# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
###### 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. machine learning; deep learning; jobsite safety management; structural health monitoring; workforce safety +
Footnote †: journal: Medical  
sensor aging problems. Under these circumstances, noise filtering approaches could be used to correct the data. However, this is also tedious and requires expertise. Similarly, workforce safety issues in jobsite safety management (JSM) are also a challenge for the AEC industry [9]. For example, the US Occupational Health and Safety Agency (OSHA) recorded a surprisingly high death toll of 1008 construction worker fatalities in 2018 that were mainly caused by common on-site accidents, such as being struck by falling objects and falling from heights [10; 11]. Traditionally, construction on-site safety monitoring relies on site patrols and surveillance [12]. However, the complex nature of site dynamics would make on-site safety monitoring more difficult and less proactive [13]. In addition, the fatigue level of workers cannot be accurately identified. Over the past few years, researchers have been formulating various machine learning (ML) applications for various fields [14; 15; 16; 17]. Prime ML applications in SHM and JSM include structure damage detection [18] and on-site worker safety monitoring [19]. The rapid evolution of graphics processing units (GPUs) has dramatically improved the computational capacity for processing ML algorithms, which has led to the advent of an increasing amount of deep learning (DL) applications that are underpinned by improved GPU performance [20]. In particular, the convolutional neural network (CNN), a DL algorithm, achieved extraordinary results in the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), which is a benchmark in object classification and detection for thousands of object classes and millions of images [21]. Currently, DL has outperformed many advanced algorithms in numerous fields [20; 22; 23]. More and more, DL applications are being developed and deployed to address image classification, data augmentation and object detection problems [24; 25; 26; 27]. Besides that, scholars have also made encouraging progress in integrating DL and natural language processing (NLP) for the text extraction of construction safety reports [28; 29]. Through analyzing and classifying such reports, hidden dangers can be identified in time. Therefore, corresponding measures can be taken to avoid similar accidents in the future.