Pub Date : 2024-07-26DOI: 10.36680/j.itcon.2024.022
Jian Wang, Maxwell Fordjour Antwi-Afari, A. Tezel, Prince Antwi-Afari, Tala Kasim
The integration and impact of artificial intelligence (AI) and cloud computing (CC) technology in the construction industry (CI) would support their implementation process and adoption. However, there is a lack of research in the extant literature, and recent advances in this field have not been explored. As such, the key research question focuses on the extent of existing literature, main research hotspots, and recent advances (i.e., research gaps and future directions) in AI in CC in the CI. To address this research question, this study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions. This review study used a four-step bibliometric-systematic review approach consisting of literature search, literature screening, science mapping analysis, and qualitative dis-cussion. The results found four main research hotspots, namely (1) construction project performance indicators, (2) data analysis and visualization, (3) construction quality control and safety, and (4) construction energy efficiency. These findings would provide valuable insights for scholars and practitioners seeking to understand and integrate AI and CC technology applications in the CI. This review study will lay a better foundation for future developments in construction project management processes, data-sharing protocols, real-time safety monitoring, and ethical implications of AI and CC technologies.
人工智能（AI）和云计算（CC）技术在建筑行业（CI）中的融合和影响将支持其实施过程和采用。然而，现有文献中缺乏相关研究，也未对该领域的最新进展进行探讨。因此，本研究的关键问题集中在建筑行业中的人工智能技术的现有文献范围、主要研究热点和最新进展（即研究差距和未来方向）。针对这一研究问题，本研究旨在通过对主要研究热点、研究空白和未来研究方向的定性讨论，对计算机网络中的人工智能进行最新综述。本综述研究采用了四步文献计量-系统综述方法，包括文献检索、文献筛选、科学图谱分析和定性讨论。研究结果发现了四个主要研究热点，即（1）建筑项目绩效指标；（2）数据分析与可视化；（3）建筑质量控制与安全；以及（4）建筑能效。这些研究结果将为学者和从业人员了解和整合人工智能与 CC 技术在 CI 中的应用提供有价值的见解。本综述研究将为建筑项目管理流程、数据共享协议、实时安全监控以及人工智能和 CC 技术的伦理影响等方面的未来发展奠定更好的基础。
{"title":"Artificial Intelligence in Cloud Computing technology in the Construction industry: a bibliometric and systematic review","authors":"Jian Wang, Maxwell Fordjour Antwi-Afari, A. Tezel, Prince Antwi-Afari, Tala Kasim","doi":"10.36680/j.itcon.2024.022","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.022","url":null,"abstract":"The integration and impact of artificial intelligence (AI) and cloud computing (CC) technology in the construction industry (CI) would support their implementation process and adoption. However, there is a lack of research in the extant literature, and recent advances in this field have not been explored. As such, the key research question focuses on the extent of existing literature, main research hotspots, and recent advances (i.e., research gaps and future directions) in AI in CC in the CI. To address this research question, this study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions. This review study used a four-step bibliometric-systematic review approach consisting of literature search, literature screening, science mapping analysis, and qualitative dis-cussion. The results found four main research hotspots, namely (1) construction project performance indicators, (2) data analysis and visualization, (3) construction quality control and safety, and (4) construction energy efficiency. These findings would provide valuable insights for scholars and practitioners seeking to understand and integrate AI and CC technology applications in the CI. This review study will lay a better foundation for future developments in construction project management processes, data-sharing protocols, real-time safety monitoring, and ethical implications of AI and CC technologies.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.36680/j.itcon.2024.023
Min-Yuan Cheng, R. R. Khasani
In the construction industry, traditional methods of cost estimation are inefficient and cannot reflect real-time changes. Modern techniques are essential to create new tools that outperform current cost estimation. This study introduced the Least Square Moment Balanced Machine (LSMBM), AI-based inference engine, to improve construction cost prediction accuracy. LSMBM considers moments to determine the optimal hyperplane and uses the Backpropagation Neural Network (BPNN) to assign weights to each data point. The effectiveness of LSMBM was tested by predicting the construction costs of residential and reinforced concrete buildings. Correlation analysis, PCA, and LASSO were used for feature selection to identify the most relevant variables, with the combination of LSMBM-PCA giving the best performance. When compared to other machine learning models, the LSMBM model achieved the lowest error values, with an RMSE of 0.016, MAE of 0.010, and MAPE of 4.569%. The overall performance measurement reference index (RI) further confirmed the superiority of LSMBM. Furthermore, LSMBM performed better than the Earned Value Management (EVM) method. LSMBM model has proven to enhanced the precision in predicting cost estimates, facilitating project managers to anticipate potential cost overruns and optimize resource allocation, provide information for strategic and operational decision-making processes in construction projects.
