In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.

中文翻译：

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增强电网抗洪灾能力的决策支持系统

在美国的不同地区，有电线杆和其他关键基础设施容易受到洪水的破坏。这项多学科研究的目标是通过嵌入独特的混合整数线性规划 (MILP) 优化框架的传统水文、流体静力和岩土计算来评估和最小化电线杆故障的可能性。一旦确定了导致电线杆倾覆的流速，就可以创建最具成本效益的地下管网配置，从而允许洪水从易受攻击的基础设施元件重新引导。使用 Julia 科学编程语言模拟优化框架，考虑到整个网络的众多决策变量和约束，使用 JuMP 接口和 Gurobi 求解器包来求解最小成本网络流目标函数。我们在美国三个不同的流域实施了我们的优化框架。这些流域位于北卡罗来纳州惠蒂尔附近；科罗拉多州莱德维尔；和伦敦，AR。在这些流域内实施最小成本网络流量优化模型产生的结果表明，可以将必要数量的洪水从电线杆上输送出去，以防止洪水造成故障。