Utilizing Deep Learning for the Extraction of Cost Risk Factors from Project Risk Registers: Enhancing Contingency Estimation

Prioritizing accurate cost contingency estimation through risk identification is essential for the success of construction projects. Traditional methods for identifying and classifying risk factors, such as workshops, interviews, and referencing similar projects, are predominantly manual, subjective, and time-consuming. To overcome these challenges, this study introduces a novel deep learning approach that leverages the BERTopic algorithm to extract cost-related risk factors from extensive project risk registers. The methodology consists of three key steps: (1) identifying risk factor topics; (2) visualizing topics, documents, and terms; and (3) revealing dynamic features of the topics. The effectiveness and practicality of this approach were demonstrated using risk register data from 277 public works projects in Hong Kong, with a comparative analysis against traditional topic modeling techniques, such as latent Dirichlet allocation (LDA) and Top2Vec. This analysis, validated by a panel of project planning experts, successfully identified critical cost-related risk factors, such as design changes, market conditions, project delays, and underground conditions. The findings offer valuable insight for project planners, enabling more effective assessment and prioritization of cost risk factors in future construction projects.