey's insights underscored that professionals in the construction industry indeed recognize the potential of stilt technology in the realm of flood mitigation and management, particularly within housing projects. Practical implications This research has significant practical implications. It provides a precise financial contrast between housing categories using 5D BIM and incorporates construction experts’ viewpoints on raised housing. Enhanced design considerations for raised housing can make it economically viable, offering a cost-effective, nature-based approach to flood mitigation. This approach can bring substantial benefits to residents by reducing flood-related damages and enhancing community resilience. Originality/value One of the notable aspects of this research is its originality. It uses a dual quantitative methodology involving modeling and survey techniques to address its objectives effectively. This approach contributes significantly to the relatively limited body of research focused on stilt housing and the application of 5D BIM. By combining these methodologies, the study explores a relatively uncharted area, making a valuable contribution to the field.

Integrating Advanced Surveying Technologies and Indigenous Knowledge into 3D Building Information Modeling

Integrating Advanced Surveying Technologies and Indigenous Knowledge into 3D Building Information Modeling

Pre-processing and analysis of building information models for automated geometric quality control

Research in Quality Control (QC) process digitalisation has principally focused on novel technologies for data acquisition and processing during construction. In contrast, this manuscript focuses on the planning phase and proposes a method that analyses the as-planned 4D Building Information Model (‘BIM model’) to obtain: (1) an exhaustive list of all geometric QC instances to be checked during construction; (2) and an initial schedule of when these checks can be conducted. A rule-based approach is employed to identify the geometric QC instances in the BIM model represented as a graph. The method is demonstrated with three real case studies, including building and rail infrastructure projects, with geometric specifications encoded from the EN 13670 and EN 1090–2 standards as well as other relevant industry sources. The list of QC instances outputted by the method can be used as-is by QC surveyors and managers, or can serve as input to automated QC technologies.

Volume and Cutting Optimization of Reinforcing Steel in Construction of a Satpol PP Building Project

The world of construction is very dependent on technology, especially in the era of the industrial revolution which demands efficiency and competitiveness. The use of BIM (Building Information Modeling) technology supported by Autodesk Revit software enables more effective and efficient project planning and implementation. One application of BIM is in planning reinforcing steel requirements. To overcome material waste which often occurs due to less than optimal cutting of reinforcing steel, material management with a bar bending schedule and the use of BIM is an important solution. In the Satpol PP Building Construction Project in Bantul Regency by PT Quinad Bahana Indonesia, this new method with software was applied to correct cutting of reinforcing steel, reduce waste and increase construction efficiency. This research method includes data collection, 3D BIM modeling, comparing work volume using Autodesk Revit and conventional. After getting the reinforcing steel volume from the Autodesk Revit software, continue by entering the volume data into the BIM to obtain optimal reinforcing steel cutting patterns. The research results show that the use of Autodesk Revit software for column and beam work results in an overall difference of 11%. The volume resulting from Revit quantity take-off is less than manual analysis. Based on waste level calculations, the average value of waste level is 0.97%. This proves that the BIM is effective in reducing material waste on construction projects. Apart from reducing material waste, the BIM is also able to optimize reinforcing steel cuts.

Analisa Pengaruh Tingkat Penerapan BIM 5D Terhadap Kinerja Waktu Proyek Konstruksi

In construction, risks are usually requests for additional time, cost, or quality of work. Disputes will occur If risks are not resolved properly. In the industrial revolution 4.0, we can use the Building Information Modeling (BIM) method to increase efficiency. BIM is a digital display that contains all information about building elements integrated with the building's life cycle period. BIM will optimize the performance of construction projects. In Indonesia, several consultants/contractors have implemented BIM in construction projects. This study aims to determine and analyze what factors in the process of implementing BIM 5D that affect the performance of construction project time. The research method used is descriptive research method with the Q-method approach using closed survey research instruments to 53 contractor/consultant respondents using BIM. The results obtained from the 5D BIM indicators have 3 effects on the time performance of construction projects.

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.