Machine Learning Construction Cost, Delay and Resource Prediction: A Literature Review Toward Opportunity-Cost-Aware Decision Support

Construction projects routinely suffer from cost overruns, schedule delays and inefficient allocation of scarce resources across competing projects. This paper reviews ten studies covering machine-learning cost estimation, explainable cost prediction, BIM-integrated prediction, cost forecasting, delay-risk and duration prediction, AI-assisted resource allocation, and decision support for project portfolio selection. For each study, the contribution, the identified limitation and its relevance to a proposed predictive opportunity-cost decision dashboard are discussed. The review shows that predictive models for cost and delay have matured, whereas resource-allocation and portfolio methods operate largely without learned predictions. A gap is therefore identified between project-level prediction and multi-project, opportunity-cost-aware decision making, and a set of requirements covering explainable prediction, resource constraints, standardised data, opportunity-cost estimation and decision-oriented presentation is derived.