Earthquake and Multi-Hazard Resilience: Community-Level Insights and AI/ML Applications

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1323

Special Issue Editors


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Guest Editor
NIST Center for Risk-Based Community Resilience Planning, Colorado State University, Fort Collins, CO, USA
Interests: earthquake engineering; fragility modeling; multi-hazard simulation; community-scale resilience; AI/ML in hazard modeling

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Guest Editor
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA
Interests: community resilience modeling; earthquake engineering; performance-based design; structural systems; hazard simulation

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Guest Editor
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA
Interests: structural and community resilience; cascading hazards; post-disaster recovery; earthquake performance modeling

Special Issue Information

Dear Colleagues,

The frequency and intensity of natural hazards—including earthquakes, tsunamis, and fire following earthquakes—pose critical challenges to infrastructure and community resilience. In light of these risks, the integration of advanced modeling tools, community-scale simulations, and data-driven approaches has become essential for informed decision-making and risk reduction. Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field by enabling high-resolution fragility modeling, functional recovery analysis, and optimization of mitigation strategies under complex, multi-hazard conditions.

This Special Issue focuses on advancing knowledge and applications in earthquake engineering and multi-hazard resilience, particularly at the community level. Contributions that span structure-, community-, and regional-level analysis are welcome, including those related to the seismic performance of buildings, fragility and loss modeling, post-disaster functionality, and interdependencies across systems.

We especially encourage submissions that leverage AI/ML frameworks, incorporate multi-event sequences, or bridge the gap between structural response and socio-technical resilience planning. The scope of this issue aligns with the journal Infrastructures, which emphasizes integrated, data-informed, and forward-looking research for infrastructure systems under stress from natural and technological hazards.

Topics of interest include, but are not limited to, the following:

  • Earthquake and multi-hazard fragility modeling;
  • Functional recovery and post-disaster dislocation analysis;
  • Cascading and sequential hazard events (e.g., earthquake–tsunami and EQ–fire);
  • AI/ML-enhanced modeling and decision-support systems;
  • Simulation-based optimization for retrofitting strategies;
  • Community- and regional-level risk assessment and resilience planning;
  • Infrastructure interdependencies and system-of-systems modeling;
  • Digital twins and sensing technologies for structural health monitoring;
  • Open-source tools and computational frameworks for resilience;
  • Integration of physical, social, and economic data in hazard-informed design.

We invite original research articles, literature review papers, and case studies that address the above themes. Contributions from interdisciplinary teams and applications to real-world case studies are particularly encouraged.

We look forward to hearing from you.

Dr. Mojtaba Harati
Prof. Dr. John W. van de Lindt
Dr. ‪Maria Koliou‬
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • earthquake engineering
  • multi-hazard resilience
  • AI/ML applications
  • community resilience
  • fragility modeling
  • functional recovery
  • cascading events
  • digital twins
  • post-disaster modeling
  • structural retrofitting

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Published Papers (2 papers)

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Research

25 pages, 3812 KB  
Article
Seismic Vulnerability Assessment and Prioritization of Masonry Railway Tunnels: A Case Study
by Yaser Hosseini, Reza Karami Mohammadi and Tony Y. Yang
Infrastructures 2025, 10(10), 254; https://doi.org/10.3390/infrastructures10100254 - 23 Sep 2025
Viewed by 343
Abstract
Assessing seismic vulnerability and prioritizing railway tunnels for seismic rehabilitation are critical components of railway infrastructure management, especially in seismically active regions. This study focuses on a railway network in Northwest Iran, consisting of 103 old masonry rock tunnels. The vulnerability of these [...] Read more.
Assessing seismic vulnerability and prioritizing railway tunnels for seismic rehabilitation are critical components of railway infrastructure management, especially in seismically active regions. This study focuses on a railway network in Northwest Iran, consisting of 103 old masonry rock tunnels. The vulnerability of these tunnels is evaluated under 12 active faults as seismic sources. Fragility curves derived from the HAZUS methodology estimate the probability of various damage states under seismic intensities, including peak ground acceleration (PGA) and peak ground displacement (PGD). The expected values of the damage states are computed as the damage index (DI) to measure the severity of damage. A normalized prioritization index (NPI) is proposed, considering seismic vulnerability and life cycle damages in tunnel prioritizing. Finally, a detailed prioritization is provided in four classes. The results indicate that 10% of the tunnels are classified as priority, 33% as second priority, 40% as third priority, and 17% as fourth priority. This prioritization is necessary when there are budget limitations and it is not possible to retrofit all tunnels simultaneously. The main contribution of this study is the development of an integrated, data-driven framework for prioritizing the seismic rehabilitation of aging masonry railway tunnels, combining fragility-based vulnerability assessment with life-cycle damage considerations in a high-risk and data-limited region. The framework outlined in this study enables decision-making organizations to efficiently prioritize the tunnels based on vulnerability, which helps to increase seismic resilience. Full article
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24 pages, 4550 KB  
Article
Community-Scale Seismic Vulnerability Assessment of RC Churches: A Simplified Approach for Cultural Infrastructure Resilience
by Giuseppe Brandonisio and Muhammad Tayyab Naqash
Infrastructures 2025, 10(9), 234; https://doi.org/10.3390/infrastructures10090234 - 4 Sep 2025
Viewed by 354
Abstract
This study proposes a simplified, mechanics-based methodology for assessing the seismic vulnerability of reinforced concrete (RC) churches, particularly those with basilica plans and cathedral portal frames such as a repetitive inclined-beam portal frame. The method integrates linear and nonlinear static analyses, plastic limit [...] Read more.
This study proposes a simplified, mechanics-based methodology for assessing the seismic vulnerability of reinforced concrete (RC) churches, particularly those with basilica plans and cathedral portal frames such as a repetitive inclined-beam portal frame. The method integrates linear and nonlinear static analyses, plastic limit theory, and capacity spectrum methods to generate seismic risk indices using minimal input data, making it suitable for large-scale screening in low-data conditions. The model is calibrated using the Cathedral of Reggio Calabria and applied to the Church of San Giovanni Battista dei Fiorentini in Naples. Key outputs include simplified capacity curves and performance indicators. The methodology addresses current limitations in conventional approaches by offering an accessible tool for rapid assessment of cultural infrastructure. Future developments may incorporate AI and machine learning (AI/ML) techniques to improve typological classification and enable automated vulnerability screening at the regional scale. Full article
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