What question did this study set out to answer?

The aim is to develop and validate mathematical models for predicting occupational accidents using input from near misses.

August 13, 2026Archives of Civil and Mechanical Engineering0 citationsOpen Access

Application of machine learning and deep learning methods for the prediction of near misses and occupational accidents in the construction industry

Key Points

The aim is to develop and validate mathematical models for predicting occupational accidents using input from near misses.
Experimental testing of thirteen machine learning and deep learning algorithms.
Comparison of models including Random Forest, logistic regression, SVC, ARIMA, SARIMAX, and a modified CNN.
Model performance was evaluated using metrics such as accuracy, F1-score, and G-Mean.
The CNNKT model achieved an accuracy of 0.939 and an F1-score of 0.934.
The model's G-Mean was 0.942, showing high effectiveness in predicting accidents.
Findings indicate deep convolutional networks outperformed traditional statistical models.

Abstract

Abstract Occupational accidents remain one of the key challenges for modern work systems, both from the perspective of employee health and life, and in terms of economic, social, and organisational losses for society. Despite the significant diagnostic value of near misses, data on such events have not been incorporated as input variables in predictive models. This study aimed to develop, experimentally test, and empirically validate the effectiveness of various mathematical models in predicting occupational accidents. The performance of thirteen machine learning and deep learning algorithms, including Random Forest, logistic regression, SVC, ARIMA, SARIMAX, and a proposed modified convolutional neural network incorporating Bayesian correction and a tailored ReLU activation function, was compared. The best results were achieved with a CNNKT model, yielding an accuracy of 0.939, an F1-score of 0.934, and a G-Mean of 0.942. The findings confirm the superiority of deep convolutional networks over traditional statistical models in occupational safety analysis.

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