Yin Junjia Ph.D., Aidi Hizami Alias Ph.D., Nuzul Azam Haron Ph.D., Nabilah Abu Bakar Ph.D.
{"title":"Deep learning for safety risk management in modular construction: Status, strengths, challenges, and future directions","authors":"Yin Junjia Ph.D., Aidi Hizami Alias Ph.D., Nuzul Azam Haron Ph.D., Nabilah Abu Bakar Ph.D.","doi":"10.1016/j.autcon.2024.105894","DOIUrl":null,"url":null,"abstract":"<div><div>Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. Deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024 in modular construction. It found that the six most popular DL algorithms in this area are “Convolutional Neural Network (CNN),” “Recurrent Neural Network (RNN),” “Generative Adversarial Network (GAN),” “Auto-Encoder (AE),” “Deep Belief Network (DBN)” and “Transformer.” However, in addition to each algorithm's limitations, problems like data constraints, talent gaps, and a lack of guidance frameworks also exist. To address these issues, three strategies are proposed. They are “establishing a multi-modal data sharing platform,” “proposing a paradigm framework for the application of DL algorithms,” and “constructing a compound construction talent training mechanism,” which provide researchers with future references.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"169 ","pages":"Article 105894"},"PeriodicalIF":9.6000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524006307","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. Deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024 in modular construction. It found that the six most popular DL algorithms in this area are “Convolutional Neural Network (CNN),” “Recurrent Neural Network (RNN),” “Generative Adversarial Network (GAN),” “Auto-Encoder (AE),” “Deep Belief Network (DBN)” and “Transformer.” However, in addition to each algorithm's limitations, problems like data constraints, talent gaps, and a lack of guidance frameworks also exist. To address these issues, three strategies are proposed. They are “establishing a multi-modal data sharing platform,” “proposing a paradigm framework for the application of DL algorithms,” and “constructing a compound construction talent training mechanism,” which provide researchers with future references.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.