Özet:

Abstract Construction projects in Iraq are vulnerable to a variety of risks, each one of which has the potential to affect the project's schedule, finances, or results. It is absolutely necessary to carry out precise risk and cost assessment in order to reduce the impact of these hazards. This research article investigates the application of machine learning, the risk impact index (RII), the Spearman's rank correlation coefficient, and the Delphi approach for estimating risk in Iraq. In addition to that, the research delves into other approaches to cost estimating, such as parametric estimation and bottom-up estimation. It is demonstrated via the use of a case study how successful these strategies are in enhancing the final results of the project. This research aims to estimate construction project risks in Iraq using the Delphi technique, RII, Spearman's rank correlation coefficient, and machine learning. The research will provide valuable insights into the most significant risk factors in construction projects in Iraq and inform risk management strategies for future projects. The methodology proposed in this research can be adapted for use in other developing countries with similar construction industries. By improving the accuracy and efficiency of risk estimation, this research can contribute to the successful completion of construction projects and the development of infrastructure in Iraq and other developing countries. The research comes to the conclusion that accurate risk and cost estimation are essential to the success of construction projects in Iraq. The techniques that were discussed can assist project teams in the development of realistic budgets and schedules, as well as effective risk management strategies.

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