Seismic Resilience Evaluation of Urban Water Distribution Network using Machine Learning
Subject Areas : Farm water management with the aim of improving irrigation management indicatorsMahnaz Haghighi 1 , Ali Delnavaz 2 , Majid Safehian 3 , Mohammad Delnavaz 4
1 - Ph.D. Candidate in Construction Engineering and Management, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant professor 'Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
3 - Assistant professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Associate professor "Faculty of Engineering, Civil Engineering Department, Kharazmi University, Tehran, Iran.
Keywords: Seismic vulnerability, Seismic resilience, Machine learning, Water distribution network,
Abstract :
Background and Aim: Seismic resilience focuses on the performance, recovery, and functionality of urban infrastructures after earthquakes, with water supply networks being critical for providing drinking water and firefighting support. Assessing and enhancing the seismic resilience of these networks is a key challenge for researchers. This study evaluates the seismic resilience of urban water distribution, specifically focusing on Tehran's water network in Districts 1-4.
Method: This research introduces a machine learning-based model to predict seismic vulnerability and evaluate the resilience of urban water supply networks. Machine learning models are based on primary data sets, so to provide the primary database initially, finite element modeling (720 scenarios) using Abaqus software was conducted to extract the seismic behavior of buried pipes. Various machine learning algorithms were then applied to develop a predictive model, which was optimized and paired with a numerical algorithm to assess damage under uncertainty using the Monte Carlo method. The model was tested on five seismic scenarios in Tehran’s Districts 1-4, extracting results on pipe damage, and seismic resilience was evaluated based on damage, water supply needs, and recovery time.
Results: The results obtained from the performance of the machine learning model using various algorithms demonstrated that Gaussian algorithms performed better compared to other computational machine learning algorithms. Specifically, the Rational Quadratic Gaussian algorithm exhibited minimal error, with a mean squared error (MSE) of 3.0564 and a correlation coefficient (R) of 0.94. For the purpose of assessing seismic resilience of the water transmission and distribution network in Tehran's areas 1 to 4, the extent of damage to the water supply network (number and location of pipe breaks/leaks in the network) under 5 seismic loading scenarios was calculated and extracted using the vulnerability prediction model. Additionally, an assessment of the relative damage index (Bridge index) in the pipeline network under study was conducted. In evaluating the minimum required water supply considering the population of each area and the required water quantity, calculations were made and assessments were carried out. The recovery time/recovery rate index of the water distribution network following an earthquake event, based on the level of pipe damage in each area and repair and restoration strategies, was studied. Based on the results obtained, it is evident that the longest recovery time of the system was for area 4 of Tehran under S1 loading (earthquake magnitude of 7.4 Richter), amounting to 493.5 hours, while the shortest recovery time was for area 3 of Tehran under S2 loading (earthquake magnitude of 6.2 Richter), amounting to 316.5 hours. The results indicate that the water supply network under scenarios with larger earthquake magnitudes experienced longer recovery times compared to scenarios with smaller earthquake magnitudes.
Conclusion: The results demonstrated that the machine learning model for predicting seismic damage in the water transmission network had acceptable accuracy in assessing damage under incident conditions. The main points and solutions proposed in this study towards enhancing seismic resilience of the water supply network include: accurate prediction of seismic damage levels in the water transmission network using the proposed model (for precision and improvement of vulnerability indices), making precise management decisions to secure alternative water resources in high earthquake risk areas (to enhance augmentation indices), and implementing repair and restoration strategies for damaged pipelines by equipping areas with skilled support teams (to improve recovery time indices). Based on the obtained results, the model presented in this study can be utilized as a comprehensive model for various regions and under different seismic loading conditions. In addition to estimating seismic vulnerability of the water supply network, it can provide a detailed assessment of seismic resilience and enhance the flexibility of this critical infrastructure.
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