New Publication: A Dynamic Bayesian Network Approach to Characterize Multi-Hazard Risks and Resilience in Interconnected Critical Infrastructures.

Abstract

A new paradigm for risk assessment has emerged, recognizing the escalating frequency and severity of disasters associated with natural hazards. Conventional risk assessments often fail to capture the dynamic and interconnected nature of disruptions within infrastructure systems during failure scenarios. This study introduces a Dynamic Bayesian Network (DBN) framework, designed to assess risk in interconnected infrastructure systems under complex hazard scenarios. The framework addresses the limitations of static models by dynamically capturing the progression of disruptions during failure and the restoration process during recovery. Using a case study in Saint Lucia, a Caribbean Island susceptible to natural hazards, this study examines the complex network of critical infrastructure. The DBN framework explores various failure scenarios, highlighting the cascading effects across infrastructure sectors, and captures the probabilistic hazard conditions and functional dynamics during disruption and restoration processes. Results from the case study illuminate the heightened vulnerability of the international airport and tourism sectors, emphasizing the interdependencies and propagation of failures within the infrastructure system. By investigating failure scenarios, the DBN approach characterizes the complex interactions between infrastructure systems, providing valuable insights into how multi-hazard events affect interconnected networks. These findings underscore the critical need for dynamic, real-time risk assessments that consider both short-term disruptions and long-term recovery processes. The study highlights the urgency of embracing dynamic risk assessment methodologies and offers a foundation for developing adaptive, multi-hazard risk assessment strategies to enhance the resilience of critical infrastructure networks.

Citation: Bakhtiari, S., Najafi, M. R., Goda, K., & Peerhossaini, H. (2025). A Dynamic Bayesian Network Approach to Characterize Multi-Hazard Risks and Resilience in Interconnected Critical Infrastructures. Reliability Engineering & System Safety, 1–18. https://doi.org/10.1016/j.ress.2025.110815

You can find the article in Reliability Engineering & System Safety Journal

List of Publications from the CIRCLE Projects Across Various Study Areas

New Publication: Effect of Calibration Data on Performance of Tsunami Early Warning Model

Abstract

Data-driven tsunami early warning systems can be calibrated using possible wave profiles that are simulated from numerous hypothetical rupture scenarios. However, tsunami wave profiles that are simulated based on a certain synthesis method may not capture future situations comprehensively. To quantify the effects of calibration datasets on tsunami early warning models, a case study focusing on Vancouver Island that faces major tsunami threats from the Cascadia subduction earthquakes is explored. Two tsunami wave databases are generated by considering a logic tree model of potential tsunami sources for probabilistic tsunami hazard analysis and stochastic rupture sources with variable geometry and heterogeneous slip distribution. Tsunami early warning models are developed based on three fitting methods, namely, multiple linear regression, random forest, and neural network. Using consistent and inconsistent training-testing (calibration-evaluation) datasets, performances of the tsunami early warning models are compared. The results of the comparative analyses indicate that the use of random forest and neural network outperform conventional multiple linear regression methods. The effects of calibration data on the model performance are significant and may not be captured well by a conventional cross-validation scheme. This study highlights the importance of epistemic uncertainty of the tsunami early warning model performance.

Citation: Goda, K., Chamatidis, I., & Istrati, D. (2025). Effect of calibration data on performance of tsunami early warning model. Coastal Engineering Journal, 1–18. https://doi.org/10.1080/21664250.2025.2516324

You can find the article in Coastal Engineering Journal OR request via researchgate.net

List of Publications from the CIRCLE Projects Across Various Study Areas

New Publication: Quick Loss Estimation Tool (QLET) for Seismic Risk Assessment in Canada

A new peer-reviewed article has been published in GeoHazards titled:

“Rapid Computation of Seismic Loss Curves for Canadian Buildings Using Tail Approximation Method”
Authors: Payam Momeni, Katsuichiro Goda, Navid Sirous, and Sheri Molnar

Traditional seismic risk assessments often require specialized expertise and extensive computational time, making probabilistic seismic risk evaluations less accessible to practitioners and decision-makers. To reduce the barriers related to applications of quantitative seismic risk analysis, this paper develops a Quick Loss Estimation Tool (QLET) designed for rapid seismic risk assessment of Canadian buildings.

By approximating the upper tail of a seismic hazard curve using an extreme value distribution and integrating it with building exposure-vulnerability models, the QLET enables efficient computation of seismic loss curves for individual sites. The tool generates seismic loss exceedance probability curves and financial risk metrics based on Monte Carlo simulations, offering customizable risk assessments for various building types.

The QLET also incorporates regional site proxy models based on average shear-wave velocity in the uppermost 30 m to enhance site-specific hazard characterization, addressing key limitations of global site proxy models and enabling risk-based seismic microzonation. The QLET streamlines hazard, exposure, and vulnerability assessments into a user-friendly tool, facilitating regional-scale risk evaluations within practical timeframes, making it particularly applicable to emergency preparedness, urban planning, and insurance analysis.

QLET is freely available as an open-source repository on GitHub, providing both the MATLAB codes and GUI-based application for users and researchers:
https://github.com/pmomeni/QuickLossEstimationTool_QLET

Read the full article:
https://www.mdpi.com/2624-795X/6/2/26

New Release: Comprehensive Textbook on Probabilistic Tsunami Hazard and Risk Analysis

Dr. Katsuichiro Goda led the editing of the first textbook on 'Probabilistic Tsunami Hazard and Risk Analysis', published by Elsevier. More information can be found at: Elsevier website