Hybrid Recommendation Approach for Behavior Modification in the Chinese Construction Industry

Self Cite

Purpose: Construction workers’ reactions to safety-related issues during operation vary from person to person due to their different occupational levels, which can be attributed to various influencing factors and their correspondingly complicated interactions. This research aims to propose an integrated framework to combine the concepts of these factors and provide a holistic interpretation of the interrelationship among them. Methods: Based on items that were mainly extracted from competency theory, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to identify the critical factors from the data collected from 243 scaffolders on Wuhan Metro construction sites. The interactions among the identified factors were then analyzed, and the safety competency model was thus established with the use of structural equation modeling (SEM). Results: A total of 17 items were identified as critical to workers’ safety competency, and these were further tested and attributed to four factors: (1) individual character and inclination; (2) self-adjustment and adaptability; (3) working attitudes; (4) safety-related operation qualification. Subsequent analysis showed that all the factors significantly contributed to one’s safety competency, and individual character and inclination contributed most to the formation of one’s ability, while the intermediating effects of self-adjustment and adaptability should not be neglected both in theoretical and practical terms. The resultant safety competency model consisting of these four factors was revealed to share a hierarchical structure with the classical competency model. Significance: This study provided an integrated theoretical framework and a set of modeling approaches to combine the related concepts and facilitate a greater understanding of construction safety in terms of workers’ characteristics and behaviors. Practical implications: This study presented a tentative approach for assessing construction workers’ safety competency, as well as emphasized to the managers and professionals the necessity of developing training systems to ensure workers are integrated into a crew in an appropriate and smooth manner. Limitations and Future Work: The volume and the scope of samples impeded the study from achieving a more generalized result and a more cost-efficient data collection approach is in need of development for a comprehensive and in-depth investigation.

Section: Modelingmentioning

confidence: 99%

Understanding the Factors and the Corresponding Interactions That Influence Construction Worker Safety Performance from a Competency-Model-Based Perspective: Evidence from Scaffolders in China

Liang

Fung

Xiong

et al. 2019

Self Cite

“…However, in China, the number of fatal accidents in the construction industry has surpassed that in the mining industry since 2012, ranking the first among all industrial and production sectors in China [ 8 ]. Furthermore, the average number of fatal accidents in the construction industry in China has exceeded 1800 and increased to more than 3800 in 2016 and 2017, which was significantly higher than the figures of many developed countries [ 43 , 44 , 45 ]. Particularly, 1732 accidents occurred, killing 1752 persons in the first half of 2018 alone [ 46 ].…”

Section: Investigation Of the Fatality Rate Of Various Countriesmentioning

confidence: 99%

Comparative Analysis of the National Fatality Rate in Construction Industry Using Time-Series Approach and Equivalent Evaluation Conditions

Shim

Jeong

Lee

et al. 2022

Fatality rates such as fatalities per full-time equivalent workers are officially used to compare the risk level of the construction industry among various countries. However, each country evaluates the fatality rate using different conditions. This paper presents the comparison of fatality rates of various countries using conventional (national data) and pair (equivalent condition) methods through a time-series approach. The research was conducted in three stages. The risk level was evaluated in order in South Korea (1.54), Japan (0.84), Mexico (0.83), China (0.70), United Kingdom (0.15), and Singapore (0.13) in terms of national data. However, the risk level was re-evaluated in order in China (2.27), South Korea (2.05), Mexico (1.23), Singapore (0.98), Japan (0.80), and United Kingdom (0.47) in terms of equivalent conditions. The risk level of each can be changed when the fatality rate is compared under given equivalent conditions.

“…Compared with other combinations of unsafe behaviors, these four critical groups of unsafe behaviors reflect unsafe behaviors that have a greater impact on the four types of accidents and thus should be collaboratively controlled in building construction. Furthermore, as shown in Table 5, corrective measures are suggested for the unsafe behaviors in these groups, including safety inspection, safety training for workers and safety training for managers/engineers based on the previous work [43,44]. For the unsafe behaviors, such as engaging in a specialized operation without a permit, which will not cause accidents immediately, safety inspection should be strictly executed before and during construction tasks.…”

Section: Backward Reasoning For Identifying Critical Unsafe Behaviorsmentioning

confidence: 99%

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Guo

et al. 2019

Self Cite

Unsafe behavior is a critical factor leading to construction accidents. Despite numerous studies supporting this viewpoint, the process by which accidents are influenced by construction workers’ unsafe behaviors and the extent to which unsafe behaviors are involved in this process remain poorly discussed. Therefore, this paper selects cases from Chinese building construction accidents to explore the probabilistic transmission paths from unsafe behaviors to accidents using a Bayesian network. First, a list of unsafe behaviors is constructed based on safety standards and operating procedures. Second, several chains of unsafe behaviors are extracted from 287 accident cases within four types (fall, collapse, struck-by and lifting) to form a Bayesian network model. Finally, two accidents are specifically analyzed to verify the rationality of the proposed model through forward reasoning. Additionally, critical groups of unsafe behaviors leading to the four types of accidents are identified through backward reasoning. The results show the following: (i) The time sequence of unsafe behaviors in a chain does not affect the final posterior probability of an accident, but the accident attribute strength of an unsafe behavior, affects the growth rate of the posterior probability of an accident. (ii) The four critical groups of unsafe behaviors leading to fall, collapse, struck-by, and lifting are identified. This study is of theoretical and practical significance for on-site behavioral management and accident prevention.