Simulating the seismic response of embankments via artificial neural networks

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

doi:10.1016/j.advengsoft.2008.11.005

Cited by 29 publications

(10 citation statements)

References 35 publications

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“…In this study, ANN models are used for estimating the seismic stability of the geostructure. Over the last two decades SC techniques, such as ANN, have emerged as a powerful tool that can be used to replace time-consuming procedures in many engineering applications [1][2][3]. Some of the fields where ANN have been successfully applied are: pattern recognition, regression (function approximation/fitting), optimization, nonlinear system modelling, identification, damage assessment, etc.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Among SC methods, artificial neural networks (ANN) is considered as one of the most eminent approaches [1,2]. Given that uncertainties are inherent in geotechnical engineering practice, approximation methods like ANN can be applied effectively in such problems [3]. Over the last decade an increasing number of articles presenting implementations of ANN in geotechnical engineering have been published.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some of the latest studies in this field are examining the applicability of ANN in soil dynamic analyses as well. A detailed literature review of the implementation of various metamodels in the field of geotechnical engineering can be found in a recent study by Tsompanakis et al [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally efficient seismic fragility analysis of geostructures

Lagaros

Tsompanakis

Psarropoulos

et al. 2009

Computers & Structures

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computationally efficient seismic fragility analysis of geostructures

Lagaros

Tsompanakis

Psarropoulos

et al. 2009

Computers & Structures

Self Cite

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“…Large earthquakes with particularly strong vibrations and a characterized compliant medium are necessary conditions for a nonlinear site response [1]. However, obtaining site response spectra due to soil nonlinearity, the unavoidable uncertainties as well as adopted simplifications during the design process can be an imprecise scientific field [13]. Moreover, the available conventional computer programs have an inherent limitation due to their sequential and algorithmic approach.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Esmaeilabadi¹,

Shahri²

2016

Adv. Sci. Technol. Res. J.

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Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN) using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

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“…The use of surrogate models (also frequently referenced as metamodels) is a popular approach for addressing this challenge and for further alleviating the computational cost associated with such simulation-based frameworks for natural hazard risk assessment (Gavin and Yau 2007;Tsompanakis et al 2009;Gidaris et al 2014). Surrogate models offer a computationally inexpensive input/output relationship based on a database of observations obtained through the initial (expensive) simulation model.…”

Section: Introductionmentioning

confidence: 99%

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

Taflanidis

Jia

Gidaris

2016

Multi-Hazard Approaches to Civil Infrastructure Engineering

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Assessment of risk under natural hazards is associated with a significant computational burden when comprehensive numerical models and simulation-based methodologies are involved. Despite recent advances in computer and computational science that have contributed in reducing this burden and have undoubtedly increased the popularity of simulation-based frameworks for quantifying/estimating risk in such settings, in many instances, such as for real-time risk estimation, this burden is still considered as prohibitive. This chapter discusses the use of kriging surrogate modeling for addressing this challenge. Kriging establishes a computationally inexpensive input/output relationship based on a database of observations obtained through the initial (expensive) simulation model. The upfront cost for obtaining this database is of course high, but once the surrogate model is established, all future evaluations require small computational effort. For illustration, two different applications are considered, involving two different hazards: seismic risk assessment utilizing stochastic ground motion modeling and real-time hurricane risk estimation. Various implementation issues are discussed, such as (a) advantages of kriging over other surrogate models, (b) approaches for obtaining high efficiency when the output under consideration is high dimensional through integration of principal component analysis, and (c) the incorporation of the prediction error associated with the metamodel into the risk assessment.

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Simulating the seismic response of embankments via artificial neural networks

Cited by 29 publications

References 35 publications

Computationally efficient seismic fragility analysis of geostructures

Computationally efficient seismic fragility analysis of geostructures

Prediction of Site Response Spectrum Under Earthquake Vibration Using an Optimized Developed Artificial Neural Network Model

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

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