Pub Date : 2024-10-30DOI: 10.1016/j.autcon.2024.105841
Ali Mansouri , Hosein Taghaddos , Ala Nekouvaght Tak , Amir Sadatnya , Kamyab Aghajamali
Large-scale earthmoving operations, common in mining excavation, contribute significantly to Greenhouse Gas (GHG) emissions. This paper introduces a simulation-based system aimed at quantifying these emissions and identifying practical and achievable steps for reducing them. The system we developed considers site-specific factors, including equipment specifications, topography, route, and weather conditions. Notably, it enables ‘what-if’ scenario analyses, allowing us to evaluate the impact of different parameters on emissions. The system’s unique feature is the optimal allocation of resources through an intelligent decision-making system, which reduced GHG emissions by approximately 7.6% in the case study. Turning off equipment during idle periods further decreases emissions by up to 11.3%. These findings highlight the potential of operational adjustments in mitigating the environmental impact of earthmoving projects.
{"title":"Simulation-based planning of earthmoving equipment for reducing greenhouse gas (GHG) emissions","authors":"Ali Mansouri , Hosein Taghaddos , Ala Nekouvaght Tak , Amir Sadatnya , Kamyab Aghajamali","doi":"10.1016/j.autcon.2024.105841","DOIUrl":"10.1016/j.autcon.2024.105841","url":null,"abstract":"<div><div>Large-scale earthmoving operations, common in mining excavation, contribute significantly to Greenhouse Gas (GHG) emissions. This paper introduces a simulation-based system aimed at quantifying these emissions and identifying practical and achievable steps for reducing them. The system we developed considers site-specific factors, including equipment specifications, topography, route, and weather conditions. Notably, it enables ‘what-if’ scenario analyses, allowing us to evaluate the impact of different parameters on emissions. The system’s unique feature is the optimal allocation of resources through an intelligent decision-making system, which reduced GHG emissions by approximately 7.6% in the case study. Turning off equipment during idle periods further decreases emissions by up to 11.3%. These findings highlight the potential of operational adjustments in mitigating the environmental impact of earthmoving projects.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105841"},"PeriodicalIF":9.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantification and optimisation of Construction Waste (CW) in the design stages are vital to implementing preventive CW management measures. Previous ICT-integrated CW models are not efficiently upscaled to achieve an interoperable and automated workflow. Therefore, this paper presents a BIM-integrated semantic framework for CW quantification and optimisation from the early design stages. A CW data model using Semantic-Web-Technologies (SWT) was developed and integrated with BIM. The results proved that unified data structure, standardised and granular information, established semantic relationships between building material and CW data, and diverse measurement units proposed in the framework facilitate seamless and dynamic information flows between BIM and CW platforms. The research outcomes are critical to improving interoperability and automation across the CW assessment process, enhancing the accuracy and reliability of results, supporting timely and integrated decision-making, and easing communication and collaboration among the supply-chain members. A test-case building demonstrates the application of the framework.
{"title":"BIM-integrated semantic framework for construction waste quantification and optimisation","authors":"Subarna Sivashanmugam, Sergio Rodriguez Trejo, Farzad Rahimian","doi":"10.1016/j.autcon.2024.105842","DOIUrl":"10.1016/j.autcon.2024.105842","url":null,"abstract":"<div><div>Quantification and optimisation of Construction Waste (CW) in the design stages are vital to implementing preventive CW management measures. Previous ICT-integrated CW models are not efficiently upscaled to achieve an interoperable and automated workflow. Therefore, this paper presents a BIM-integrated semantic framework for CW quantification and optimisation from the early design stages. A CW data model using Semantic-Web-Technologies (SWT) was developed and integrated with BIM. The results proved that unified data structure, standardised and granular information, established semantic relationships between building material and CW data, and diverse measurement units proposed in the framework facilitate seamless and dynamic information flows between BIM and CW platforms. The research outcomes are critical to improving interoperability and automation across the CW assessment process, enhancing the accuracy and reliability of results, supporting timely and integrated decision-making, and easing communication and collaboration among the supply-chain members. A test-case building demonstrates the application of the framework.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105842"},"PeriodicalIF":9.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.autcon.2024.105838
Yuanlin Zhao , Wei Li , Jiangang Ding , Yansong Wang , Lili Pei , Aojia Tian
Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model’s computational load. To handle varying segmentation challenges, a range of models with different computational demands has been designed to suit diverse needs. The most lightweight model can be deployed for real-time use on edge devices. A module in the neck of the pipeline encodes crack coordinate information, and end-to-end training has resulted in state-of-the-art performance across multiple datasets.