{"title":"Least Square Moment Balanced Machine: A New Approach To Estimating Cost To Completion For Construction Projects","authors":"Min-Yuan Cheng, R. R. Khasani","doi":"10.36680/j.itcon.2024.023","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.023","url":null,"abstract":"In the construction industry, traditional methods of cost estimation are inefficient and cannot reflect real-time changes. Modern techniques are essential to create new tools that outperform current cost estimation. This study introduced the Least Square Moment Balanced Machine (LSMBM), AI-based inference engine, to improve construction cost prediction accuracy. LSMBM considers moments to determine the optimal hyperplane and uses the Backpropagation Neural Network (BPNN) to assign weights to each data point. The effectiveness of LSMBM was tested by predicting the construction costs of residential and reinforced concrete buildings. Correlation analysis, PCA, and LASSO were used for feature selection to identify the most relevant variables, with the combination of LSMBM-PCA giving the best performance. When compared to other machine learning models, the LSMBM model achieved the lowest error values, with an RMSE of 0.016, MAE of 0.010, and MAPE of 4.569%. The overall performance measurement reference index (RI) further confirmed the superiority of LSMBM. Furthermore, LSMBM performed better than the Earned Value Management (EVM) method. LSMBM model has proven to enhanced the precision in predicting cost estimates, facilitating project managers to anticipate potential cost overruns and optimize resource allocation, provide information for strategic and operational decision-making processes in construction projects.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.36680/j.itcon.2024.024
Pardis Pishdad, Ihuoma O. Onungwa
Increasing expectancy for efficiency in the delivery of building projects and the adoption of lean production processes for construction has made the necessity for the development of an integrated system for cost estimating, cost monitoring, cost control, and payments in the construction lifecycle important. Existing 5D BIM tools are used to estimate the cost of projects during the preconstruction period. There is a lack of integration between the 5D BIM models, existing progress monitoring tools, and payment systems used in construction. Lack of standardization in the use of model elements through the project lifecycle has also been identified as one of the factors limiting automation in 5D BIM. Construction project monitoring can be automated by combining modern technologies that allow for visualization of building progress (Laser scanners, computer vision) with 5D BIM cost estimation tools. These project monitoring tools can be combined with Artificial Intelligence (AI), and Smart contracts to develop an integrated lifecycle system for cost management in construction. This paper examines existing systems used in 5D BIM to develop integrated practices and systems that will streamline the process of cost estimating, cost monitoring, cost control, and cash flow in the construction supply chain. This will reduce the inefficiency that exists today with traditional contracts and payment applications that do not interact with the 5D BIM application. By leveraging a standardized classification ID system throughout a project life cycle and applying AI and smart contract, features like cost estimation cost control, and payments can be fully streamlined, integrated, and automated. A case study of an existing construction project utilizing 5D BIM was examined. According to the study, 5D BIM is used in the pre-construction stage of a cost estimation project. It was also revealed that 5D BIM improves project cost visualization and budget control.
