Advancing resilience in infrastructure projects through machine learning-driven models
本研究开发了一种神经网络驱动的韧性模型，通过特征重要性技术提升预测准确性和可解释性，帮助管理者更早识别威胁和检测干扰，适用于基础设施项目。
Traditional risk management enhances infrastructure utility but remains limited in addressing complexity and uncertainty. This has shifted attention towards resilience, particularly the readiness dimension, to improve early threat detection and prevention. Machine Learning (ML) offers opportunities to advance resilience modelling, yet empirically validated ML-enabled approaches, especially those using neural networks, are scarce, restricting accuracy, reliability, and applicability. This study develops a neural network-enabled resilience model optimized for training efficiency and predictive performance. By incorporating established feature importance techniques, the model improves accuracy, interpretability, and the identification of influential factors. The findings extend resilience typologies by ranking factor importance in critical infrastructure, highlighting ‘Operational resilience’ as the most significant determinant of project success. Practically, the model provides managers with clearer insights for decision-making, supporting earlier threat recognition and stronger disruption detection. The framework is adaptable across resilience contexts with appropriate industry-or platform-specific modifications.