Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model

Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards can only be predicted to some extent, their impact can be managed by increasing the emergency response and reducing the vulnerability of infrastructure. In the context of risk management, the ability of infrastructure to withstand damage and re-establish their initial condition has recently gained prominence. Several resilience strategies have been investigated by numerous scholars to reduce disaster risk and evaluate the recovery time following disastrous events. A key parameter to quantify the seismic resilience of infrastructures is the Downtime (DT). Generally, DT assessment is challenging due to the parameters involved in the process. Such parameters are highly uncertain and therefore cannot be treated in a deterministic manner. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate the DT of selected infrastructure types following earthquakes. To demonstrate the applicability of the methodology, three scenarios are performed. Results show that the methodology is capable of providing good estimates of infrastructure DT despite the uncertainty of the parameters. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes in immediate post-event applications as well as to promptly recover damaged infrastructure.

Automated summary

The resilience of infrastructure is crucial in reducing disaster risk and evaluating recovery time after catastrophic events, with Downtime (DT) being a key parameter to measure infrastructural seismic resilience. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate infrastructure DT post-earthquake, with three scenarios demonstrating the methodology's effectiveness despite uncertain parameters. The methodology can support decision-makers in managing and minimizing earthquake impacts, as well as in promptly recovering damaged infrastructure.