{"title":"Analysis of 5D BIM for cost estimation, cost control and payments","authors":"Pardis Pishdad, Ihuoma O. Onungwa","doi":"10.36680/j.itcon.2024.024","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.024","url":null,"abstract":"Increasing expectancy for efficiency in the delivery of building projects and the adoption of lean production processes for construction has made the necessity for the development of an integrated system for cost estimating, cost monitoring, cost control, and payments in the construction lifecycle important. Existing 5D BIM tools are used to estimate the cost of projects during the preconstruction period. There is a lack of integration between the 5D BIM models, existing progress monitoring tools, and payment systems used in construction. Lack of standardization in the use of model elements through the project lifecycle has also been identified as one of the factors limiting automation in 5D BIM. Construction project monitoring can be automated by combining modern technologies that allow for visualization of building progress (Laser scanners, computer vision) with 5D BIM cost estimation tools. These project monitoring tools can be combined with Artificial Intelligence (AI), and Smart contracts to develop an integrated lifecycle system for cost management in construction.\u0000This paper examines existing systems used in 5D BIM to develop integrated practices and systems that will streamline the process of cost estimating, cost monitoring, cost control, and cash flow in the construction supply chain. This will reduce the inefficiency that exists today with traditional contracts and payment applications that do not interact with the 5D BIM application. By leveraging a standardized classification ID system throughout a project life cycle and applying AI and smart contract, features like cost estimation cost control, and payments can be fully streamlined, integrated, and automated. A case study of an existing construction project utilizing 5D BIM was examined. According to the study, 5D BIM is used in the pre-construction stage of a cost estimation project. It was also revealed that 5D BIM improves project cost visualization and budget control.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36680/j.itcon.2024.012
Senem Seyis, Selen Özkan
Using common data environments (CDEs) adds value to BIM-based construction projects' organizational and project performance. However, CDEs are used very limitedly in the construction industry. One of the reasons for the limited implementation of CDEs can be associated with the need for construction companies' knowledge about the positive impacts of CDEs on performance management. A well-structured CDE can provide countless benefits and promote long-term improvements in construction projects and organizations, increasing their business success. Despite the acknowledged importance of CDEs, research needs to investigate the impacts of CDEs on project and organizational performance considering the construction KPIs. This study aims to reveal (1) how the CDEs facilitate performance measurement in the construction phase of BIM-based projects and (2) how the CDEs positively affect the project and organizational performance in the construction phase of BIM-based projects. This scope uses seven construction KPIs: time, cost, quality, safety, productivity, organizational sustainability, and client satisfaction. This study conducts a systematic literature review, semi-structured interviews with five subject-matter experts, and the two-rounded Delphi method to fulfill the research objective. The results show that implementing the CDEs in the construction phase of BIM-based projects positively affects productivity, quality, and time KPIs, followed by organizational sustainability, cost, client satisfaction, and safety, respectively. This research contributes to collating and uncovering the added value of CDEs for the organizational and performance management of BIM-based projects. Accordingly, this study would increase the awareness of construction companies about ‘how they can benefit from the data located in the CDEs from project management through knowledge management in the best way'.
{"title":"Analyzing the added value of common data environments for organizational and project performance of BIM-based projects","authors":"Senem Seyis, Selen Özkan","doi":"10.36680/j.itcon.2024.012","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.012","url":null,"abstract":"Using common data environments (CDEs) adds value to BIM-based construction projects' organizational and project performance. However, CDEs are used very limitedly in the construction industry. One of the reasons for the limited implementation of CDEs can be associated with the need for construction companies' knowledge about the positive impacts of CDEs on performance management. A well-structured CDE can provide countless benefits and promote long-term improvements in construction projects and organizations, increasing their business success. Despite the acknowledged importance of CDEs, research needs to investigate the impacts of CDEs on project and organizational performance considering the construction KPIs. This study aims to reveal (1) how the CDEs facilitate performance measurement in the construction phase of BIM-based projects and (2) how the CDEs positively affect the project and organizational performance in the construction phase of BIM-based projects. This scope uses seven construction KPIs: time, cost, quality, safety, productivity, organizational sustainability, and client satisfaction. This study conducts a systematic literature review, semi-structured interviews with five subject-matter experts, and the two-rounded Delphi method to fulfill the research objective. The results show that implementing the CDEs in the construction phase of BIM-based projects positively affects productivity, quality, and time KPIs, followed by organizational sustainability, cost, client satisfaction, and safety, respectively. This research contributes to collating and uncovering the added value of CDEs for the organizational and performance management of BIM-based projects. Accordingly, this study would increase the awareness of construction companies about ‘how they can benefit from the data located in the CDEs from project management through knowledge management in the best way'.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140686450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36680/j.itcon.2024.014
Andressa Oliveira, J. Granja, M. Bolpagni, Ali Motamedi, Miguel Azenha
Facility Management (FM) is an essential practice for the operational phase of built assets. FM requires a vast range of data arising from diverse activities, which demands tools and processes for data collection and management. The Building Information Modeling (BIM) methodology implies an integrated information management process which helps in effective communication and information flow. Therefore, adopting BIM to support FM (BIM-FM) has become the subject of academic and industry interest. When BIM methodology is implemented, information models are the main information repository, while information requirements set the guidelines for their development. The EN ISO 19650 series and EN 17412-1 are currently the most recent standards in the European context to assist the development of information requirements. However, there is still a lack of research on their detailed application to real-case scenarios. In this context, the present article cooperates with the broad adoption of BIM-FM by presenting the establishment of information requirements to inform the development of an information model for the ongoing operational phase of a university canteen, focusing on developing Exchange Information Requirements (EIR), and including other activities of ISO19650’s information management process to demonstrate the applicability of the requirements. The procedure applies the Level of Information Need (EN 17412-1) as the framework for defining the extent and granularity of the information requirements, and it employs the IFC schema to establish the required alphanumerical information. The paper thoroughly discusses the decision-making process and its implications, working as a detailed demonstration of the standards applied in a case study. The results demonstrate the efficiency of the purpose-driven process based on standardization, and the information model developed from the requirements is proven to deliver the required information accurately. Ultimately, the paper results in a robust source for process replication on FM real-case scenarios.
