Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Net characterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.

翻译：社区韧性是一个复杂的多维现象，源于不同社会-技术系统及其韧性属性之间复杂的非线性相互作用。然而，当前关于社区韧性的研究主要关注脆弱性评估，并采用基于指标的方法，在捕捉社区社会-技术系统内的异质性特征以及这些特征在塑造韧性稳健性、冗余性和资源性三个组成部分中的非线性交互作用方面能力有限。为填补这一空白，本文提出了一个用于社区韧性评分的集成三层深度学习模型（称为Resili-Net）。在社区社会-技术系统（即设施、基础设施和社会）中，指定并计算了与稳健性、冗余性和资源性三个韧性组成部分相关的十二个可测量韧性特征。利用美国多个大都市统计区的公开可获取数据，Resili-Net将空间区域的韧性水平划分为五个不同等级。模型结果的可解释性使得能够对每个韧性等级内区域韧性的决定因素进行特征分析，从而识别具体的韧性提升策略。通过改变相关社会-技术系统特征的值，进一步考察了城市发展模式下的社区韧性特征变化。因此，这些结果通过利用机器智能和异质性城市大数据，为社区韧性评估提供了新视角。