Descriptive and Prescriptive Analysis of Construction Site Incidents Using Decision Tree Classification and Association Rule Mining

Abstract

Learning from previous incidents is essential for preventing future incidents and taking the necessary precautions. We analyze construction site incidents by employing data science process and machine learning algorithms such as decision trees and Apriori. Patterns that are extracted using machine learning algorithms provides interesting insights on the causes of incidents and their relations with other factors. The dataset we use is a novel dataset containing hundreds of construction site incidents between 2014 and 2020 from an international construction company. The data consist of a wide range of features such as activity during incident, incident condition, hazard source, incident severity, location, and time. The decision tree is used in a descriptive analytics setting to extract patterns in our dataset. Additionally, the Apriori algorithm is employed to extract patterns in the form of frequent itemsets and association rules. The patterns we extract using machine learning algorithms shed light on associations between different factors and different types of incidents. One of the interesting results of our study is that the patterns extracted from a supervised classifier in a descriptive analytics setting collides with the patterns extracted using the unsupervised machine learning algorithm of Apriori. The generated rules can be used for informing the health and safety experts by developing a decision support mechanism for taking necessary precautions for minimizing the risk of different types of incidents.