Abstract
Risk Assessments in construction projects help in cost savings, energy efficiency, as well as on time completion within the allocated budget. To meet these requirements, companies need to use analytical models that save time, deal with large amounts of data, and allow them to make better decisions more quickly. In addition, it will facilitate a fast, accurate, and automated process. This paper aims to provide a comprehensive critical analysis of traditional and AI -based risk assessment frameworks for sustainable construction projects (SCP) and the most practical framework. In this respect, the paper reviewed the literature and conducted semistructured interviews on risk assessment and its application in construction projects. It also explored the advantages and disadvantages of traditional and AI -based risk assessments. A case study of modern sustainable construction projects in a selected country (Iraq) was undertaken to assess risk factors associated with these projects. An Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANNs) approaches were used to reveal the variation of results between traditional and AIbased risk assessment regarding accuracy, reliability, and cost-effectiveness. An independent sample t -test was calculated to verify the differences in the results obtained by the AHP and the ANN methods. The findings show the advantages and disadvantages of traditional and advanced risk assessment in sustainable construction projects and establish recommendations for optimum practice in risk assessment methodologies. Also, the findings indicate that the value of the t -test was 0.7415, which is greater than 0.05; this means there is no substantial difference in the results of assessing the risk factors that impact sustainable construction projects if AHP or ANNs methods are used. The paper concludes that while traditional frameworks are still prevalent in the construction industry, advanced techniques can improve accuracy, reliability, and cost-effectiveness. The implications for sustainable construction practice and policy and identifies future research directions.