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Title: Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks
Abstract

The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city‐scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation‐based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post‐earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing‐based algorithm. Application of the proposed approach to city‐level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to water distribution network managers for identifying economical rehabilitation policies for enhancing the seismic robustness at a city‐scale within a reasonable amount of time.

 
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Award ID(s):
1926792
NSF-PAR ID:
10157179
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Computer-Aided Civil and Infrastructure Engineering
Volume:
35
Issue:
10
ISSN:
1093-9687
Page Range / eLocation ID:
p. 1101-1114
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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