Building Information Modeling Data Research Articles

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction.

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.

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.

Integrating GIS and BIM with MCDM for infrastructure planning: a comprehensive framework

PurposePresent study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.Design/methodology/approachThis analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.FindingsA comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.Research limitations/implicationsThe study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.Practical implicationsThe proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.Originality/valueBy integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

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 generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network

Precast construction is a productivity-improving technology in the architectural, engineering, and construction industry that improves construction efficiency by combining factory-based manufacturing and lean assembly. Many international efforts have encouraged the adoption of this approach. This study presents an integrated Building Information Modelling (BIM) with technological automation interoperability to enable generative design and prefabrication for precast buildings. A generic BIM-based graph representation is established to explicitly formulate buildings' spatial and geometric features. Following this, a graph-constrained layout generator is developed, with a generative modelling algorithm and graph convolutional neural network, to extract pairwise spatial-geometric features for generating the optimal precast layout. This is followed by semantic enrichment of BIM data (i.e., Industry Foundation Classes) with precast data schema to facilitate data transformation for prefabrication automation until site delivery. The holistic approach presented in this study empowers pre-construction planning optimisation and fabrication automation in precast construction.

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.

3D object recognition using deep learning for automatically generating semantic BIM data

The successful reuse of Building Information Model (BIM) data is reliant on the use of clearly defined objects. File formats such as the Industry Foundation Classes (IFC) along with classification systems offer approaches to standardising semantic BIM data. Inconsistent application of these standards during BIM authoring results in objects that are unable to be reused across systems. This research proposes a Machine Learning (ML) based alternative for detecting objects and enriching BIM data based on geometrical representations of objects to support interoperability across systems throughout the supply chain. First, an existing dataset of training and test data is modified to correspond with predefined data types from the IFC schema. Second, a convolutional deep belief network is implemented and applied on new testing data from the National BIM Library to identify 3D objects and enabling semantic enrichment of BIM data. Finally, the testing is extended to four real-world industry BIM models. The findings show that the geometric data from emerging BIM object libraries can be used to search and retrieve objects within industry BIM models based on geometric rather than semantic data.

BIM-based task planning method for wheeled-legged rebar binding robot

ABSTRACT Building Information Modeling (BIM) data has the advantages of high quality, operability, and parametric capabilities. In recent years, the application of BIM in construction planning for building robots has been increasing. Task planning for rebar binding is an important aspect of construction, and there is a growing trend of using robots to improve the accuracy and efficiency of binding operations. However, the complex and dynamic construction site environment poses significant challenges for task planning in rebar binding. Therefore, this paper proposes a new BIM-based task planning method for a wheeled-legged rebar binding robot, which can quickly generate an optimal task sequence. Firstly, an initial set of rebar intersection points is generated based on BIM data. Then, considering the execution dead zone of the robot and the variable workspace of the binding mechanism, the rebar intersection points set is decomposed and filtered using morphological opening operations. Finally, a constrained genetic algorithm is employed to sort the set of task points. Simulation and real-world experiments were conducted using a wheeled-legged rebar binding robot as the research subject. The results demonstrate that, compared to manual planning, the average binding efficiency is approximately 1500 points per hour, which is 1.97 times higher than manual operation. This validates the applicability and effectiveness of the proposed method.

A BIM-integrated multi objective optimization model for sustainable building construction management

Purpose The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process. Design/methodology/approach Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors. Findings The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment. Practical implications This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors. Originality/value The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

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

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

Building Information Modeling and Geographic Information System: Integrated Framework in Support of Facility Management (FM)

Currently, the management of public real estate stocks is one of the most challenging activities. In this case, facility management emerges as a strategic but complex discipline that requires a great amount of integrated and structured data and information of heterogeneous origin and nature. Starting from this premise, this paper focuses on how FM discipline can be supported by Building Information Modeling (BIM) and Geographic Information System (GIS) integrated methodologies and tools achieving high levels of effectiveness and efficiency in public building stock management. This led to the definition and development of a framework that mainly takes advantage of open-source tools (e.g., Quantum Geographic Information System—QGIS, and Dynamo) and non-proprietary data exchange formats like the COBie (Construction Operations Building Information Exchange) international standard. This methodological framework is capable of integrating different but strongly complementary tools, data, information, and expert knowledge for a multiscale approach. The methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform but leads instead to an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. The findings of experimental applications of the methodological approach to the former Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are presented and discussed. Finally, the analysis about potentials and criticalities of the developed methodological framework aims to trigger the widest future development and dissemination of this research.

