Interactive mechanism of working environments and construction behaviors with cognitive work analysis: an elevator installation case study

Influencing Factors, Mechanism and Prevention of Construction Workers' Unsafe Behaviors: A Systematic Literature Review

Unsafe behaviors of construction workers are one of the main causes of accidents at construction sites. The research on unsafe behaviors of workers helps to reduce the incidence of accidents and has attracted much attention. However, a systematic literature review in this field is still lacking, which hinders stakeholders' comprehensive understanding of the unsafe behaviors of construction workers. Therefore, the aim of this study is to address this research gap based on retrieved literature from the Web of Science. First, the study conducted a descriptive analysis of the year, quantity, publishing organization, and keywords of the literature. In addition, three research topics were identified and discussed, including the influencing factors of construction workers' unsafe behaviors, the formation mechanism of unsafe behaviors, and the pre-control methods of unsafe behaviors. Moreover, a research framework was proposed and future research directions were also suggested. The research findings promote stakeholders' understanding of the influencing factors, formation mechanism, and pre-control methods of construction workers' unsafe behaviors, and lead to future research directions in the studied field.

Antecedents of safety behavior in construction: A literature review and an integrated conceptual framework

There has been no scarcity in the literature of suggested antecedents of employee safety behavior, and this paper brings together the disaggregated antecedents of safety behavior in the construction field. In total, 101 eligible empirical articles are obtained. Bibliometric and context analyses are combined to identify the influential journals, scholars, keywords, use of theory, research methods, and countries or regions of the empirical samples. The 83 factors that are identified are divided into five groups, namely (a) individual characteristics, (b) workgroup interactions, (c) work and workplace design, (d) project management and organization, and (e) family, industry, and society. This indicates that the causes of safety behavior are manifold. Various factors from different systems likely work in concert to create situations in which an individual chooses to comply with safety rules and participate voluntarily in safety activities. Given this, we propose that safety behavior is only an ostensible symptom of more complex "The Self-Work-Home-Industry/Society" systems and establish a safety behavior antecedent analysis and classification model. Based on this model, we develop a resource flow model, illustrating why, how, and when the flow of resources between the five systems-namely the self system, work system, home system, work-home interface system, and industry/society system-either promotes or inhibits safety behavior. The safety behavior antecedent analysis and classification model and resource flow model are based mainly on bioecological system theory and resources theories. Avenues for future theoretical development and method designs are suggested based on the reviewed findings and the two conceptual models. The intention with this systematic review together with the two integrated conceptual models is to advance theoretical thinking on how safety behavior can be promoted, or instead, inhibited.

Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency

To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. Compared with other algorithms, the proposed FSM algorithm has the largest mutual information (0.1337), the highest accuracy (0.9899), the lowest false positive rate (0.0624), and the lowest false negative rate (0.1126); compared with other models, the elevator fault warning model proposed in this study has the lowest root mean-square error (RMSE) value (0.0201), the highest accuracy (0.9834), the lowest Loss value (0.0012), and the shortest convergence time (88.2608s), indicating that the elevator safety monitoring system and elevator fault warning model are feasible. This study establishes a good direction for elevator safety monitoring efficiency in China.

Understanding the Sociocognitive Process of Construction Workers' Unsafe Behaviors: An Agent-Based Modeling Approach

Previous literature has recognized that workers’ unsafe behavior is the combined result of both isolated individual cognitive processes and their interaction with others. Based on the consideration of both individual cognitive factors and social organizational factors, this paper aims to develop an Agent-Based Modeling (ABM) approach to explore construction workers’ sociocognitive processes under the interaction with managers, coworkers, and foremen. The developed model is applied to explore the causes of cognitive failure of construction workers and the influence of social groups and social organizational factors on the workers’ unsafe behavior. The results indicate that (1) workers’ unsafe behaviors are gradually reduced with the interaction with managers, foremen, and workers; (2) the foreman is most influential in reducing workers’ unsafe behaviors, and their demonstration role can hardly be ignored; (3) the failure of sociocognitive process of construction workers is affected by many factors, and cognitive process errors could be corrected under social norms; and (4) among various social organizational factors, social identity has the most obvious effect on reducing workers’ unsafe behaviors, and preventive measures are more effective than reactive measures in reducing workers’ unsafe behaviors.