Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

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 a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and 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, used 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 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.

Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework

“…Therefore, enhancing productivity through using emerging technologies can significantly reduce carbon emissions and energy consumption. In related research, Chehri and Saeidi (2021) and Elghaish et al (2021c) proposed the integration of IoT and deep learning to detect the deterioration of structural health for bridges’ elements because of the environmental causes, which can increase the operating lifetime of these elements. Enabling CE requires continuous data collection and a deep data analysis tool therefore, Ramadoss et al (2018) developed a framework to employ low-cost sensors in re-usable products/devices to collect data and use AI to analyse these data.…”

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

“…The capability and robustness of traditional approaches have been greatly extended by deep learning techniques [16]. In DL, the term "deep" refers to a large number of layers present between the input and the output layers [17][18][19]. DL models are different from traditional machine learning approaches in that they are capable of learning the representations of the data without introducing any hand-crafted rules or knowledge and have shown great performance in solving the crack detection problem [1].…”

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

“…The integration of different emerging technologies is highly recommended to develop workable solutions for complex projects as a part of digital construction transformation (Elghaish et al, 2021a(Elghaish et al, , 2021b. The coupling of deep learning and other emerging digital technologies started from 2015 to automate objects recognitions such as workers and equipment through using Internet of Things (IoT) sensors.…”

“…There are a few attempts to review deep learning applications for construction industry, however, most of studies either focused on distresses detection or general review. For example, Elghaish et al (2021aElghaish et al ( , 2021b studied the recent published articles regarding using deep learning to detect distresses in pavements and buildings. Akinosho et al (2020) presented a study to summarize different applications of deep learning for construction industry; however, the construction site management applications were not critically highlighted.…”