{"title":"Crack instance segmentation using splittable transformer and position coordinates","authors":"Yuanlin Zhao , Wei Li , Jiangang Ding , Yansong Wang , Lili Pei , Aojia Tian","doi":"10.1016/j.autcon.2024.105838","DOIUrl":"10.1016/j.autcon.2024.105838","url":null,"abstract":"<div><div>Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model’s computational load. To handle varying segmentation challenges, a range of models with different computational demands has been designed to suit diverse needs. The most lightweight model can be deployed for real-time use on edge devices. A module in the neck of the pipeline encodes crack coordinate information, and end-to-end training has resulted in state-of-the-art performance across multiple datasets.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105838"},"PeriodicalIF":9.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-26DOI: 10.1016/j.autcon.2024.105839
Andong Qiu, Zhouwang Yang
Distributed photovoltaic power systems, typically deployed in complex scenarios like irregular rooftops, present a challenging detailed cable routing problem (DCRP). This involves grouping solar modules and routing cables to connect each group, traditionally addressed through manual design. This paper presents a variable-depth large neighborhood search (VDLNS) algorithm to address the DCRP, which is modeled as a specialized cycle covering problem using arc-flow and partition formulations. A cycle-split heuristic, derived from DCRP’s connection to the traveling salesman problem, is introduced and combined with a series of destroy operators to construct the VDLNS algorithm. Numerical experiments conducted on both synthetic and real-world instances validated the algorithm’s efficacy, achieving an average total cost reduction of 12.87% on house rooftop instances compared to manual design. The results indicate that the method effectively streamlines photovoltaic system design by delivering cost-efficient cable routing schemes within a reasonable timeframe.
{"title":"Variable-depth large neighborhood search algorithm for cable routing in distributed photovoltaic systems","authors":"Andong Qiu, Zhouwang Yang","doi":"10.1016/j.autcon.2024.105839","DOIUrl":"10.1016/j.autcon.2024.105839","url":null,"abstract":"<div><div>Distributed photovoltaic power systems, typically deployed in complex scenarios like irregular rooftops, present a challenging detailed cable routing problem (DCRP). This involves grouping solar modules and routing cables to connect each group, traditionally addressed through manual design. This paper presents a variable-depth large neighborhood search (VDLNS) algorithm to address the DCRP, which is modeled as a specialized cycle covering problem using arc-flow and partition formulations. A cycle-split heuristic, derived from DCRP’s connection to the traveling salesman problem, is introduced and combined with a series of destroy operators to construct the VDLNS algorithm. Numerical experiments conducted on both synthetic and real-world instances validated the algorithm’s efficacy, achieving an average total cost reduction of 12.87% on house rooftop instances compared to manual design. The results indicate that the method effectively streamlines photovoltaic system design by delivering cost-efficient cable routing schemes within a reasonable timeframe.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105839"},"PeriodicalIF":9.6,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.autcon.2024.105843
Gyumin Lee , Ali Turab Asad , Khurram Shabbir , Sung-Han Sim , Junhwa Lee
Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.
{"title":"Robust localization of shear connectors in accelerated bridge construction with neural radiance field","authors":"Gyumin Lee , Ali Turab Asad , Khurram Shabbir , Sung-Han Sim , Junhwa Lee","doi":"10.1016/j.autcon.2024.105843","DOIUrl":"10.1016/j.autcon.2024.105843","url":null,"abstract":"<div><div>Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105843"},"PeriodicalIF":9.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.autcon.2024.105844
SeyedeZahra Golazad , Abbas Mohammadi , Abbas Rashidi , Mohammad Ilbeigi
As the use of predictive models in construction rapidly increases, the need for preprocessing raw construction data has become more critical. This systematic review investigates data preprocessing techniques for machine learning (ML), deep learning (DL), and reinforcement learning (RL) models in the construction domain. Through a comprehensive analysis of 457 studies, the prevalence of six data types (i.e., tabular, image, video frame, time series, text, and point cloud) and their respective preprocessing methods are examined. Key findings reveal data transformation, cleaning, reduction, augmentation, and scaling as fundamental preprocessing categories, with applications varying across data types. The paper highlights knowledge gaps, including limited synthetic data adoption, lack of standardized annotation practices, absence of comprehensive preprocessing frameworks, and need for automated labeling. Furthermore, critical considerations regarding data privacy, security, sharing, and management practices are discussed. The review underscores the pivotal role of robust data preprocessing in enabling reliable predictive models.
