Abstract
Purpose: 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 paper aims at providing (1) a state-of-the-art review of the literature with respect to 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.
Design/methodology/approach: A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, employed methods, findings and limitations.
Findings: The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network (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, and running costs, frequency of capturing data, and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.
Practical implications: 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.
Originality/Value: 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.
Design/methodology/approach: A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, employed methods, findings and limitations.
Findings: The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network (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, and running costs, frequency of capturing data, and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.
Practical implications: 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.
Originality/Value: 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.
| Original language | English |
|---|---|
| Pages (from-to) | 554-579 |
| Journal | Construction Innovation |
| Volume | 22 |
| Issue number | 3 |
| Early online date | 09 Nov 2021 |
| DOIs | |
| Publication status | Published - 08 Jun 2022 |
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- Deep learning for detecting distresses in buildings and pavements: A critical gap analysis
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