Seismic risk analysis using Bayesian belief networks

Tesfamariam, Solomon; Liu, Z.

doi:10.1533/9780857098986.2.175

Cited by 16 publications

(5 citation statements)

References 75 publications

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“…). A BBN generated through expert knowledge can furnish comparable results to those generated through learning algorithms . Advantage of the expert‐derived BBN, however, is that the causal relation between different parameters of engineering significance can be maintained.…”

Section: Development Of the Bayesian Network Model Of Bridge Fragilitymentioning

confidence: 97%

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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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Section: Development Of the Bayesian Network Model Of Bridge Fragilitymentioning

confidence: 97%

“…The BBN structure can be generated through learning algorithms or expert knowledge (e.g. ). A BBN generated through expert knowledge can furnish comparable results to those generated through learning algorithms .…”

Section: Development Of the Bayesian Network Model Of Bridge Fragilitymentioning

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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“…Boulanger and Idriss revised the magnitude scaling factor relationship for SPT-based liquefaction triggering analyses, incorporating functional dependency on earthquake magnitude [25]. S. Tesfamariam and Z. Liu [26] considered the Stark and Olson [27] earthquake liquefaction datasets and concluded that the likelihood of soil liquefaction increases with increased earthquake magnitude i.e., 7 or more than 7, and consequently decreasing with earthquake magnitude below 6. The study conducted by Ahmad et al [28] also revealed that the susceptibility of sand increases with earthquakes of magnitude 7.5 to 8.0.…”

Section: Figurementioning

confidence: 99%

“…A preliminary attenuation relationship has also been developed for a limited range of soft soil sites [31]. S. Tesfamariam and Z. Liu [26] considered the Stark and Olson [27] earthquake liquefaction datasets and concluded that the probability of soil liquefaction increases with an increase in the peak ground acceleration value. When the peak ground acceleration is 0.23 g or more, the soil liquefaction susceptibility increases, and consequently, the soil liquefaction susceptibility decrease with peak ground acceleration below 0.15 g. The existing liquefaction assessment methods were used to back-calculate the magnitude and peak ground accelerations which produced the liquefaction that occurred in the Charleston earthquake and the results suggest that the ground motions of the earthquake were significantly less than those currently proposed by the seismological community [32].…”

Section: Peak Ground Accelerationmentioning

confidence: 99%

“…At the same density, soil under high effective stress is more susceptible to liquefaction than soil under low effective stress. S. Tesfamariam and Z. Liu [26] considered the Stark and Olson [27] earthquake liquefaction datasets and, intuitively, with a decrease in effective vertical stress, (σ v ) the likelihood of soil liquefaction increases.…”

Section: Standard Penetration Test Blow Countsmentioning

confidence: 99%

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Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction

et al. 2019

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Seismic soil liquefaction is considered as one of the most complex geotechnical earthquake engineering problems owing to the uncertainty and complexity involved in soil parameters, seismic parameters, and site condition factors. Each one of these parameters contains a variety of factors that trigger liquefaction and have varying degrees of importance. However, estimating accurate and reliable liquefaction-induced hazards requires identification and benchmarking of the most influential factors that control soil liquefaction. Seismic soil liquefaction factors were identified by Systematic Literature Review (SLR) approach and analyzed through Interpretive Structural Modeling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies. The ISM model presented the relationships between fifteen seismic soil liquefaction factors and their benchmarking position from higher to lower-level significant factors in hierarchy. MICMAC is used to examine the strength of the relationship between seismic soil liquefaction significant factors based on their driving and dependence power. This research characterizes the identification and benchmarking of the seismic soil liquefaction factors and their relationships. The results show that the factors—duration of earthquake, peak ground acceleration, drainage condition, and standard penetration test (SPT) blow counts—influence seismic soil liquefaction directly and soil type is the governing factor that forms the base of the ISM hierarchy and consequently triggers seismic soil liquefaction. The results provide a more accurate way of selecting significant factors for establishment of seismic soil liquefaction potential and liquefaction-induced hazards risk assessment models.

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Ahmad

Tang

Qiu

et al. 2021

Front. Struct. Civ. Eng.

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Seismic risk analysis using Bayesian belief networks

Cited by 16 publications

References 75 publications

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

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