Robustness assessment of Muscat coastal highway network (CHN) under multi-hazard scenarios focusing on traffic stability and adaptation measures

This study critically examines the reliability and resilience of the Muscat coastal highway network (CHN) under the compounded effects of earthquakes and floods, representing interacting multi-hazard scenarios. The analysis utilized fragility functions for both earthquake-induced and flood-induced landslides, integrating these with traffic data for selected highway links to estimate bridge damage and assess CHN functionality in post-hazard conditions. Economic sensitivity analysis revealed a significant increase in costs due to flood-induced landslides, emphasizing the impact of dominant intensity measures on network costs and traffic flow. The analysis categorized Muscat areas into low, moderate, and high resilience based on hazard susceptibility and infrastructure quality, revealing that over 50% of highway links require retrofitting, highlighting the need for enhanced flood management and infrastructure improvements. The resilience assessment highlighted the necessity for targeted retrofitting to mitigate damage and reduce economic losses, particularly for highway links with bridges of high failure probabilities that face prolonged recovery times. The results provide valuable insights for designers, consultants, policymakers, and decision-makers in developing effective post-hazard mitigation strategies for Muscat and similar coastal cities.

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Publication Details

Journal
Scientific Reports
Published
2024-12-24
DOI
https://doi.org/10.1038/s41598-024-79730-3
Citations
14
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
3.65

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article

Robustness assessment of Muscat coastal highway network (CHN) under multi-hazard scenarios focusing on traffic stability and adaptation measures

Issa El-Hussain, Abdullah Ansari, Khalifa Saif Al-Jabri, Ayed Eid Alluqmani et al.
14 citations
Scientific Reports
Infrastructure Resilience and Vulnerability Analysis
3.65
article

Robustness assessment of Muscat coastal highway network (CHN) under multi-hazard scenarios focusing on traffic stability and adaptation measures

Issa El-Hussain, Abdullah Ansari, Khalifa Saif Al-Jabri, Ayed Eid Alluqmani, Pranjal Mandhaniya, Yousuf Al Shijbi
article en
14 citations

Abstract

This study critically examines the reliability and resilience of the Muscat coastal highway network (CHN) under the compounded effects of earthquakes and floods, representing interacting multi-hazard scenarios. The analysis utilized fragility functions for both earthquake-induced and flood-induced landslides, integrating these with traffic data for selected highway links to estimate bridge damage and assess CHN functionality in post-hazard conditions. Economic sensitivity analysis revealed a significant increase in costs due to flood-induced landslides, emphasizing the impact of dominant intensity measures on network costs and traffic flow. The analysis categorized Muscat areas into low, moderate, and high resilience based on hazard susceptibility and infrastructure quality, revealing that over 50% of highway links require retrofitting, highlighting the need for enhanced flood management and infrastructure improvements. The resilience assessment highlighted the necessity for targeted retrofitting to mitigate damage and reduce economic losses, particularly for highway links with bridges of high failure probabilities that face prolonged recovery times. The results provide valuable insights for designers, consultants, policymakers, and decision-makers in developing effective post-hazard mitigation strategies for Muscat and similar coastal cities.

Scientific ReportsVol. 14(1)
Norwegian University of Science and Technology (NO), Islamic University of Madinah (SA), Sultan Qaboos University (OM)
Norges Teknisk-Naturvitenskapelige Universitet, Inha University, Sultan Qaboos University, St. Olavs Hospital Universitetssykehuset i Trondheim
Openalex Percentile: Top 7%
Infrastructure Resilience and Vulnerability Analysis
3.65
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