Framework for Post-Earthquake Risk Assessment and Decision Making for Infrastructure Systems

Bensi, Michelle; Kiureghian, Armen Der; Straub, Daniel

doi:10.1061/ajrua6.0000810

Cited by 29 publications

(19 citation statements)

References 16 publications

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“…The CPT, B p = { p ( x i | pa ( x i )| x i ε X )}, gives the causal relation between parents pa( x i ) and child x i . The joint distribution of the random variables in the BBN is given as the product of the conditional probability distributions :

p ((), x_{1}, \dots, x_{n}) = true {true\prod}_{i = 1}^{n} p ((), x_{i} (|, p a ((), x_{i}))) .

…”

Section: Development Of the Bayesian Network Model Of Bridge Fragilitymentioning

confidence: 99%

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Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Franchin

Lupoi

Noto³

et al. 2015

Earthq Engng Struct Dyn

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Summary Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so‐called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations. Copyright © 2015 John Wiley & Sons, Ltd.

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p ((), x_{1}, \dots, x_{n}) = true {true\prod}_{i = 1}^{n} p ((), x_{i} (|, p a ((), x_{i}))) .

…”

Section: Development Of the Bayesian Network Model Of Bridge Fragilitymentioning

confidence: 99%

“…Various authors have reported application of BBN in reliability assessment of network of bridges and other civil infrastructure systems . The assessment of bridge vulnerability requires methods that can combine empirically and analytically derived data, and heuristics.…”

Section: Introductionmentioning

confidence: 99%

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Franchin

Lupoi

Noto³

et al. 2015

Earthq Engng Struct Dyn

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“…This makes it convenient not only for graphical communication of the model but also for quantitative probabilistic modeling. For these reasons, BN are increasingly applied for risk assessment of natural hazards, e.g., for wildfire occurrence (Dlamini 2009), rockfall hazards (Straub 2005), avalanches (Grêt-Regamey & Straub 2006), tsunamis (Blaser et al 2009) and earthquakes (Bayraktarli et al 2005, Kuehn et al 2011, Bensi et al 2014. For a detailed introduction to BN, the reader is referred to Jensen & Nielsen (2007).…”

Section: Canadian Forest Fire Weather Index Systemmentioning

confidence: 99%

Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

Papakosta¹,

Straub²

2017

iForest

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The prediction of wildfire occurrence is an important component of fire management. We have developed probabilistic daily fire prediction models for a Mediterranean region of Europe (Cyprus) at the mesoscale, based on Poisson regression. The models use only readily available spatio-temporal data, which enables their use in an operational setting. Influencing factors included in the models are weather conditions, land cover and human presence. We found that the influence of weather conditions on fire danger in the studied area can be expressed through the FWI component of the Canadian Forest Fire Weather Index System. However, the prediction ability of FWI alone was limited. A model that additionally includes land cover types, population density and road density was found to provide significantly improved predictions. We validated the probabilistic prediction provided by the model with a test data set and illustrate it with maps for selected days.

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“…The influence diagram provides efficient updating of a deterioration model when indirect information is available and in addition it includes the possibility to find expected utilities for decision alternatives [234]. Bensi et al [235] used the Bayesian network and influence diagram to develop a framework for post-earthquake risk assessment and decision making for infrastructure systems. In order to reduce the number of memory links, an alternative is to use the limited memory influence diagram (LIMID) that includes only direct parents [236][237][238].…”

Section: Influence Diagramsmentioning

confidence: 99%

“…For the application to real-life infrastructure systems, additional work is needed to develop more efficient approaches to improve the feasibility of near-real-time applications involving large infrastructure systems [235].…”

mentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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Framework for Post-Earthquake Risk Assessment and Decision Making for Infrastructure Systems

Cited by 29 publications

References 16 publications

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

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