A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this chapter aims at providing (1) a state-of-the-art review of the literature concerning deep learning techniques for detecting distress in both pavements and buildings; (2) research advancements per asset/structure type; and (3) future recommendations in deep learning applications for distress detection. A critical analysis was conducted on 181 deep learning-based crack detection papers. A structured analysis was adopted to analyse major articles according to their focus of study, employed methods, findings, and limitations. The utilisation of deep learning to detect pavement cracks is advanced compared to assessing and evaluating buildings’ structural health. There is a need for studies that compare different CNN models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation, running costs, capturing data frequency, and deep learning tool. In conclusion, the future of applying deep learning algorithms instead of manual inspection for detecting distresses has shown promising results. The availability of previous research and the required improvements in the proposed computational tools and models (e.g., artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as, building a knowledge base from the findings of other research to support developing future workable solutions.
Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this chapter aims at providing (1) a state-of-the-art review of the literature concerning deep learning techniques for detecting distress in both pavements and buildings; (2) research advancements per asset/structure type; and (3) future recommendations in deep learning applications for distress detection. A critical analysis was conducted on 181 deep learning-based crack detection papers. A structured analysis was adopted to analyse major articles according to their focus of study, employed methods, findings, and limitations. The utilisation of deep learning to detect pavement cracks is advanced compared to assessing and evaluating buildings’ structural health. There is a need for studies that compare different CNN models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation, running costs, capturing data frequency, and deep learning tool. In conclusion, the future of applying deep learning algorithms instead of manual inspection for detecting distresses has shown promising results. The availability of previous research and the required improvements in the proposed computational tools and models (e.g., artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as, building a knowledge base from the findings of other research to support developing future workable solutions.
Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology
https://orcid.org/https://orcid.org/0000-0001-7443-4723
The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this chapter aims at providing (1) a state-of-the-art review of the literature concerning deep learning techniques for detecting distress in both pavements and buildings; (2) research advancements per asset/structure type; and (3) future recommendations in deep learning applications for distress detection. A critical analysis was conducted on 181 deep learning-based crack detection papers. A structured analysis was adopted to analyse major articles according to their focus of study, employed methods, findings, and limitations. The utilisation of deep learning to detect pavement cracks is advanced compared to assessing and evaluating buildings’ structural health. There is a need for studies that compare different CNN models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation, running costs, capturing data frequency, and deep learning tool. In conclusion, the future of applying deep learning algorithms instead of manual inspection for detecting distresses has shown promising results. The availability of previous research and the required improvements in the proposed computational tools and models (e.g., artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as, building a knowledge base from the findings of other research to support developing future workable solutions.
Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology
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: 7 ; 129-158
2022-07-10
30 pages
Article/Chapter (Book)
Electronic Resource
English
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