This seminar addresses risk management in complex Engineering, Procurement, an‎d Construction/Turnkey (EPC/Turnkey) projects, where effective frameworks are vital for controlling costs, mitigating complexity, an‎d ensuring timely completion. Current research in this domain predominantly relies on subjective, questionnaire-based methodologies, which limits the development of objective an‎d dynamic risk management systems. This reliance creates a significant gap for intelligent, data-driven solutions capable of managing interconnected risks throughout the project lifecycle. While emerging technologies like Reinforcement Learning (RL) show theoretical promise for optimizing such systems, their practical application remains largely conceptual, lacking tailored algorithms an‎d clear integration pathways. Through a seminal review of existing literature, this study identifies this methodological shortcoming an‎d proposes a novel integrated approach to enhance EPC/Turnkey risk models. We introduce a two-pronged methodological advancement: first, employing a reinforcement learning algorithm to optimize decision-making an‎d risk response within the management model; an‎d second, utilizing sentiment analysis an‎d text-mining techniques to extract high-fidelity risk data from project documentation. The proposed hybrid framework is designed for practical assessment an‎d is considered for application to a real-world EPC/Turnkey project in Iraq to eva‎luate its performance an‎d efficacy.

12/29/2025 12:00:00 AM

89231

1404/10/16

RISK MANAGEMENT FRAMEWORK IN TURNKEY CONTRACTS

Risk management , EPC/ Turnkey , Iraq , intelligent model , Proposing the method