A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Deep Learning to Improve Construction Site Management Tasks
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The digital construction transformation requires utilising emerging digital technology such as deep learning to automate implementing tasks. Therefore, this article evaluates the current state of utilising deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions and find research gaps to carry out more research to bridge revealed knowledge and practice gaps. First, the scientometric analysis is conducted for 181 articles to assess the density of publications on different topics of deep learning-based construction management applications. After that, a thematic and gap analysis is conducted to analyse the contributions and limitations of key published articles in each area of application. The scientometric analysis indicates four main applications of deep learning in construction management: automating progress monitoring, automating safety warning for workers, managing construction equipment, and integrating IoT with deep learning to collect data from the site automatically. The thematic and gap analysis refers to many successful cases of deep learning in automating site management tasks. However, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners’ and workers’ perspectives to implement mentioned applications in their daily tasks. This article enables researchers to directly find the research gaps in the developed solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected in speeding the digital construction transformation, a strategy worldwide. This article is the first to adopt a structured technique to assess deep learning-based construction site management applications to enable researchers/practitioners to either adopt these applications in their projects or conduct further research to extend developed solutions and bridge revealed knowledge gaps.
Deep Learning to Improve Construction Site Management Tasks
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The digital construction transformation requires utilising emerging digital technology such as deep learning to automate implementing tasks. Therefore, this article evaluates the current state of utilising deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions and find research gaps to carry out more research to bridge revealed knowledge and practice gaps. First, the scientometric analysis is conducted for 181 articles to assess the density of publications on different topics of deep learning-based construction management applications. After that, a thematic and gap analysis is conducted to analyse the contributions and limitations of key published articles in each area of application. The scientometric analysis indicates four main applications of deep learning in construction management: automating progress monitoring, automating safety warning for workers, managing construction equipment, and integrating IoT with deep learning to collect data from the site automatically. The thematic and gap analysis refers to many successful cases of deep learning in automating site management tasks. However, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners’ and workers’ perspectives to implement mentioned applications in their daily tasks. This article enables researchers to directly find the research gaps in the developed solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected in speeding the digital construction transformation, a strategy worldwide. This article is the first to adopt a structured technique to assess deep learning-based construction site management applications to enable researchers/practitioners to either adopt these applications in their projects or conduct further research to extend developed solutions and bridge revealed knowledge gaps.
Deep Learning to Improve Construction Site Management Tasks
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The digital construction transformation requires utilising emerging digital technology such as deep learning to automate implementing tasks. Therefore, this article evaluates the current state of utilising deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions and find research gaps to carry out more research to bridge revealed knowledge and practice gaps. First, the scientometric analysis is conducted for 181 articles to assess the density of publications on different topics of deep learning-based construction management applications. After that, a thematic and gap analysis is conducted to analyse the contributions and limitations of key published articles in each area of application. The scientometric analysis indicates four main applications of deep learning in construction management: automating progress monitoring, automating safety warning for workers, managing construction equipment, and integrating IoT with deep learning to collect data from the site automatically. The thematic and gap analysis refers to many successful cases of deep learning in automating site management tasks. However, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners’ and workers’ perspectives to implement mentioned applications in their daily tasks. This article enables researchers to directly find the research gaps in the developed solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected in speeding the digital construction transformation, a strategy worldwide. This article is the first to adopt a structured technique to assess deep learning-based construction site management applications to enable researchers/practitioners to either adopt these applications in their projects or conduct further research to extend developed solutions and bridge revealed knowledge gaps.
Deep Learning to Improve Construction Site Management Tasks
Elghaish, Faris (author) / Pour Rahimian, Farzad (author) / Brooks, Tara (author) / Dawood, Nashwan (author) / Abrishami, Sepehr (author)
Blockchain of Things and Deep Learning Applications in Construction ; Chapter: 6 ; 99-127
2022-07-10
29 pages
Article/Chapter (Book)
Electronic Resource
English
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