Blockchain technology applications in maintaining heritage buildings

Preservation of cultural heritage in the face of human and natural disasters, as well as improper maintenance practices, is a crucial endeavor that must be undertaken throughout the lifespan of heritage buildings. In order to maximize the value derived from these assets and strike a harmonious balance between accurate documentation and performance optimization, asset management strategies have been employed. In recent years, the advent of innovative methodologies such as Building Information Modeling (BIM) and laser scanning has opened new possibilities for their application in various contexts. However, successful implementation of these methodologies faces a range of challenges, including limited knowledge, inexperienced teams, and reluctance to embrace change on the part of administrators. To address these obstacles and enhance asset management practices, the integration of BIM, Chatbot, and Blockchain methodology has emerged as a viable solution. This methodology not only facilitates the improvement of data security and user data capture but also streamlines the process of updating BIM models. In this research, we propose a data-driven Chatbot framework based on Blockchain, specifically designed to manage maintenance activities through the utilization of BIM. The proposed framework serves as a valuable tool for government authorities and asset managers seeking to adopt BIM for the preservation of heritage buildings. Comprising three key components – the BIM model, the Blockchain model, and the Chatbot model – the framework offers comprehensive support. The BIM model encompasses all relevant information pertaining to the management of the heritage building, while the Chatbot model replicates human interaction through text-based communication. Users can access up-to-date BIM data by posing predefined questions to the Chatbot. On the other hand, the Blockchain model serves as a secure and immutable record-keeping system, validating and verifying user updates, thereby fostering trust among stakeholders. This proposed model empowers asset stakeholders to request and update asset information via a trusted and reliable channel. To showcase the capabilities of the framework, we present a real-world case study centered on the Omar Toson Palace in Cairo, Egypt. Through this case study, we demonstrate the effectiveness and potential of our proposed framework in meeting the challenges associated with heritage building preservation.

Interlinking BIM and GIS data for a semantic pedestrian network and applications in high-density cities

In high-density cities, pedestrians frequently traverse many publicly accessible indoor spaces, such as metro stations and footbridges, which seamlessly connect with outdoor sidewalks, forming indoor-outdoor combined pedestrian networks. However, the information island phenomenon hinders these connections in the digital world because outdoor sidewalks and indoor spaces are modelled using Geographical Information Systems (GIS) and Building Information Modelling (BIM) technologies, respectively, which challenges obtaining integrated information for intelligent pedestrian services. This study presents an approach for interlinking BIM and GIS data for a semantic pedestrian network using semantic web technologies. The proposed approach automatically converts BIM and GIS data into linked data and establishes interlinkages between BIM and GIS data through semantic queries and inferences. The resulting semantic pedestrian network based on integrated linked data graphs forms a knowledge base, including topological and geometrical information and abundant semantic information from BIM and GIS datasets. The application potential of the semantic pedestrian network is demonstrated through information query and semantic route planning. This research contributes to establishing indoor-outdoor combined semantic pedestrian networks ensuring seamless data integration.

