Computationally efficient seismic fragility analysis of geostructures

Lagaros, Nikos D.; Tsompanakis, Yiannis; Psarropoulos, Prodromos N.; Georgopoulos, Evaggelos C.

doi:10.1016/j.compstruc.2008.12.001

Cited by 88 publications

(31 citation statements)

References 12 publications

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“…Metamodels are constructed to calibrate the relation between DMs and uncertain inputs of the structural models, including IMs and material parameters. The construction of the metamodels is either achieved by decomposing the nonlinear input-output relation with high-dimensional model representation (HDMR) [13,14], or realized with polynomial regression [15,16,17,18,19] or other more advanced statistical tools, such as artificial neural networks (ANNs) [20,21,22,23,24], LASSO regression [25], Bayesian networks [26], merging multivariate adaptive regression splines, radial basis function network, support vector regression [27], Kriging [9,28], etc. On the other hand, earthquake accelerations are also used directly as inputs of the metamodel in [29] to predict structural response time histories.…”

Section: Introductionmentioning

confidence: 99%

Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment

Wang

Pedroni

Zentner

et al. 2018

Engineering Structures

122

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The fragility curve is defined as the conditional probability of failure of a structure, or its critical components, at given values of seismic intensity measures (IMs). The conditional probability of failure is usually computed adopting a log-normal assumption to reduce the computational cost. In this paper, an artificial neural network (ANN) is constructed to improve the computational efficiency for the calculation of structural outputs. The following aspects are addressed in this paper: (a) Implementation of an efficient algorithm to select IMs as inputs of the ANN. The most relevant IMs are selected with a forward selection approach based on semi-partial correlation coefficients; (b) Quantification and investigation of the ANN prediction uncertainty computed with the delta method. It consists of an aleatory component from the simplification of the seismic inputs and an epistemic model uncertainty from the limited size of the training data. The aleatory component is integrated in the computation of fragility curves, whereas the epistemic component provides the confidence intervals; (c) Computation of fragility curves with Monte Carlo method and verification of the validity of the log-normal assumption. This methodology is applied to estimate the probability of failure of an electrical cabinet in a reactor building studied in the framework of the KARISMA benchmark.

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Section: Introductionmentioning

confidence: 99%

Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment

Wang

Pedroni

Zentner

et al. 2018

Engineering Structures

122

View full text Add to dashboard Cite

show abstract

“…The ANN that has been used in this study consisted of three layers: (a) the input layer with sixteen nodes (16 structural modeling parameters in Table 1); (b) the hidden layer; and (c) the output layer with two nodes (one for the median value and the other for the standard deviation). It was observed by Lagaros et al (2009) that increasing the number of hidden layers did not alter significantly the performance of the ANN, thus the runs were performed using one intermediate layer. When applying ANN for the simulation of the structural reliability, especially under dynamic loading conditions and including material nonlinearity, it is difficult to find the best configuration of ANN architecture.…”

Section: The Prediction Capabilities Of Annmentioning

confidence: 99%

“…Conventionally, fragility assessment under seismic loading using a MCS-based method is a computationally intensive problem due to the reason that the MCS-based method needs a large number of samples to get an accuracy result (Lagaros et al 2009). Thus, in this study, the ANN models were used for the prediction of fragility curves to reduce the computational cost.…”

Section: Fragility Curves Via Ann Modelsmentioning

confidence: 99%

“…Papadrakakis et al (2008) proposed an NN based Monte Carlo simulation procedure for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the dam using the continuum strong discontinuity approach. While in the work by Lagaros et al (2009), two types of ANN were implemented into a Monte Carlo simulation based vulnerability analysis framework of geostructures, where the randomness of material properties, geometry and the pseudo statically imposed seismic loading was considered. hidden layer by the connection weights.…”

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confidence: 99%

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An Artificial Neural Network Based Method for Seismic Fragility Analysis of Highway Bridges

Pang

Dang

Yuan

2014

Advances in Structural Engineering

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Fragility functions have become widely adopted in the seismic risk assessment of highway bridges, or even a transportation network. The computational effort required for a fragility analysis of highway bridges using incremental dynamic analysis (IDA) can become excessive, far beyond the capability of modern computing systems, especially when dealing with the structural parameter uncertainty in generating the fragility functions. In this paper, an artificial neural network (ANN) based prediction scheme for the generation of analytical fragility curves for highway bridges is presented. And the extremely time-consuming process in traditional analytical fragility methodologies is replaced by properly trained ANNs. The implementation of ANNs is focused on the simulation of median value and standard deviation of IDA curves at a given intensity level. The uniform design method (UDM) is proposed for selecting the training datasets for establishing a well-trained ANN model. It is observed that the proposed procedure can provides accurate estimates of fragility curves with relative short time compared to conventional procedures. The sensitivity study also reveals importance of material or geometric uncertainty in developing fragility curves of highway bridges.

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“…PGA was used as the intensity measure and damage states were based on the computed deformation of the slope crest. A similar approach was employed by Lagaros et al (2009) and Tsompanakis et al (2010), who also used PGA as an intensity measure. However unlike most examples of fragility curves, which typically use a physical damage descriptor to define damage states, the authors used target factors of safety from Greek seismic design codes as damage states.…”

Section: Introductionmentioning

confidence: 99%

Fragility curves for rainfall-induced shallow landslides on transport networks

2018

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Many of the earthworks assets on rail transport networks were constructed in the 1800's and have thus operated for periods far in excess of their expected service life. Incidences of failure, particularly shallow planar landslides are increasing, in part due to the effect of more intense and longer duration rainfall events. Network owners have difficulty in targeting scarce resources to reduce risk across networks. This paper proposes a methodology for developing fragility curves for rainfall induced landslides on transport networks. Fragility curves provide the probability of exceedance of different limit states for a given hazard considering a range of magnitudes. In this paper the vulnerability of slopes as expressed by a loss of performance, is quantified for rainfall events of various intensities and duration. The approach expands upon probabilistic slope stability analysis and provides a rational logical framework for considering how vulnerable a slope is to rainfall induced failure.

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Computationally efficient seismic fragility analysis of geostructures

Cited by 88 publications

References 12 publications

Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment

Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment

An Artificial Neural Network Based Method for Seismic Fragility Analysis of Highway Bridges

Fragility curves for rainfall-induced shallow landslides on transport networks

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