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Data-driven estimation of interdependencies and restoration of infrastructure systems

机译：数据驱动的相互依存性估算和基础架构系统恢复

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摘要

Modern urban systems contain intricate interconnected networks whose components depend on each other to operate, provide value, and sustain a functional society. However, this interconnectedness increases the fragility of these systems by allowing the propagation of disruptions through their interdependencies, which may result in large cascades of failures that can cause severe loss of functionality and recovery capability. Furthermore, the resilience of these systems does not only depend on the individual components, but on their combined ability to recover promptly. With the aim of quantifying the interdependence between these systems, this work introduces a new statistical model for evaluating and simulating the restoration of complex interdependent systems, while modeling their restoration as interdependent processes. The statistical model is introduced along with a custom calibration algorithm that fits the model to observed time series data of infrastructure restoration of functionality. Data from six iconic earthquakes are used to fit and test the model against a suite of service restoration curves associated with different infrastructures. It is concluded that the model may be used to simultaneously estimate the restoration and resilience of an infrastructure system after the disruptions caused by a mainshock. Limitations, possible extensions, and improvements of the model are discussed.
机译：现代城市系统包含错综复杂的相互连接的网络，它们的组成部分彼此依赖，以相互操作，提供价值并维持功能性社会。但是，这种互连通过允许中断之间的相互依赖性传播而增加了这些系统的脆弱性，这可能会导致大量故障，从而可能导致功能和恢复能力的严重丧失。此外，这些系统的弹性不仅取决于各个组件，还取决于其迅速恢复的综合能力。为了量化这些系统之间的相互依赖性，这项工作引入了一种新的统计模型，用于评估和模拟复杂的相互依赖系统的恢复，同时将其恢复建模为相互依赖的过程。引入了统计模型以及自定义校准算法，该算法将模型拟合到基础结构恢复功能的观测时间序列数据。来自六次标志性地震的数据用于根据与不同基础架构相关的一组服务恢复曲线来拟合和测试模型。结论是，该模型可用于同时估算主震造成的破坏后基础设施系统的恢复和弹性。讨论了模型的局限性，可能的扩展和改进。

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