A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges

Abstract

The evaluation of a bridge's structural damage state following a seismic event and the decision on whether or not to open it to traffic under the threat of aftershocks (ASs) can significantly benefit from information about the mainshock (MS) earthquake's intensity at the site, the bridge's structural response, and the resulting damage experienced by critical structural components. This paper illustrates a Bayesian network (BN)-based probabilistic framework for updating the AS risk of bridges, allowing integration of such information to reduce the uncertainty in evaluating the risk of bridge failure. Specifically, a BN is developed for describing the probabilistic relationship among various random variables (e.g., earthquakeinduced ground-motion intensity, bridge response parameters, seismic damage, etc.) involved in the seismic damage assessment. This configuration allows users to leverage data observations from seismic stations, structural health monitoring (SHM) sensors and visual inspections (VIs). The framework is applied to a hypothetical bridge in Central Italy exposed to earthquake sequences. The uncertainty reduction in the estimate of the AS damage risk is evaluated by utilising various sources of information. It is shown that the information from accelerometers and VIs can significantly impact bridge damage estimates, thus affecting decision-making under the threat of future ASs.

Probabilistic risk assessment for reinforced concrete frame structures subject to mainshock‐aftershock sequences

“…The results showed that the life-cycle cost tends to increase with the contribution of ASs. In addition, other studies have also explored the AS risk on various structural systems including RC buildings, [37][38][39][40] steel frames, 41 wood structures, 2 containment buildings, 42 bridges, 43,44 and critical infrastructures. 45 All their findings imply that ASs highly affect the structural response and damage condition.…”

Aftershock probabilistic seismic hazard analysis based on enhanced Bayesian network considering frequency information

“…In addressing this issue, Mun and Song (2022) suggested a risk modeling approach that employs BN to consider a sequence of mainshocks and aftershocks. Tubaldi et al (2022) utilized a BN-based probability framework to update the aftershock risk of bridges. However, they did not focus on the following problem, namely, an uncertain mainshock can trigger a series of aftershocks over a long period, which is essentially a frequency problem and contradicts the purely probabilistic nature of BN.…”