Seismic resilient three-stage enhancement for gas distribution network using computational optimization algorithms

A computer-simulation-based three-stage optimization strategy is proposed for the resilience enhancement of urban gas distribution networks (GDNs). In stage I (pre-earthquake stage), the Fixed Proportion and Direct Comparison Genetic Algorithm (FPDC-GA) is applied to select key pipelines need to be strenthened or replaced under limited funding in preparation for future potential earthquakes. In stage II (post-earthquake stage), pressure tests must be carried out according to the gas leakage situation reported by users or detected by devices. The Multi-Label K-Nearest-Neighbor (ML-KNN) algorithm is used to predict the corresponding failed pipelines and optimize the pipeline pressure test order. In stage III (repair stage), a strategy based on a greedy algorithm is applied to optimize the pipeline repair sequence. The proposed methods were applied to the GDN of a city in northern China. The following conclusions were drawn from the results: (1) The FPDC-GA enhanced the robustness and resourcefulness of the GDN system to the maximum level within the available funding budget. (2) The pipeline pressure test order calculated using the ML-KNN algorithm was significantly improved compared with a random pressure test order or one based on the empirical failure probability of pipelines. (3) After optimization using a greedy algorithm, the performance recovery curves under different earthquake conditions were shaped as an exponential function, which indicates that the performance of the GDNs recovered in the most efficient manner. The findings of this study could be useful as tools for the seismic resilience enhancement of GDNs in different stages. The proposed optimization algorithms can also be extended to the lifeline of other networks.

Automated summary

The proposed three-stage optimization strategy based on computer simulation enhances the resilience of urban gas distribution networks. The FPDC-GA algorithm improves system robustness and resourcefulness, ML-KNN algorithm optimizes pressure test order, and greedy algorithm enhances the efficiency of pipeline repair.