设施管理（FM）是建筑资产运营阶段的一项基本工作。设施管理需要从各种活动中获取大量数据，这就需要数据收集和管理的工具和流程。建筑信息模型（BIM）方法意味着一个综合的信息管理流程，有助于有效的沟通和信息流。因此，采用 BIM 支持 FM（BIM-FM）已成为学术界和业界关注的主题。在实施 BIM 方法时，信息模型是主要的信息库，而信息要求则为信息模型的开发设定了准则。EN ISO 19650 系列和 EN 17412-1 是目前欧洲协助制定信息要求的最新标准。然而，关于它们在实际场景中的具体应用，仍然缺乏研究。在此背景下，本文将配合 BIM-FM 的广泛采用，介绍信息需求的建立，为大学食堂持续运营阶段的信息模型开发提供参考，重点是开发交换信息需求（EIR），并包括 ISO19650 信息管理流程的其他活动，以证明需求的适用性。该程序采用信息需求等级（EN 17412-1）作为框架，以定义信息需求的范围和粒度，并采用 IFC 模式来建立所需的字母数字信息。本文全面讨论了决策过程及其影响，并在案例研究中详细展示了所应用的标准。结果表明了基于标准化的目的驱动流程的效率，并证明了根据需求开发的信息模型能够准确提供所需的信息。最终，本文为在调频实际案例中复制流程提供了可靠的资料来源。
{"title":"Development of standard-based information requirements for the facility management of a canteen","authors":"Andressa Oliveira, J. Granja, M. Bolpagni, Ali Motamedi, Miguel Azenha","doi":"10.36680/j.itcon.2024.014","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.014","url":null,"abstract":"Facility Management (FM) is an essential practice for the operational phase of built assets. FM requires a vast range of data arising from diverse activities, which demands tools and processes for data collection and management. The Building Information Modeling (BIM) methodology implies an integrated information management process which helps in effective communication and information flow. Therefore, adopting BIM to support FM (BIM-FM) has become the subject of academic and industry interest. When BIM methodology is implemented, information models are the main information repository, while information requirements set the guidelines for their development. The EN ISO 19650 series and EN 17412-1 are currently the most recent standards in the European context to assist the development of information requirements. However, there is still a lack of research on their detailed application to real-case scenarios. In this context, the present article cooperates with the broad adoption of BIM-FM by presenting the establishment of information requirements to inform the development of an information model for the ongoing operational phase of a university canteen, focusing on developing Exchange Information Requirements (EIR), and including other activities of ISO19650’s information management process to demonstrate the applicability of the requirements. The procedure applies the Level of Information Need (EN 17412-1) as the framework for defining the extent and granularity of the information requirements, and it employs the IFC schema to establish the required alphanumerical information. The paper thoroughly discusses the decision-making process and its implications, working as a detailed demonstration of the standards applied in a case study. The results demonstrate the efficiency of the purpose-driven process based on standardization, and the information model developed from the requirements is proven to deliver the required information accurately. Ultimately, the paper results in a robust source for process replication on FM real-case scenarios.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140686895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36680/j.itcon.2024.016
Current practices for prioritizing maintenance work orders are mainly user-driven and lack consistency in collecting, processing, and managing the large amount of data. While decision-making methods have been used to address some of the existing challenges such as inconsistency, they also have challenges including variation between comparison during the actual prioritization task as opposed to those outside of maintenance context. The data analytics and machine learning methods can help with extracting meaningful and valuable information, finding patterns, and drawing conclusions from the available data. Such methods have benefits including faster prioritization performance leading to less failure and downtimes, reduced impact of knowledge loss, decreased cognitive workload, identification of errors for adjusting the system, and determination of important factors impacting work order processing to support the development of data requirements. This paper summarizes the background on existing gaps in processing maintenance work orders and provides an overview of machine learning methods to support prioritizing work order. The paper then discusses the work order data of an educational facility as a case study, presents information on data exploration and data cleaning approach, and provides insights gained from their maintenance work order data. The insights gained present challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for addressing some of the work orders, and the correlation between criteria collected by the facility and the schedule. The paper continues by implementing artificial neural networks to benefit from work order data collected for automatically prioritizing the future work orders. The results present the optimum neural network structure based on mean squared error estimated and provides the best value for each parameter used for the development of the model. The accuracy and efficiency of the developed model was validated by the facility experts of the educational facility.
