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    Next-Generation Building Condition Assessment: BIM and Neural Network Integration

    Source: Journal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006::page 04024050-1
    Author:
    Mani Amrouni Hosseini
    ,
    Mehdi Ravanshadnia
    ,
    Majid Rahimzadegan
    ,
    Saeed Ramezani
    DOI: 10.1061/JPCFEV.CFENG-4828
    Publisher: American Society of Civil Engineers
    Abstract: The inspection of building components plays a crucial role in reducing maintenance costs throughout the building’s life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management.
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      Next-Generation Building Condition Assessment: BIM and Neural Network Integration

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    contributor authorMani Amrouni Hosseini
    contributor authorMehdi Ravanshadnia
    contributor authorMajid Rahimzadegan
    contributor authorSaeed Ramezani
    date accessioned2025-04-20T10:35:12Z
    date available2025-04-20T10:35:12Z
    date copyright9/27/2024 12:00:00 AM
    date issued2024
    identifier otherJPCFEV.CFENG-4828.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305006
    description abstractThe inspection of building components plays a crucial role in reducing maintenance costs throughout the building’s life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management.
    publisherAmerican Society of Civil Engineers
    titleNext-Generation Building Condition Assessment: BIM and Neural Network Integration
    typeJournal Article
    journal volume38
    journal issue6
    journal titleJournal of Performance of Constructed Facilities
    identifier doi10.1061/JPCFEV.CFENG-4828
    journal fristpage04024050-1
    journal lastpage04024050-16
    page16
    treeJournal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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