Real-time Seismic Emergency Resilience Assessment and Decision-making for Transportation Networks using Deep Learning Techniques

DOI

10.2991/978-94-6463-856-1_8How to use a DOI?

Keywords

transportation network; seismic resilience; rapid assessment; deep learning; decision-making

Abstract

Seismic resilience assessment of transportation networks is essential for effective emergency response and infrastructure management. However, traditional Monte Carlo simulation-based methods are computationally intensive, making them impractical for real-time decision-making. To address that problem, this study employs deep learning techniques as a surrogate model to accelerate seismic resilience assessment and enhance decision-making processes. The proposed deep neural network integrates key factors, including regional seismic hazards, the seismic performance of roads, network topology, and traffic demand, to improve prediction accuracy and reliability. To demonstrate its effectiveness in delivering rapid and reliable assessments, the developed model is applied to the transportation network in Colorado, USA. Results show that the deep learning-based approach significantly reduces computational costs while maintaining high accuracy, making it a practical alternative to traditional simulation techniques. Furthermore, based on the outcomes, emergency resilience enhancement strategies are proposed to improve the robustness of transportation networks against seismic events. This study provides critical insights into seismic emergency resilience assessment and contributes to the advancement of data-driven decision-making frameworks for disaster management.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaotian Guan
AU  - Xinru Ran
AU  - Zhenliang Liu
AU  - Weigang Zhao
PY  - 2025
DA  - 2025/09/22
TI  - Real-time Seismic Emergency Resilience Assessment and Decision-making for Transportation Networks using Deep Learning Techniques
BT  - Proceedings of the 2025 International Conference on Resilient City and Safety Engineering (ICRCSE 2025)
PB  - Atlantis Press
SP  - 70
EP  - 79
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-856-1_8
DO  - 10.2991/978-94-6463-856-1_8
ID  - Guan2025
ER  -