From prediction to decision support: explainable machine learning for schedule-delay risk in complex infrastructure projects

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications The framework supports structured managerial prioritisation by translating model outputs into operational risk bands, trigger conditions, responsible actors, intervention timing and measurable outcomes. Social implications 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. Originality/value 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.