{"title":"Work order prioritization using neural networks to improve building operation","authors":"","doi":"10.36680/j.itcon.2024.016","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.016","url":null,"abstract":"Current practices for prioritizing maintenance work orders are mainly user-driven and lack consistency in collecting, processing, and managing the large amount of data. While decision-making methods have been used to address some of the existing challenges such as inconsistency, they also have challenges including variation between comparison during the actual prioritization task as opposed to those outside of maintenance context. The data analytics and machine learning methods can help with extracting meaningful and valuable information, finding patterns, and drawing conclusions from the available data. Such methods have benefits including faster prioritization performance leading to less failure and downtimes, reduced impact of knowledge loss, decreased cognitive workload, identification of errors for adjusting the system, and determination of important factors impacting work order processing to support the development of data requirements. This paper summarizes the background on existing gaps in processing maintenance work orders and provides an overview of machine learning methods to support prioritizing work order. The paper then discusses the work order data of an educational facility as a case study, presents information on data exploration and data cleaning approach, and provides insights gained from their maintenance work order data. The insights gained present challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for addressing some of the work orders, and the correlation between criteria collected by the facility and the schedule. The paper continues by implementing artificial neural networks to benefit from work order data collected for automatically prioritizing the future work orders. The results present the optimum neural network structure based on mean squared error estimated and provides the best value for each parameter used for the development of the model. The accuracy and efficiency of the developed model was validated by the facility experts of the educational facility.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140689575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36680/j.itcon.2024.013
Chi Tian, Yunfeng Chen, Jiansong Zhang, Yiheng Feng
Accurate classification of workers’ activity is critical to ensure the safety and productivity of construction projects. Previous studies in this area are mostly focused on building construction environments. Worker activity identification and classification in mobile work zone operations is more challenging, due to more dynamic operating environments (e.g., more movements, weather, and light conditions) than building construction activities. In this study, we propose a deep learning (DL) based classification model to classify workers’ activities in mobile work zones. Sensor locations are optimized for various mobile work zone operations, which helps to collect the training data more effectively and save cost. Furthermore, different from existing models, we innovatively integrate transportation and construction domain knowledge to improve classification accuracy. Three mobile work zone operations (trash pickup, crack sealing, and pothole patching) are investigated in this study. Results show that although using all sensors has the highest performance, utilizing two sensors at optimized locations achieves similar accuracy. After integrating the domain knowledge, the accuracy of the DL model is improved. The DL model trained using two sensors integrated with domain knowledge outperforms the DL model trained using three sensors without integrating domain knowledge.
