A machine-learning-based framework for contractor selection and order allocation in public construction projects considering sustainability, risk, and safety

Effective contractor selection is crucial for successful execution of construction projects. In contrast to the conventional lowest-bid approach prevalent in the public sector, this study focuses on developing a framework that minimizes time and cost overruns by considering diverse criteria for cont...

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