Construction safety: an analysis of the cross-influence of economic, construction, and accident death factors

Exploring the economic occupational health, safety, and fatal accidents in high-risk industries

Despite advancements in occupational health and safety (OHS) management, high-risk industries in China continue to report a significant number of fatal accidents, underscoring systemic challenges in protecting the well-being of workers while supporting economic development. This study analyzed 22 years of historical data on OHS incidents, labor dynamics, and economic growth in China's high-risk industries via multiple regression and network analysis methods. The findings reveal hierarchical influence relationships, with coal mine fatalities emerging as critical upstream factors and transportation fatalities and national labor force dynamics emerging as key downstream factors. Notably, the study reveals a negative correlation between GDP and fatal workplace incidents: for every 0.461 trillion CNY increase in GDP, production safety accident deaths decrease by one. Conversely, each safety accident resulted in 1.052 coal mine fatalities and 0.153 cases of occupational disease. These results offer a novel quantitative perspective on the interplay between economic growth and workplace safety. The study's models provide practical guidance for enhancing the effectiveness of OHS prevention and control efforts, contributing to sustainable economic and public health outcomes.

Analysis of HFE impact of COVID-19 on OHS in construction enterprises

Human factors are critical to Occupational Health and Safety (OHS) in construction enterprises. However, comprehensive industry-wide recognition remains challenging, underscoring the need for Human Factors Engineering (HFE) research. This study develops an optimized HFE evaluation model based on fundamental HFE principles. Examining COVID-19's significant impact on construction enterprise OHS, this research employs an empirical investigation of 259 cases, utilizing a model that integrates NetLogo's System Dynamics (SD) and Multiple Linear Regression (MLR) to analyze the interactions between human factors and other variables. The findings reveal four key factors influencing human factors: management, material, environmental, and methodological. These factors demonstrate a quadratic parabolic relationship, with peak influence occurring at step 36 of the research period. Twelve of the 20 survey factors exhibit a linear regression relationship with human factors' four sub-factors, with pre-job training (Q9) demonstrating multiple influential interactions. The strongest correlation is between pre-job training (Q9) and living materials (Q14), with a weight coefficient of .325. Psychological counseling (Q8) and living materials (Q14) show a close relationship (weight coefficient .301). Notably, Q9 and empirical prevention materials (Q11) display a negative correlation with a weight coefficient of -.156. This study's practical significance lies in enabling enterprises to identify key HFE control factors and understand critical sub-factors for mitigating COVID-19's adverse impacts.

Construction accident data mining: A retrospective study using structural equation modeling based on 10-year data

BACKGROUND

The data mining of construction accidents based on a robust modeling process can be used as a practical technique for reducing the frequency of construction accidents.

OBJECTIVE

This study was designed to data-mine construction accidents.

METHODS

This study was conducted in 2020 on construction accidents in Iran for ten years (2009-2018). The instruments to collect the required data were the checklists and descriptive reports of the accidents. The dependent variables of the study included reactive safety indicators related to construction accidents (lost working days (LWD) and total accident costs (TAC)). The independent variables consisted of four latent factors: personal variables, organizational variables, unsafe working conditions, and unsafe acts. The data were collected based on the conceptual model designed for data mining. The data mining process was carried out based on the structural equation modeling by IBM AMOS V. 23.0.

RESULTS

A total of 5742 construction accidents occurring in 10 years were analyzed. The means of TAC and LWD indicators were estimated to be 248.20±52.60 days and 1893.10±152.22 $. These two indicators directly correlated with the two latent factors of unsafe conditions and unsafe acts and their related variables and were indirectly influenced by latent personal and organizational factors. The relationship between unsafe conditions and unsafe acts was significantly positive. The relationship between latent personal and organizational factors and the two construction accident indicators was significantly negative (p <0.05).

CONCLUSION

The model results showed that personal and organizational variables could, directly and indirectly, affect reactive safety indicators in construction projects. Thus, these findings can be used to design and improve safety strategies to prevent and decrease construction accidents and incidents.

Analysis of the Correlation between Occupational Accidents and Economic Factors in China

One of the important factors affecting the production safety of a country or region is the level of economic development. Avoiding accidents under the condition of ensuring economic development is a problem that needs in-depth research. On the basis of collecting the data of occupational accidents and economic development indicators in China from 2000 to 2020, this paper studies the relationship between occupational accidents and five economic indicators, such as resident consumption, energy consumption, education funds, wage level and research input. The grey working accident model of Gaussian function is established, the occurrence trend of occupational accidents is quantitatively analyzed, and the accident reduction measures are suggested based on the relationship between accidents and economy. The results show that there is a strong correlation between accident and economic indicators, and the comprehensive correlation coefficient among scientific research investment, education funds and accident indicators is significantly higher than that of other economic indicators. Increasing investment in scientific research and education is conducive to improving the quality of workers and training safety professionals and can effectively reduce workplace accidents.