{"title":"Integrating Domain Knowledge with Deep Learning Model for Automated Worker Activity Classification in mobile work zone","authors":"Chi Tian, Yunfeng Chen, Jiansong Zhang, Yiheng Feng","doi":"10.36680/j.itcon.2024.013","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.013","url":null,"abstract":"Accurate classification of workers’ activity is critical to ensure the safety and productivity of construction projects. Previous studies in this area are mostly focused on building construction environments. Worker activity identification and classification in mobile work zone operations is more challenging, due to more dynamic operating environments (e.g., more movements, weather, and light conditions) than building construction activities. In this study, we propose a deep learning (DL) based classification model to classify workers’ activities in mobile work zones. Sensor locations are optimized for various mobile work zone operations, which helps to collect the training data more effectively and save cost. Furthermore, different from existing models, we innovatively integrate transportation and construction domain knowledge to improve classification accuracy. Three mobile work zone operations (trash pickup, crack sealing, and pothole patching) are investigated in this study. Results show that although using all sensors has the highest performance, utilizing two sensors at optimized locations achieves similar accuracy. After integrating the domain knowledge, the accuracy of the DL model is improved. The DL model trained using two sensors integrated with domain knowledge outperforms the DL model trained using three sensors without integrating domain knowledge.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140688675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.36680/j.itcon.2024.015
Hanan-Allah Mohamed, Norfashiha Hashim, N. M. Yusuwan, Mohd Hafiz Hanafiah, S. M. Shamsuddin
One of the main constraints posed during the implementation of Building Information Modelling (BIM) is the high cost of adoption. This leads to studies related to value management in project and organizational contexts, especially for the public sector. However, the empirical measurement of BIM value must be done systematically to produce more accurate and valid results for applications. Therefore, this study attempts to pave the way for development of Cost Benefit Analysis (CBA) of BIM implementation in Malaysian Public Works Department (PWD) by determining the BIM benefit attributes that have been realized and cost attributes that are needed for that. A total of 150 survey questionnaires were distributed to four design departments in Malaysian PWD Headquarter (HQ) to be rated using 5-points Likert’s interval scale. Based on the data collected, the results were analyzed using Confirmatory Composite Analysis (CCA) as a method of confirming measurement quality (MCMQ) in Partial Least Square Structural Equation Modelling (PLS-SEM). The study model was conceptualized as a reflective-formative type II Hierarchical Component Model (HCM). The results indicate key benefit attributes and cost attributes related to two main BIM uses in Malaysian PWD current practices which are the ‘design review’ and ‘automated clash detection’. Based on the final form of the model, there was a total of eight key benefits of BIM implementation which are ‘lower cost’, ‘better scenario and alternative analysis’, ‘improved communication’, ‘improved coordination’, ‘improved output quality’, ‘better change management’, ‘less rework’, and ‘fewer error’. On the other hand, three cost attributes that were confirmed are ‘software related investment’, ‘hardware related investment’ and ‘infrastructure cost’. This paper provides researchers on the approach of confirming key items needed to measure BIM value and is hoped to assist the value analyst to perform the Value Management (VM) analyses for their projects.
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Pub Date : 2024-03-29DOI: 10.36680/j.itcon.2024.009
Aynur Hurriyet Turkyilmaz, Gul Polat, Aysegul Gurkan
The construction industry is a leading sector in terms of labor force development and economic involvement on a global scale. It is widely recognized that this industry faces numerous obstacles. The digital revolution has penetrated all aspects of every organization. It could offer potential solutions to the challenges faced in the construction industry, which has been generally resistant to adopting the efficiency provided by information technologies. Multiple studies are dedicated to examining the difficulties encountered by the construction industry, as well as the advancement of technologies in this field. However, further research is required to examine the extent to which construction professionals are aware of and acknowledge new technologies, as well as their expectations regarding the problem-solving capabilities of Construction 4.0 technologies. This study investigates the degree of awareness of Construction 4.0 technologies, the significance of the primary challenges frequently encountered in construction projects, the advantages expected from these technologies, and the level of consensus among various groups of construction professionals on these matters. Based on an extensive examination of existing literature, 13 specific technologies related to Construction 4.0, 11 primary challenges and 17 anticipated advantages were identified. A survey was devised and administered to Turkish construction experts, resulting in the collection of 188 valid responses. The gathered data was subsequently subjected to statistical analyses. The investigated data led to the conclusion that there was a substantial agreement among the respondents regarding the level of recognition of Construction 4.0 technologies, the primary challenges in construction projects, and the anticipated advantages of these technologies. The results of this study can guide professionals and academics in determining which innovations to endorse, considering practical needs.
