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

Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types

Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activities, and tools/materials as input features, through the application of machine learning-based multi-class classification algorithms. 152,867 construction accident summary reports, composed of both structured (construction task, construction activity, accident type) and unstructured data (tools/materials) were used for the study. The study employed several data processing techniques, including keyword extraction through text mining, Boruta feature selection, and SMOTE data resampling enhance model accuracy. Three performance metrics (Multi-class area under the receiver operating characteristic curve (MAUC), Multi-class Matthews Correlation Coefficient (MMCC), Geometric-mean (G-mean)) were used to compare the predictive performance of four machine learning algorithms, including Decision tree, Random forest, Naïve bayes, and XGBoost. Of the four algorithms, XGBoost showed the highest performance in predicting accident type (MAUC: 0.8603, MMCC: 0.3523, G-mean: 0.5009). Furthermore, a Shapley additive explanation (SHAP) analysis was conducted to visualize feature importance. The findings of this study make a valuable contribution to improving construction safety by presenting a prediction model for accident types derived from real-world big data.

Fatal occupational injuries in North Carolina, 1992-2017

OBJECTIVES

After declining for several decades, fatal occupational injury rates have stagnated in the USA since 2009. To revive advancements in workplace safety, interventions targeting at-risk worker groups must be implemented. Our study aims to identify these at-risk populations by evaluating disparities in unintentional occupational fatalities occurring in North Carolina (NC) from 1992 to 2017.

METHODS

Our retrospective cohort study drew on both the NC Office of the Chief Medical Examiner system and the NC death certificate data system to identify unintentional fatal occupational injuries occurring from 1992 to 2017. Unintentional fatal occupational injury rates were reported across industries, occupations and demographic groups, and rate ratios were calculated to assess disparities.

RESULTS

Among those aged 18 and older, 2645 unintentional fatal occupational injuries were identified. Fatal occupational injury rates declined by 0.82 injuries/100 000 person-years over this period, falling consistently from 2004 to 2009 and increasing from 2009 to 2017. Fatal injury rates were highest among Hispanic workers, who experienced 2.75 times the fatal injury rate of non-Hispanic White workers (95% CI 2.42 to 3.11) and self-employed workers, who experienced 1.44 times the fatal injury rate of private workers (95% CI 1.29 to 1.60). We also observed that fatal injury rates increased with age group and were higher among male relative to female workers even after adjustment for differential distributions across occupations.

CONCLUSIONS

The decline in unintentional fatal occupational injury rates over this period is encouraging, but the increase in injury rate after 2009 and the large disparities between occupations, industries and demographic groups highlight the need for additional targeted safety interventions.

Behavior Choice Mechanisms and Tax Incentive Mechanisms in the Game of Construction Safety

The violation behavior of construction workers is an important cause of construction accidents. To reduce the violations of construction workers and to stimulate the supervision behavior of local governments and construction enterprises, an evolutionary game model is constructed in this paper. Then, the behavior choice mechanism of each player is analyzed. Finally, an incentive effect analysis method is put forward, and the incentive effects of different tax incentive mechanisms are analyzed. This research finds that only when the safety punishment imposed on construction workers is large enough does the supervision behavior of local governments and construction enterprises encourage construction workers to choose not to violate the regulation. Increasing the tax rate of a construction enterprise in the case of accidents can encourage the construction enterprise to supervise, but it inhibits the supervision behavior of the local government. A numerical simulation verifies the effectiveness of the incentive effect analysis method, which provides a new method for the incentive effect analysis of incentive mechanisms.