From prediction to decision support: explainable machine learning for schedule-delay risk in complex infrastructure projects
Schedule delay remains a persistent challenge in large-scale infrastructure programmes, yet many predictive studies prioritise model accuracy while offering limited support for managerial interpretation and decision-making. This study aims to develop and evaluate a case-grounded, explainable hybrid machine-learning framework for schedule-delay risk prediction and decision-support translation. The study integrates random forest and support vector machine within a weighted ensemble, with genetic algorithm optimisation applied to the random forest configuration. Shapley additive explanations is used to interpret model-attributed predictive contributions and identify non-linear risk-state patterns. The framework uses 470 case-grounded, programme-level risk-state observations derived from screened expert assessments and cross-checked against project documentation, practitioner interviews and schedule records. The random forest-genetic algorithm + support vector machine model produced strong predictive results within the studied data set, achieving 92.2% accuracy with balanced precision, recall and F1-score values. The explainability analysis indicates that predicted schedule-delay risk is associated with non-linear, model-attributed risk-state patterns rather than isolated variables. Material and equipment supply, contractor performance and design changes emerged as the most salient predictive signals. These signals represent model-attributed contributions to predicted delay probability, not causal effects. The study is based on a single large-scale infrastructure programme; therefore, broader transferability requires validation across multiple programmes, delivery systems and national contexts. The data are partly expert-informed, although screened and cross-checked against documentary and schedule evidence. SHAP-based explanations are model-attributed and should not be interpreted as causal effects. Future research should examine temporal modelling and benchmarking against XGBoost, LightGBM, CatBoost and deep-learning approaches. The framework supports structured managerial prioritisation by translating model outputs into operational risk bands, trigger conditions, responsible actors, intervention timing and measurable outcomes. By translating delay-risk predictions into explainable and trigger-based decision-support actions, the framework can support more transparent and accountable infrastructure project governance. Earlier identification of schedule-risk conditions may help reduce avoidable delays, resource waste, contractual disputes and disruption to public-service delivery. In large infrastructure projects, improved schedule-risk management can contribute to more reliable delivery of assets that affect communities, economic activity and public-sector investment efficiency. However, the framework should support, not replace, professional judgement and stakeholder accountability. The study contributes by integrating hybrid prediction, explainable artificial intelligence and operational decision-support translation within a case-grounded infrastructure programme context. Rather than treating predictive accuracy or SHAP rankings as final outputs, the framework links predicted delay probability, dominant model-attributed risk signals and operational domains to trigger-based decision support.