Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
复制标题
DOI：
10.3390/app11020821
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
L. Hou;Haosen Chen;Guomin Zhang;Xiangyu Wang
中科院分区：
文献类型：
--
作者：
L. Hou;Haosen Chen;Guomin Zhang;Xiangyu Wang
安全是建筑、工程和建筑（AEC）行业的一个重要主题。然而，传统的结构健康监测（SHM）和现场安全管理（JSM）方法不仅效率低下，而且成本高昂。在过去的十年中，学者们开发了广泛的深度学习（DL）应用程序，以解决自动化结构检测和现场安全监测，例如识别结构缺陷，恶化模式，不安全的劳动力行为和潜在的风险因素。虽然许多研究已经检查了DL方法的有效性，但迄今为止，还没有一个全面的，系统的，以证据为基础的审查，调查在SHM和JSM行业使用DL的有效性的所有个别文章，也没有检查这些方法问题的证据。因此，本文的目的是揭示当前研究进展的最新水平，并确定相关差距、挑战和未来的工作。采用CiteSpace数据库对2010 - 2020年深度学习应用的研究趋势、进展和前沿进行了系统的总结。接下来，进行了以应用为中心的文献综述，总结了研究差距，建议和未来的研究方向。总体而言，这篇综述深入了解SHM和JSM，旨在帮助研究人员制定更多类型的有效DL应用程序，这些应用程序目前还没有得到充分的解决。
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.