4.6 Article

Framework for improving the resilience and recovery of transportation networks under geohazard risks

期刊

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijdrr.2018.07.022

关键词

Disaster resilience; Debris clearance; Earthquake-triggered landslides; Graph theory; Resilience strategies; Transportation network

资金

  1. National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme [FI 370074011]
  2. Alexander von Humboldt Foundation Georg Forster Experienced Researcher Fellowship

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Rural transportation networks are highly susceptible to geohazards such as earthquakes and landslides. Indirect losses can be severe because the breakdown of a transportation network aggravates rescue, supply, and other recovery activities. The operations and logistics of rural networks that are under seismic risks must be managed using the limited resources specifically in developing countries. We propose a methodology to evaluate road recovery strategies for restoring connectivity after blockages due to earthquakes and earthquake-triggered landslides. This paper gives insight into the recovery process, which can be used by decision-makers for enhancing resilience and supplying immediate relief to rural areas. The proposed framework has four steps: 1) identification of strategies for increasing recovery performance, 2) determination of graph-based metrics to represent network connectivity, 3) applying topology-based and Monte Carlo simulations to each strategy, and 4) analysis of recovery times to compare these resilience-enhancement strategies. The methodology was tested using a case study from Sindhupalchok District, Nepal, a region that was severely affected by the Gorkha earthquake in 2015. The closed road segments and recovery times were determined through field surveys with locals and governmental authorities, and by investigating the intensity of earthquake-triggered landslides. Our results showed that the proposed approach provides information about the recovery behavior of road networks and simplifies the evaluation process. It is robust enough to extend and assess decision-makers' preferences for improving resilience.

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