Improving Building Information Modeling (BIM) Implementation throughout the Construction Industry

Building Information Modeling (BIM) is extensively used in the construction industry due to its benefits throughout the Project Life Cycle (PLC). BIM can simulate buildings throughout PLC, detect and resolve problems, and improve building visualization that contributes to the representation of actual project details in the construction stage. BIM contributes to project management promotion by detecting problems that lead to conflicts, cost overruns, and time delays. This work aims to implement an effective BIM for the Iraqi construction projects’ life cycle. The methodology used is a literature review to collect the most important factors contributing to the success of BIM implementation, interview the team of the Central Bank of Iraq (CBI) building, and strive to improve the BEP of the CBI building. However, previous studies indicate collaborative work and communications enhance effective BIM implementation, which can improve BIM use by applying a BEP and an AEC (UK) BIM protocol that leads to positive BIM impact. BEP comprises important information and goals related to the intended project, including the BIM collaborative process (process map), information exchange requirements, BIM data management, BIM model management, and quality control, which are considered essential for enhancing BIM collaboration during PLC. This paper concludes that implementing BIM effectively requires overcoming obstacles faced by Iraqi construction projects. Effective BIM implementation requires improving collaboration and communication throughout the construction process, which could be achieved by depending on the BIM Execution Planning Guide(BEP Guide) and the AEC (UK) BIM Protocol 2012 V2.0

Green Retrofitting of Building using BIM-based Sustainability Optimization

The construction industry consumes around 55 percent of Pakistan’s annual energy and is the country’s most energy-intensive sector. It is, therefore, significant to devise a novel and effective model of green retrofitting of current buildings based on Building Information Modelling (BIM) which aims to reduce building energy consumption to effectively meet diversified climatic concerns and to promote sustainable structures. This research aims to reform the existing building by modifying the design parameters for an inefficient building envelope based on BIM simulation and data optimization results to optimize the overall energy consumption. BIM tools, such as Autodesk Revit and DesignBuilder have been used for the evaluation of the optimized energy-efficient design by comparing different design alternatives. After a detailed analysis, this research has concluded that the existing building is consuming a high amount of annual energy. With the help of BIM tools, which possess advanced algorithms for an effective optimization process, a list of ‘green’ solutions i.e., optimal designs has been generated, out of which the optimal and cost-effective design saved 46% of energy in total. In this study, the design variables selection has been fulfilled according to the local environmental sustainability.

User-Centered Fire Emergency Response System Design for Presenting BIM and IoT Data

Advanced technologies, such as Building Information Modeling (BIM) and the Internet of Things (IoT), can supply information for decision-making during a fire emergency. However, the data retrieved fails to cover all the critical knowledge required for situational awareness or fails to present to the users in a usable and effective way. This study aims to develop an effective user interface for a Fire Emergency Response System (FERS) based on BIM and IoT technologies to provide the information required for situational awareness. The study uses the User-Centered Design (UCD) method to achieve this aim and develops the Graphical User Interface (GUI) of the FERS based on the identified information requirements. To evaluate the usability of the developed GUI and to finalize the design, the feedback from the experts was retrieved via recordings when they were using the prototype and a combined usability questionnaire of Usability Metric for User Experience (UMUX) and After-Scenario Questionnaire (ASQ). The findings illustrate that the GUI successfully presented the information to meet the user goals and has the potential to significantly improve the efficacy of fire responding operations and minimize the risk of loss of life and property damage.

Zero watermarking algorithm for BIM data based on distance partitioning and local feature

ABSTRACT Zero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper’s algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection.

Quantitative Evaluation Method and Process of BIM Data for Generating BIM-Based 2D Deliverables

Views, drawings, and data extracted from building information modeling (BIM) constitute essential deliverables throughout the lifecycle of an architecture, engineering, and construction project, offering crucial insights for comprehending the design. Nevertheless, many employers evaluating BIM deliverables lack standardized criteria for the specific intended use of each BIM outcome, which hampers the practical utility of BIM results. This study introduces a quantitative evaluation method for the management of BIM-based two-dimensional (2D) deliverables. The BIM outcome measurement index for 2D deliverables (BOMI-2D) is formulated to provide a quantitative assessment of BIM data, focusing on their composition, structure, data readiness, and consistency. Pilot tests validated the efficacy of BOMI-2D, revealing an impressive 88.3% reduction in additional work required for 2D deliverables when data readiness increased by 25% and consistency improved by 32%. BOMI-2D is poised to play a pivotal role as an evaluation index for BIM data and outcomes, ultimately enhancing their utilization and productivity.

Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.