Engineering, Construction and Architectural Management最新文献

{"title":"A morphology-Euclidean-linear recognition method for rebar point clouds of highway tunnel linings during the construction phase","authors":"Lizhi Zhou, Chuan Wang, Pei Niu, Hanming Zhang, Ning Zhang, Quanyi Xie, Jianhong Wang, Xiao Zhang, Jian Liu","doi":"10.1108/ecam-12-2023-1227","DOIUrl":"https://doi.org/10.1108/ecam-12-2023-1227","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"8 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a flourishing workplace: exploring the impact of digitalization on young construction professionals’ physical–mental well-being","authors":"Sachin Batra, Aritra Halder","doi":"10.1108/ecam-02-2024-0190","DOIUrl":"https://doi.org/10.1108/ecam-02-2024-0190","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The construction industry has more propensity to negatively affect employees’ physical–mental well-being due to the inherently intense and demanding nature of the work involved. Digitalization can streamline the construction processes, and reduce stress, overtime and overall job-related pressure generated due to the nature of employment, contributing to the well-being of employees. Hence, the authors examined how digitalization, technostress and individual resilience could contribute to construction professionals’ physical–mental well-being using the transaction model of stress, self-determination theory and job-demand resources theory.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Data were collected from 86 young professionals using a structured survey. The professionals were working in Indian construction organizations where digitalization is implemented extensively. The survey consists of 21 items to measure four latent variables namely digitalization, technostress, physical–mental well-being and individual resilience. The study employs a partial least squares structural equation modeling (PLS-SEM) approach to examine the theoretical model empirically.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results revealed that digitalization was positively associated with physical–mental well-being and negatively associated with technostress. Further, individual resilience was a moderating variable in the relationship between digitalization and technostress. Finally, technostress partially mediated the relationship between digitalization and physical–mental well-being.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>Digitalization has emerged as a valuable tool to tackle these challenges and improve the overall well-being of construction personnel. In the present study, digitalization is found to augment the physical–mental well-being of young construction professionals. Also, digitalization helps to significantly reduce technostress, thereby improving the physical–mental well-being of young professionals.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the architecture billings index (ABI) using machine learning predictive models","authors":"Sooin Kim, Atefe Makhmalbaf, Mohsen Shahandashti","doi":"10.1108/ecam-06-2023-0544","DOIUrl":"https://doi.org/10.1108/ecam-06-2023-0544","url":null,"abstract":"&lt;h3&gt;Purpose&lt;/h3&gt;\u0000&lt;p&gt;This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Design/methodology/approach&lt;/h3&gt;\u0000&lt;p&gt;The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Findings&lt;/h3&gt;\u0000&lt;p&gt;The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.&lt;/p&gt;&lt;!--/ Abstract__block --&gt;\u0000&lt;h3&gt;Practical implications&lt;/h3&gt;\u0000&lt;p&gt;The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bi","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic monitoring the risk coupling of foundation pits: integrated point cloud, computer vision and Bayesian networks approach","authors":"Xuelai Li, Xincong Yang, Kailun Feng, Changyong Liu","doi":"10.1108/ecam-02-2024-0149","DOIUrl":"https://doi.org/10.1108/ecam-02-2024-0149","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"44 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal relationship between project financing and overruns in major dam projects in Africa","authors":"Oluwole Alfred Olatunji, James Olabode Bamidele Rotimi, Funmilayo Ebun Rotimi, Chathurani C.W. Silva","doi":"10.1108/ecam-03-2023-0286","DOIUrl":"https://doi.org/10.1108/ecam-03-2023-0286","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Cost and schedule overruns are rife in dam projects. Normative evidence espouses overruns as though they are inimical to development and prosperity aspirations of stakeholders. This study examines the causal relationship between project financing and overruns.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Causative data were extracted from completion reports of 28 major dam projects in Africa. Each of the projects was financed jointly by up to 10 international development lenders. Relationships between causes of overruns and project outcomes were analysed.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Analyses elicit indicators of remarkable correlations between finance procedures and project outcomes. Lenders’ disposition to risk attenuation was the main debacles to project success. Interests had mounted, whilst release of fund was erratic and ill-timed. Finance objectives and mechanisms were grossly inadequate for projects’ intense bifurcations. Projects had slowed or stalled because lenders’ risks attenuation processes were purposed to favour lenders’ objectives, and not projects’ interests. In addition, findings also show project owners’ own funds and the number of lenders to a single project correlate with overruns.</p><!--/ Abstract__block -->\u0000<h3>Practical implications</h3>\u0000<p>Findings imply commercial complexities around major projects. They also show transactions are shaped by subtle (mis)trust behaviours in project finance procedures. Thus, scholarly solutions to project performance issues should consider behavioural issues of stakeholding parties more broadly, beyond contractors and project owners. Project finance ecosystems are vulnerable to major actors’ self-interests, opportunism and predatory conducts. Borrowers would manage this by developing and improving their capacity to build resilience and trust. Evidence shows intense borrower nations in Africa have limited capacity and acuity for these.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This study contextualises megaprojects in complexity rather than cost. Its additionality is in how finance steers absolute control of project environment away from project owners and how finance administration triggers risks and overrun.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"44 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Architects and designers on LinkedIn: perceptions and strategies for professional success","authors":"Camila Marcela Sauer, Samer Skaik, Roksana Jahan Tumpa","doi":"10.1108/ecam-07-2023-0716","DOIUrl":"https://doi.org/10.1108/ecam-07-2023-0716","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>LinkedIn, as a professional networking platform, plays a crucial role in connecting professionals globally and facilitating their professional growth. This study aims to analyse the perceptions of architects and designers regarding the utilisation of LinkedIn for career development.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The research focuses on gathering professional perceptions and opinions through a qualitative analysis of primary data. A semi-structured interview approach was used for data collection. The study selected 12 actively engaged professionals from the architecture and design industry in Perth, Western Australia, who actively use LinkedIn.