Unsupervised learning approach for benchmark models to identify construction projects with high accident risk levels

Purpose: The construction sector is highly prone to accidents, traditionally assessed using subjective qualitative measurements. To enhance the allocation of risk management resources and identify high-risk projects during pre-construction, an objective and quantitative approach is necessary. This study introduces a three-step clustering methodology to quantitatively evaluate accident risk levels in construction projects. Design/methodology/approach: In the first step, accident and total construction revenue by project were collected to calculate accident probabilities. In the second step, accident probabilities were calculated by project type using the data collected in the first step. After that, benchmark models were suggested using clustering methods to identify high-risk project types for risk management. Before suggesting the benchmark models, an uncertainty analysis was conducted due to the limited amount of data. In the third step, the suggested benchmark models were validated for accuracy. Findings: The results categorized risk levels for fatalities and injuries into four distinct groups. Validation through ordinal logistic regression demonstrated high explanatory power, with fatality risk levels ranging from 79.9 to 100% and injury risk levels from 90.3 to 100%. Originality/value: This benchmark model facilitates effective comparisons and analyses across various construction sectors and countries, offering a robust quantitative standard for risk management. By identifying high-risk projects such as “Dam,” this methodology enables better resource allocation during the pre-construction phase, thereby improving overall safety management in the construction industry and providing a basis for legislative applications.