Strategic Management and Seismic Resilience Enhancement of Water Distribution Network Using Artificial Neural Network Model
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 16, Issue 1
Abstract
The seismic resilience of water distribution networks (WDNs) as the most critical urban infrastructure is a major aspect of crisis management. Developing accurate computational models to evaluate the seismic behavior of WDNs and enhance seismic resilience remains a major research challenge. This paper introduces a novel model to evaluate the seismic vulnerability of WDNs through an artificial neural network (ANN) approach. To generate initial data to develop the seismic damage prediction model, extensive numerical modeling was performed using commercially available software to extract the strain behavior of buried pipes under seismic loading. A total of 720 numerical simulations were performed. The numerical model was validated using an experimental model. The results showed that the numerical model had an error smaller than 10% in evaluating the strain behavior of buried pipes, suggesting satisfactory model performance. This paper used a multilayer perceptron (MLP) and the Levenberg–Marquardt algorithm (LMA) because it has shown good performance in regression applications. The sensitivity of the proposed model to the number of hidden layers was analyzed, and the MLP with 15 hidden layers was found to be optimal in predicting the strain of the buried pipe under seismic loading, with a mean squared error (MSE) of 0.301 and a correlation coefficient . The proposed seismic damage prediction model was executed on the WDN of Tehran, Iran, based on the initial data set under five seismic loading scenarios, calculating the numbers of breaks and leaks. The seismic resilience of the WDN was evaluated using damage, minimum water demand, and restoration time indices. Several strategies were proposed to enhance the seismic resilience of WDNs. The developed seismic resilience assessment model in this study has the capability to be applied and implemented across other WDNs.
Practical Applications
The water distribution network is critical for urban resilience, and evaluating its seismic vulnerability is essential for managing earthquake impacts, particularly in ensuring postearthquake water supply and firefighting capabilities. This study introduces a novel ANN model designed to assess seismic resilience in urban water distribution networks. The model employs advanced computational techniques to comprehensively evaluate resilience and propose enhancement strategies. A case study conducted in Tehran, Iran, validated the model’s accuracy in assessing earthquake damage and resilience strategies specific to the water distribution network. The results demonstrate the model’s effectiveness in improving urban infrastructure performance during seismic events. The seismic vulnerability prediction model and seismic resilience analysis of the water distribution network presented in this study can serve as a comprehensive tool for urban stakeholders and planners. This model is adaptable and can be applied to assess vulnerability and enhance resilience in various urban water distribution networks.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank Tehran Province Water and Wastewater Company for providing data for the research.
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Information & Authors
Information
Published In
Journal of Pipeline Systems Engineering and Practice
Volume 16 • Issue 1 • February 2025
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 30, 2023
Accepted: Jun 10, 2024
Published online: Sep 26, 2024
Published in print: Feb 1, 2025
Discussion open until: Feb 26, 2025
ASCE Technical Topics:
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Buried pipes
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Dynamic loads
- Dynamics (solid mechanics)
- Earthquake engineering
- Engineering fundamentals
- Engineering mechanics
- Geotechnical engineering
- Infrastructure
- Infrastructure resilience
- Models (by type)
- Neural networks
- Numerical models
- Pipeline systems
- Pipes
- Seismic effects
- Seismic loads
- Seismic tests
- Solid mechanics
- Structural dynamics
- Structural engineering
- Tests (by type)
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