A graph convolution network-deep reinforcement learning model for resilient water distribution network repair decisions

Water distribution networks (WDNs) are critical infrastructure for communities. The dramatic expansion of the WDNs associated with urbanization makes them more vulnerable to high-consequence hazards such as earthquakes, which requires strategies to ensure their resilience. The resilience of a WDN is related to its ability to recover its service after disastrous events. Sound decisions on the repair sequence play a crucial role to ensure a resilient WDN recovery. This paper introduces the development of a graph convolutional neural network-integrated deep reinforcement learning (GCN-DRL) model to support optimal repair decisions to improve WDN resilience after earthquakes. A WDN resilience evaluation framework is first developed, which integrates the dynamic evolution of WDN performance indicators during the post-earthquake recovery process. The WDN performance indicator considers the relative importance of the service nodes and the extent of post-earthquake water needs that are satisfied. In this GCN-DRL model framework, the GCN encodes the information of the WDN. The topology and performance of service nodes (i.e., the degree of water that needs satisfaction) are inputs to the GCN; the outputs of GCN are the reward values (Q-values) corresponding to each repair action, which are fed into the DRL process to select the optimal repair sequence from a large action space to achieve highest system resilience. The GCN-DRL model is demonstrated on a testbed WDN subjected to three earthquake damage scenarios. The performance of the repair decisions by the GCN-DRL model is compared with those by four conventional decision methods. The results show that the recovery sequence by the GCN-DRL model achieved the highest system resilience index values and the fastest recovery of system performance. Besides, by using transfer learning based on a pre-trained model, the GCN-DRL model achieved high computational efficiency in determining the optimal repair sequences under new damage scenarios. This novel GCN-DRL model features robustness and universality to support optimal repair decisions to ensure resilient WDN recovery from earthquake damages.

Automated summary

This paper introduces a graph convolutional neural network-integrated deep reinforcement learning model to support optimal repair decisions for water distribution networks (WDNs) after earthquakes. The model incorporates the dynamic evolution of WDN performance indicators during the post-earthquake recovery process and uses a GCN-DRL framework to select the optimal repair sequence for achieving the highest system resilience. Experimental results show that the model's repair decisions achieved the highest system resilience index values and the fastest system performance recovery.