Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims to construct a joint probability distribution model to describe the post-hazard state of infrastructure networks and propose an efficient surrogate model of the joint distribution for large-scale systems. Specifically, we present maximum entropy modeling of the regional impact of natural hazards on civil infrastructures. Provided with the current state of knowledge, the principle of maximum entropy yields the ``most unbiased`` joint distribution model for the performances of infrastructures. In the general form, the model can handle multivariate performance states and higher-order correlations. In a particular yet typical scenario of binary performance state variables with knowledge of their mean and pairwise correlation, the joint distribution reduces to the Ising model in statistical physics. In this context, we propose using a dichotomized Gaussian model as an efficient surrogate for the maximum entropy model, facilitating the application to large systems. Using the proposed method, we investigate the seismic collective behavior of a large-scale road network (with 8,694 nodes and 26,964 links) in San Francisco, showcasing the non-trivial collective behaviors of infrastructure systems.

翻译：受自然灾害扰动，群落级基础设施网络的行为类似于多体系统，其涌现行为源于将个体组件动力学与群体关联和相互作用相结合。因此，我们可以借鉴统计物理方法研究基础设施系统对自然灾害的响应。本研究旨在构建描述灾后基础设施网络状态的联合概率分布模型，并提出适用于大规模系统的联合分布高效替代模型。具体而言，我们提出了基于最大熵的自然灾害对民用基础设施区域影响建模方法。在现有知识状态下，最大熵原理可得出基础设施性能的"最无偏"联合分布模型。该通用形式能处理多变量性能状态和高阶相关性。在二元性能状态变量已知其均值和成对相关性的特殊典型场景中，联合分布简化为统计物理中的伊辛模型。为此，我们提出采用二分高斯模型作为最大熵模型的高效替代，便于在大型系统中应用。通过所提方法研究了旧金山大规模道路网络（含8694个节点和26964条链路）的地震集体行为，揭示了基础设施系统具有非平凡集体特性的现象。