Resilience engineering studies the ability of a system to survive and recover from disruptive events, which finds applications in several domains. Most studies emphasize resilience metrics to quantify system performance, whereas recent studies propose statistical modeling approaches to project system recovery time after degradation. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this paper proposes three alternative neural network (NN) approaches including (i) Artificial Neural Networks, (ii) Recurrent Neural Networks, and (iii) Long-Short Term Memory (LSTM) to model and predict system performance, including negative and positive factors driving resilience to quantify the impact of disruptive events and restorative activities. Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared. Our results indicate that NN models outperformed the traditional model on all goodness-of-fit measures. More specifically, LSTMs achieved an over 60\% higher adjusted R squared, and decreased predictive error by 34-fold compared to the traditional method. These results suggest that NN models to predict resilience are both feasible and accurate and may find practical use in many important domains.

翻译：韧性工程研究系统抵御并从破坏性事件中存续与恢复的能力，在多个领域具有应用价值。现有研究多聚焦于通过韧性指标量化系统性能，而近期研究则提出利用统计建模方法预测系统退化后的恢复时间。然而，既有研究或基于恢复后数据进行，或局限于理想化趋势。为此，本文提出三种替代性神经网络方法，包括：(i) 人工神经网络、(ii) 循环神经网络及(iii) 长短期记忆网络，以建模并预测系统性能，涵盖影响韧性的正向与负向因素，进而量化破坏性事件与恢复性活动的影响。通过计算平均平方误差与调整后R²等拟合优度指标，将所提模型与经典统计模型进行对比评估。结果表明，神经网络模型在所有拟合优度指标上均优于传统模型。具体而言，LSTM模型的调整后R²相较传统方法提升超过60%，预测误差降低至传统方法的1/34。这些结果证实，基于神经网络预测韧性的方法兼具可行性与准确性，可在多个重要领域获得实际应用。