{"title":"From raw to refined: Data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models","authors":"SeyedeZahra Golazad , Abbas Mohammadi , Abbas Rashidi , Mohammad Ilbeigi","doi":"10.1016/j.autcon.2024.105844","DOIUrl":"10.1016/j.autcon.2024.105844","url":null,"abstract":"<div><div>As the use of predictive models in construction rapidly increases, the need for preprocessing raw construction data has become more critical. This systematic review investigates data preprocessing techniques for machine learning (ML), deep learning (DL), and reinforcement learning (RL) models in the construction domain. Through a comprehensive analysis of 457 studies, the prevalence of six data types (i.e., tabular, image, video frame, time series, text, and point cloud) and their respective preprocessing methods are examined. Key findings reveal data transformation, cleaning, reduction, augmentation, and scaling as fundamental preprocessing categories, with applications varying across data types. The paper highlights knowledge gaps, including limited synthetic data adoption, lack of standardized annotation practices, absence of comprehensive preprocessing frameworks, and need for automated labeling. Furthermore, critical considerations regarding data privacy, security, sharing, and management practices are discussed. The review underscores the pivotal role of robust data preprocessing in enabling reliable predictive models.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105844"},"PeriodicalIF":9.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.autcon.2024.105837
Qishen Ye, Yihai Fang, Nan Zheng
Road work zones pose significant safety risks to both vehicles passing by and the construction workers moving within the work zones. Over recent years, significant research efforts have been dedicated to work zone safety, particularly by leveraging emerging technologies. This paper aims to review the literature on performance evaluation of safety technologies designed to mitigate struck-by hazards. This review identified 57 relevant publications focusing on technology evaluation, which were critically reviewed using the Four-component Cyber-Physical System (CPS) hierarchy and the Adapted Layer of Protection Analysis (ALOPA) framework. The CPS hierarchy-based review unveiled the focused components under evaluation, the relationship among these components, the methodologies employed, and the key performance results. The extent and completeness of the evaluation methods were examined through the ALOPA framework. The findings of this research highlight emerging trends that explore the impact of human factors on accident avoidance outcomes in risk-free virtual environments and suggest several prospective considerations as per ALOPA that can guide future research towards performance-based evaluations and design optimisations.
{"title":"Performance evaluation of struck-by-accident alert systems for road work zone safety","authors":"Qishen Ye, Yihai Fang, Nan Zheng","doi":"10.1016/j.autcon.2024.105837","DOIUrl":"10.1016/j.autcon.2024.105837","url":null,"abstract":"<div><div>Road work zones pose significant safety risks to both vehicles passing by and the construction workers moving within the work zones. Over recent years, significant research efforts have been dedicated to work zone safety, particularly by leveraging emerging technologies. This paper aims to review the literature on performance evaluation of safety technologies designed to mitigate struck-by hazards. This review identified 57 relevant publications focusing on technology evaluation, which were critically reviewed using the Four-component Cyber-Physical System (CPS) hierarchy and the Adapted Layer of Protection Analysis (ALOPA) framework. The CPS hierarchy-based review unveiled the focused components under evaluation, the relationship among these components, the methodologies employed, and the key performance results. The extent and completeness of the evaluation methods were examined through the ALOPA framework. The findings of this research highlight emerging trends that explore the impact of human factors on accident avoidance outcomes in risk-free virtual environments and suggest several prospective considerations as per ALOPA that can guide future research towards performance-based evaluations and design optimisations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105837"},"PeriodicalIF":9.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.autcon.2024.105834
M. Franciosi, M. Kasser, M. Viviani
Digital twins are evolving to oversee the entire construction life cycle, with a strong emphasis on sustainability across environmental, financial, regulatory, and administrative dimensions. This paper introduces a methodology for managing existing bridges through an adaptable digital twin. The aim of this research is to develop a framework for constructing digital twins that, by enabling structural analysis and “what-if” scenario simulations, supports more reliable maintenance decision-making. Such type of digital twin ensure safety, extend lifespan, and provide a precise database for managing end-of-life processes within a circular “cradle to cradle” framework. This methodology also addresses obsolescence issues related to software evolution and the longer lifespan of a bridge compared to its creator. A case study demonstrates the methodology's effectiveness, showing that digital twins can be flexible, cost-effective tools for managing all types of bridges, including small and existing ones.
