Automated Hazard Identification and Visualisation in Design Using Building Information Modelling and Machine Learning

Abstract

The construction industry is recognised globally as one of the most hazardous sectors. Effective hazard management necessitates identifying and communicating these risks early in the project lifecycle. Construction Hazard Prevention through Design (CHPtD) addresses this by incorporating safety information into the design phase that is often cumbersome and heavily reliant on reviewer expertise. The present work enhances hazard recognition and visualisation by automating the process using computational intelligence and building information modelling, aligning with the theoretical framework of CHPtD. The proposed tool provides detailed hazard information, including the nature of the hazard, its causes, and potential resolutions, empowering designers to make informed decisions and mitigate risks proactively. The tool’s performance is evaluated using a confusion matrix, demonstrating promising results with an overall accuracy of 84.77% and a Kappa coefficient of 0.83. While the tool shows strong performance in identifying several hazard classes, further refinement is needed to improve its ability to detect catastrophic events and manage traffic-related hazards.