{"title":"Application of Construction 4.0 Technologies: Empirical Findings from the Turkish Construction Industry","authors":"Aynur Hurriyet Turkyilmaz, Gul Polat, Aysegul Gurkan","doi":"10.36680/j.itcon.2024.009","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.009","url":null,"abstract":"The construction industry is a leading sector in terms of labor force development and economic involvement on a global scale. It is widely recognized that this industry faces numerous obstacles. The digital revolution has penetrated all aspects of every organization. It could offer potential solutions to the challenges faced in the construction industry, which has been generally resistant to adopting the efficiency provided by information technologies. Multiple studies are dedicated to examining the difficulties encountered by the construction industry, as well as the advancement of technologies in this field. However, further research is required to examine the extent to which construction professionals are aware of and acknowledge new technologies, as well as their expectations regarding the problem-solving capabilities of Construction 4.0 technologies. This study investigates the degree of awareness of Construction 4.0 technologies, the significance of the primary challenges frequently encountered in construction projects, the advantages expected from these technologies, and the level of consensus among various groups of construction professionals on these matters. Based on an extensive examination of existing literature, 13 specific technologies related to Construction 4.0, 11 primary challenges and 17 anticipated advantages were identified. A survey was devised and administered to Turkish construction experts, resulting in the collection of 188 valid responses. The gathered data was subsequently subjected to statistical analyses. The investigated data led to the conclusion that there was a substantial agreement among the respondents regarding the level of recognition of Construction 4.0 technologies, the primary challenges in construction projects, and the anticipated advantages of these technologies. The results of this study can guide professionals and academics in determining which innovations to endorse, considering practical needs.","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.36680/j.itcon.2024.010
Zahra Ghorbani, John Messner
Operations and Maintenance (O&M) costs account for 60-80% of a facility’s lifecycle costs. Using Digital Twins (DTs) can aid in making O&M more effective and efficient, leading to time and cost savings. The concept of DT started in the Aerospace domain, and other industries eventually adopted it. DTs are a new concept to the Architecture, Engineering, Construction, and Operations (AECO) Industry, and there is a lot of confusion around this concept. The purpose of this paper is to provide a DT definition along with a classification structure to create a common ground for understanding DTs in the AECO industry, which leads to easier adoption of DTs. A systematic literature review was completed to identify the existing DT definitions and classification approaches. Then, through a content analysis, the core components of definitions were extracted. The identified components were used to develop a comprehensive and inclusive DT definition for the AECO industry, using the domain language. In a similar fashion, existing DT classification structures were studied, and their components were identified through content analysis. Using the identified components, a DT classification structure was proposed for the AECO industry using domain concepts and terms. The results were validated and refined through a series of semi-structured expert interviews and surveys. Interviewees and survey participants comprised DT experts from academia and industry with diverse backgrounds. The components of the proposed DT definition include virtual representation, data connection between physical and digital entities, analysis, actuation, and frequency of updates. The classification structure consisted of three DT categories, namely Digital Twin Prototype (DTP), Digital Shadow (DS), and Cyber-Physical System (CPS).
{"title":"A categorical approach for defining digital twins in the AECO industry","authors":"Zahra Ghorbani, John Messner","doi":"10.36680/j.itcon.2024.010","DOIUrl":"https://doi.org/10.36680/j.itcon.2024.010","url":null,"abstract":"Operations and Maintenance (O&M) costs account for 60-80% of a facility’s lifecycle costs. Using Digital Twins (DTs) can aid in making O&M more effective and efficient, leading to time and cost savings. The concept of DT started in the Aerospace domain, and other industries eventually adopted it. DTs are a new concept to the Architecture, Engineering, Construction, and Operations (AECO) Industry, and there is a lot of confusion around this concept. The purpose of this paper is to provide a DT definition along with a classification structure to create a common ground for understanding DTs in the AECO industry, which leads to easier adoption of DTs. A systematic literature review was completed to identify the existing DT definitions and classification approaches. Then, through a content analysis, the core components of definitions were extracted. The identified components were used to develop a comprehensive and inclusive DT definition for the AECO industry, using the domain language. In a similar fashion, existing DT classification structures were studied, and their components were identified through content analysis. Using the identified components, a DT classification structure was proposed for the AECO industry using domain concepts and terms. The results were validated and refined through a series of semi-structured expert interviews and surveys. Interviewees and survey participants comprised DT experts from academia and industry with diverse backgrounds. The components of the proposed DT definition include virtual representation, data connection between physical and digital entities, analysis, actuation, and frequency of updates. The classification structure consisted of three DT categories, namely Digital Twin Prototype (DTP), Digital Shadow (DS), and Cyber-Physical System (CPS).","PeriodicalId":51624,"journal":{"name":"Journal of Information Technology in Construction","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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