{"title":"Digital twins in bridge engineering for streamlined maintenance and enhanced sustainability","authors":"M. Franciosi, M. Kasser, M. Viviani","doi":"10.1016/j.autcon.2024.105834","DOIUrl":"10.1016/j.autcon.2024.105834","url":null,"abstract":"<div><div>Digital twins are evolving to oversee the entire construction life cycle, with a strong emphasis on sustainability across environmental, financial, regulatory, and administrative dimensions. This paper introduces a methodology for managing existing bridges through an adaptable digital twin. The aim of this research is to develop a framework for constructing digital twins that, by enabling structural analysis and “what-if” scenario simulations, supports more reliable maintenance decision-making. Such type of digital twin ensure safety, extend lifespan, and provide a precise database for managing end-of-life processes within a circular “cradle to cradle” framework. This methodology also addresses obsolescence issues related to software evolution and the longer lifespan of a bridge compared to its creator. A case study demonstrates the methodology's effectiveness, showing that digital twins can be flexible, cost-effective tools for managing all types of bridges, including small and existing ones.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105834"},"PeriodicalIF":9.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.autcon.2024.105845
Xin Zuo , Yu Sheng , Jifeng Shen , Yongwei Shan
CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1 k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE_DB1, highlighting its generalization capability.
{"title":"Topology-aware mamba for crack segmentation in structures","authors":"Xin Zuo , Yu Sheng , Jifeng Shen , Yongwei Shan","doi":"10.1016/j.autcon.2024.105845","DOIUrl":"10.1016/j.autcon.2024.105845","url":null,"abstract":"<div><div>CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1 k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE_DB1, highlighting its generalization capability.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105845"},"PeriodicalIF":9.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.autcon.2024.105823
Sungmin Yoon , Jeyoon Lee , Jiteng Li , Peng Wang
A virtual model that mathematically represents operational behaviors is essential for implementing the concepts of digital twins (DTs) and building information modeling (BIM) to achieve intelligent, optimal building operations. However, current research lacks an approach to reliably construct virtual models. This paper introduces a concept named virtual in-situ modeling (VIM), designed to comprehensively represent building behaviors. The VIM framework is based on five key aspects: modeling environments, model types, modeling sources, modeling approaches, and model fusion techniques. VIM bridges BIM and DT, enabling virtual modeling during the operational phase and enhancing both BIM-based DT and DT-enhanced BIM. Case studies conducted using real building operations demonstrate the effectiveness of VIM, achieving a highly accuracy (RMSE of 0.24 °C). Additionally, the VIM-assisted fault detection and diagnosis (FDD) provided early detection and diagnostic estimation, outperforming FDD without the virtual model. This paper highlights the potential of VIM for advanced building operations and maintenance.
{"title":"Virtual in-situ modeling between digital twin and BIM for advanced building operations and maintenance","authors":"Sungmin Yoon , Jeyoon Lee , Jiteng Li , Peng Wang","doi":"10.1016/j.autcon.2024.105823","DOIUrl":"10.1016/j.autcon.2024.105823","url":null,"abstract":"<div><div>A virtual model that mathematically represents operational behaviors is essential for implementing the concepts of digital twins (DTs) and building information modeling (BIM) to achieve intelligent, optimal building operations. However, current research lacks an approach to reliably construct virtual models. This paper introduces a concept named virtual in-situ modeling (VIM), designed to comprehensively represent building behaviors. The VIM framework is based on five key aspects: modeling environments, model types, modeling sources, modeling approaches, and model fusion techniques. VIM bridges BIM and DT, enabling virtual modeling during the operational phase and enhancing both BIM-based DT and DT-enhanced BIM. Case studies conducted using real building operations demonstrate the effectiveness of VIM, achieving a highly accuracy (RMSE of 0.24 °C). Additionally, the VIM-assisted fault detection and diagnosis (FDD) provided early detection and diagnostic estimation, outperforming FDD without the virtual model. This paper highlights the potential of VIM for advanced building operations and maintenance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105823"},"PeriodicalIF":9.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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