Application of machine learning to predict occurrence of accidents at Finnish construction sites

Construction industry is one of the most hazardous industries due to high frequency of accidents and several safety hazards on the construction site. Frequent safety hazards are caused by the unique, complex and dynamic nature of construction projects, including constantly changing physical environment on construction sites, physically demanding working conditions and continuously evolving construction technology that may relocate or reform the possibility of error rather than downright eliminating it. Efforts to decrease accident frequency and mitigate safety risks on construction sites require enhanced focus on safety culture, practices and equipment as well as proactive safety management while assessing the effects of individual, managerial and environmental factors on safety performance. The use of Artificial Intelligence (AI) to develop diagnostic and predictive models is deemed the next revolutionary advancement for occupational safety in the construction industry. This study is conducted as a case study on one of Finland’s most prominent construction companies, aiming at versatilely utilizing its data to develop a predictive Machine Learning (ML) model capable of predicting the occurrence of accidents. The data includes environmental, managerial and project-specific factors from over 600 construction projects in Finland. Several studies aiming at construction accident prediction with Machine Learning exist, however, majority of the previous studies have lacked access to data of non-accident cases to complement records of accidents. Thus, these studies have focused on, for example, predicting accident severity rather than occurrence of accidents, limiting their contribution to proactive safety-risk management and accident prevention. One of the greatest strengths and contributions of this thesis is its ability to combine data of accident and non-accident cases, thus providing Machine Learning models examples that enable predictive classification of incidents on construction sites. The findings show that while uninterpretable models may have significant predictive skill for this research problem as the KNN model provided the most accurate predictions, interpretable models are desperately needed to increase awareness of factors that are most influential in predicting the occurrence of accidents. While obtaining mostly satisfactory results, none of the models was able to provide excellent results with selected predictor variables, which indicates that more extensive experimentation is required regarding feature selection. Furthermore, formulating accident occurrence prediction as a multiclass classification problem may improve accuracy and applicability of the model