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The study's findings reveal diverse opinions and experiences among professionals in the architecture and design industry regarding LinkedIn. It identifies several positive impacts of LinkedIn on the Australian architecture and design industry. Architects and designers recognise the potential benefits of LinkedIn in expanding their professional networks, showcasing their work, accessing learning opportunities and contributing to industry outcomes. They also appreciate the platform's ability to connect them with peers, clients, suppliers and industry, fostering knowledge sharing and collaboration in the evolving work environment.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This study contributes to the existing literature by extending the understanding of the importance of self-presentation on LinkedIn, identifying factors influencing career goal achievement and highlighting the role of professional connections on social media. It establishes connections between 21st-century online innovations and their practical applications within the relevant context.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design phase collaborative risk management factors: a case study of a green rating system in South Africa","authors":"Udechukwu Ojiako, Lungie Maseko, David Root, Senthilkumar Venkatachalam, Alasdair Marshall, Eman Jasim Hussain AlRaeesi, Maxwell Chipulu","doi":"10.1108/ecam-11-2023-1138","DOIUrl":"https://doi.org/10.1108/ecam-11-2023-1138","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>We explore the design risk factors and associated managerial practices driving collaborative risk management for design efficacy in green building projects. By illuminating project design risk as an important project risk category in its own right, the study contributes to our understanding of optimising design efficacies for collaborative project risk management.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The study comprises exploratory interviews conducted with 27 industry project practitioners involved in the design and delivery/implementation of Green Star-certified building projects in South Africa.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The findings discursively highlight seven sources of design risk. We also identify seven specific collaborative risk management practices for design efficacy emerging from a consideration of how risk environments vary in the Green Star-certified projects, each with its own project design risk implications.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The study advances our understanding of how collaborations emerging from particular relational yet context-specific practices can be optimised to strengthen project risk management.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"48 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk coupling analysis of causal factors in construction fall-from-height accidents","authors":"Hongying Niu, Xiaodong Yang, Jiayu Zhang, Shengyu Guo","doi":"10.1108/ecam-12-2023-1306","DOIUrl":"https://doi.org/10.1108/ecam-12-2023-1306","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Construction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>A cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"68 4 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a climate for innovation index for architectural design firms","authors":"Minh Van Nguyen, Khanh Duy Ha, Tu Thanh Nguyen","doi":"10.1108/ecam-03-2023-0242","DOIUrl":"https://doi.org/10.1108/ecam-03-2023-0242","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>In recent years, climate for innovation has attracted wide attention from industry and academia. It is perceived as a critical component of innovation performance in the built environment sector, especially in architectural design firms (ADFs). This study attempts to assess the degree of climate for innovation in the Vietnamese ADFs under the organizational climate theory.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>A list of 13 innovation climate variables was found by reviewing previous studies and discussions with industry practitioners. These variables were then categorized into three principal factors (personal commitment, tolerance of difference, and support for creativity), forming the inputs of the fuzzy synthetic evaluation (FSE) analysis.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results showed that the overall level of innovation in the climate is moderate, implying that it is still necessary for more improvements to the Vietnamese ADFs. The fuzzy analysis revealed that support for creativity was the most critical factor, followed by tolerance of difference and personal commitment.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The proposed climate for innovation model is practical and reliable for architectural professionals and can be applied to assess other research areas. Few studies have emphasized the innovation climate in the construction sector, so this research may broaden the knowledge and literature on the industry, especially for the ADFs.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"50 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements and prospects in building information modeling (BIM) for construction: a review","authors":"Jacopo Cassandro, Claudio Mirarchi, Maryam Gholamzadehmir, Alberto Pavan","doi":"10.1108/ecam-04-2024-0435","DOIUrl":"https://doi.org/10.1108/ecam-04-2024-0435","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The paper clarifies research gaps and future directions in building information modeling (BIM) research by analyzing research trends and publication patterns. It aims to (1) systematically categorize the vast array of BIM literature into coherent main topics, (2) identify the most and least explored areas and (3) propose directions for future research based on identified research gaps.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This study uses the Latent Dirichlet Allocation (LDA) method to manage large datasets and uncover hidden patterns in academic journals and conference articles. To clarify the scholarly focus, the main topics in BIM research are categorized into three groups: (1) primary areas of focus, (2) moderately explored topics and (3) least investigated topics.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The findings revealed 10 main topics (MTs) and 57 subtopics (STs), identifying key areas such as project design and management (20%), innovative construction technology (14%) and sustainable construction/life cycle management (14%). Conversely, it also highlighted underexplored areas like Facility/safety management and urban data development, suitable for future research.</p><!--/ Abstract__block -->\u0000<h3>Research limitations/implications</h3>\u0000<p>While this work provides a structured overview of the BIM domain, it reveals opportunities for further exploring the complexity of the interrelation among interdisciplinary topics.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The novelty of this study is its extensive scope, analyzing over fifteen thousand BIM articles from 2013 to 2023, which significantly expands the literature scale previously reviewed. This comprehensive approach maps BIM research trends and gaps and also shows the hierarchical trend line of publications in each main topic, setting a benchmark for future studies.</p><!--/ Abstract__block -->","PeriodicalId":11888,"journal":{"name":"Engineering, Construction and Architectural Management","volume":"8 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}