Integrated Fuzzy Technique for Order Preference by Similarity to Ideal Solution and Emotional Artificial Neural Network Model for Comprehensive Risk Prioritization in Green Construction Projects

Abstract

With the rapid growth of green construction projects (GCPs) in Saudi Arabia, managing the associated risks has become crucial to ensuring project success and sustainability. These projects face a range of challenges, including socio-economic, environmental, and technical risks that need to be carefully identified and prioritized. This study systematically identifies and prioritizes the risks in GCP using a hybrid model combining fuzzy TOPSIS and an Emotional Artificial Neural Network (EANN). The focus of this study is on the risk management of the green construction industry in Saudi Arabia. Based on expert evaluations, low-quality materials and equipment (Likert scale mean is 4.71) and stakeholder resistance to adopting green ideas (4.67) emerged as the most critical risks. The fuzzy TOPSIS analysis assigned the highest weight to risk probability (0.174), followed by outcome (0.137), and vulnerability (0.123). The EANN refined the risk rankings, confirming the importance of these risks. The findings suggest that risk management strategies should prioritize material quality and stakeholder engagement, while environmental risks, ranked lower, are less of a concern. This hybrid model provides a robust framework for effective risk management, with practical implications for enhancing the sustainability and success of GCP.