Abstract
Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.
Highlights
The architecture, engineering and construction (AEC) sector is a significant driver of economic activity around the world [1]
structure health monitoring (SHM) and jobsite safety management (JSM), this paper provides a comprehensive literature review that covers machine learning (ML)- and deep learning (DL)-related research and development
To systematically identify and analyze the state-of-the-art SHM, JSM, DL and ML applications, this study used the Web of Science (WoS) as the data source for searching for articles
Summary
The architecture, engineering and construction (AEC) sector is a significant driver of economic activity around the world [1]. Structure- and workplace-related safety accidents have the potential to be life-threatening [2]. These are always some of the most overlooked things in the sector. Structure health monitoring (SHM) relates to different approaches, such as conducting regular visual inspections or relying on structural monitoring sensors [6]. The accuracy may be compromised due to changing environments or sensor aging problems. Under these circumstances, noise filtering approaches could be used to correct the data.
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