A Cloud Model-Based Framework for a Multi-Scale Seismic Robustness Evaluation of Water Supply Networks

This study proposed a cloud model-based framework for assessing the seismic robust-ness of water supply networks (WSN). A multi-scale robustness indicator system was developed, which considers physical-layer attributes (pipe material, length), topological-layer graph characteristics (node degree), and functional-layer hydraulic metrics (water supply adequacy rate). The cloud-probability density evolution method was employed to address parameter uncertainties, while Monte Carlo simulation was used to integrate these three indicators through the cloud composite weighting method to analyze the robustness qualitatively and quantitatively. The proposed method utilizes a forward cloud generator to generate the robustness distribution clouds for both net-work nodes and community-level systems, and its robustness level can be classified according to the standard cloud. A case study demonstrated the practical application of this assessment approach. The presented methodology for evaluating WSN robustness during seismic events provides critical insights for developing disaster prevention plans, formulating emergency response strategies, and implementing targeted seismic reinforcement measures. The integration of cloud theory with probabilistic assessment offers a novel paradigm for infrastructure resilience evaluation under uncertainty.