Artificial Cognition to Predict and Explain the Potential Unsafe Behaviors of Construction Workers

被引:4
作者
Deng, Shuwen [1 ]
Ni, Pingan [2 ]
Zhu, Honglei [3 ]
Cai, Yili [4 ]
Pan, Yonggang [1 ]
机构
[1] Xinjiang Univ, Coll Architecture Engn, 1230 Yananway, Urumqi 830000, Peoples R China
[2] Xian Univ Architecture & Technol, Coll Architecture, 13 Yanta Rd, Xian 710055, Peoples R China
[3] Carleton Univ, Criminol & Criminal Justice, 1233 Colonel Dr, Ottawa, ON K1S5B7, Canada
[4] Ningbo Univ, Coll Foreign Languages, 818 Fenghua Rd, Ningbo 315211, Zhejiang, Peoples R China
关键词
Artificial cognition; Potential unsafe behavior; Predicting behavior; Behavior management; LATENT CLASS ANALYSIS; SAFETY BEHAVIOR; FALLS; SYSTEMS; HEIGHTS; HEALTH; MODEL;
D O I
10.1061/JCEMD4.COENG-14130
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Unsafe behavior is considered the primary cause of construction safety accidents. However, the main measures for unsafe behavior management are real-time monitoring and postevent correction, which cannot prevent unsafe behavior. Therefore, this study attempted to construct an artificial cognition approach to predict the potential unsafe behavior of workers and explain why workers engage in unsafe behaviors. First, based on the cognitive model of unsafe behavior, data on workers were collected with a questionnaire, and the cognitive model was validated. Second, the cognitive process of unsafe behaviors was analyzed using latent class analysis, and the cognitive characteristics of four types of unsafe behaviors were obtained. Subsequently, with the cognitive model of unsafe behavior as the input attribute, seven types of algorithms (gradient Boosting, random forest, na & iuml;ve bayes, back propagation, K-nearest neighbor, logistic regression, and support vector machine) were used to construct artificial cognition to predict the potential unsafe behaviors of workers. The results showed that all seven algorithms performed well for prediction. Thus, artificial cognition that simulates the cognitive process of unsafe behavior is not limited to particular algorithms. Finally, artificial cognition was empirically validated in a construction project. The findings demonstrated that artificial cognition could effectively predict the potential unsafe behavior of workers and provide an explanation for why workers engage in unsafe behaviors.
引用
收藏
页数:13
相关论文
共 70 条
[1]  
American Bureau of Labor Statistics, 2020, Census of fatal occupational injuries summary
[2]   A qualitative investigation of factors influencing unsafe work behaviors on construction projects [J].
Asilian-Mahabadi, Hassan ;
Khosravi, Yahya ;
Hassanzadeh-Rangi, Narmin ;
Hajizadeh, Ebrahim ;
Behzadan, Amir H. .
WORK-A JOURNAL OF PREVENTION ASSESSMENT & REHABILITATION, 2018, 61 (02) :281-293
[3]  
Barabasi Albert-Laszlo., 2011, Bursts: The Hidden Patterns behind Everything We Do, from Your e- Mail to Bloody Crusades
[4]   Statistical modeling: The two cultures [J].
Breiman, L .
STATISTICAL SCIENCE, 2001, 16 (03) :199-215
[5]   Multimodel inference - understanding AIC and BIC in model selection [J].
Burnham, KP ;
Anderson, DR .
SOCIOLOGICAL METHODS & RESEARCH, 2004, 33 (02) :261-304
[6]   Human factors engineering in healthcare systems: The problem of human error and accident management [J].
Cacciabue, P. C. ;
Vella, G. .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2010, 79 (04) :E1-E17
[7]   Relationship between Unsafe Working Conditions and Workers' Behavior and Impact of Working Conditions on Injury Severity in US Construction Industry [J].
Chi, Seokho ;
Han, Sangwon ;
Kim, Dae Young .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2013, 139 (07) :826-838
[8]   Why operatives engage in unsafe work behavior: Investigating factors on construction sites [J].
Choudhry, Rafiq M. ;
Fang, Dongping .
SAFETY SCIENCE, 2008, 46 (04) :566-584
[9]  
Christian TM, 2014, 2014 International Conference on Data and Software Engineering (ICODSE), DOI 10.1109/ICODSE.2014.7062654
[10]   Group cognitive characteristics of construction Workers ' unsafe behaviors from personalized management [J].
Deng, Shuwen ;
Zhu, Honglei ;
Cai, Yili ;
Pan, Yonggang .
SAFETY SCIENCE, 2024, 175