Explainable Safety Risk Management in Construction With Unsupervised Learning

Abstract

The success of Machine Learning (ML) approaches as promising solutions has encouraged their widespread implementation across different fields. Owing to the high accident rate, the construction industry embraced ML in the risk assessment procedure. What if the machine produces knowledge of the relationship between the risk features and accident outcomes contained in the safety dataset? What if machines can explain an accident dataset without human intervention? Unsupervised ML techniques offer several advantages over supervised approaches, including their explainability to analyze and understand complex datasets. This chapter demonstrates the practical implementation of the unsupervised learning method, clustering, and dimensionality reduction to explain the similarities, differences, variances, and patterns that exist between the feature spaces of an occupational safety risk dataset. Principal component analysis (PCA) and K-means clustering with silhouette analysis were selected as two unsupervised ML approaches to demonstrate their implementation in enhancing data-centric decision-making during the construction risk assessment procedure.