Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II: Application

Abstract

The Bridge Reliability in Networks (BRAN) methodology introduced in the companion paper is applied to evaluate the reliability of part of the highway bridge network in South Carolina, USA, under a selected seismic scenario. The case study demonstrates Bayesian updating of deterioration parameters across bridges after spatial interpolation of data acquired from limited instrumented bridges. The updated deterioration parameters inform aging bridge seismic fragility curves through multidimensional integration of parameterized fragility models, which are utilized to derive bridge failure probabilities. The paper establishes the correlation structure among bridge failures from three information sources to generate realizations of bridge failures for network level reliability assessment by Monte Carlo analysis.Positive correlations improve the reliability of the case study network, also predicted from the network topology. The benefits of the BRAN methodology are highlighted in its applicability to large networks while addressing some of the existing gaps in bridge network reliability studies.

Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges

“…Although such simplistic assumptions may aid in the ease of deterioration modeling and subsequent fragility analysis using finite element bridge models, it may lead to significant under-predictions of aging bridge failure probabilities. These underestimation of seismic fragility at bridge level are also likely to be propagated during life-cycle analysis of deteriorating bridges [17,67] as well as in reliability estimation of aging bridge transportation networks [40,59].…”

Resilience-based risk mitigation for road networks

“…Ghosh et al [16] presented a two-stage reliability assessment framework for aging bridge networks, including seismic fragilities of individual bridges and correlations among them, and further estimated the network reliability by a revised Markov Chain Monte Carlo simulation. The proposed method is illustrated on part of the highway bridge network in South California, USA [36].…”

Seismic Fragilities of Curved Concrete Bridges via Bayesian Parameter Estimation Method

“…Previous studies [4,5] recently derived parameterized fragility curves of bridges using multi-parameter demand models in conjunction with logistic regression techniques. A main characteristic of the fragility model is the utilization of the uncertainty parameters in demand models, in addition to an IM and logistic regression.…”

“…However, [4] states that single-parameter demand models and fragility curves have the following limitations: (1) inability to reflect the effect of uncertainty parameters on structural performance during earthquakes without extensive resimulations for each new set of parameter combinations and (2) inability to explicitly address the effect of uncertainty parameters on fragility curves. Recently, to alleviate such limitations of the single-parameter demand models, logistic regression in conjunction with multi-parameter demand models comprising various predictor variables has been developed in the realm of seismic vulnerability and loss estimation [4,5]. [5] employed response surface demand models and logistic regression to derive aging highway bridge fragilities.…”

“…Recently, to alleviate such limitations of the single-parameter demand models, logistic regression in conjunction with multi-parameter demand models comprising various predictor variables has been developed in the realm of seismic vulnerability and loss estimation [4,5]. [5] employed response surface demand models and logistic regression to derive aging highway bridge fragilities. [4] used various surrogate modeling techniques to decide the bestfitting multi-parameter demand model for bridge fragilities.…”