Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Kerh, Tienfuan; Ting, S. B.

doi:10.1016/j.engappai.2005.02.003

Cited by 64 publications

(26 citation statements)

References 15 publications

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“…The bar chart indicates that the present NN ? GA model performs more effectively compared with the NN model in previous studies [43,44], because the present prediction results are closer to the PGA transformed from microtremor measurement. This comparison result may provide reliability and confidence in using the NN ?…”

Section: Prediction Of Pga At the Unmeasured Sitesupporting

confidence: 54%

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

Kerh

Mosallam

2015

Neural Comput & Applic

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In this study, a genetic algorithm with global searching capability was incorporated into a neural network calculating process to obtain a highly reliable model for predicting peak ground acceleration, which is the key element in evaluating earthquake response and in establishing a seismic design standard. In addition to three seismic parameters (i.e. local magnitude, focal distance, and epicentre depth), this study included two geological conditions (i.e. standard penetration test value and shearwave velocity) in the input to reflect the site response adequately. Based on the earthquake records and soil test data from 86 checking stations, within 24 seismic subdivision zones in the Taiwan area, the computational results show that using a combination of a neural network and genetic algorithm can achieve a higher performance compared with solely using a neural network model. Furthermore, a weight-based model was developed for predicting peak ground acceleration at an unmonitored site to represent each subdivision zone. The results show that three subdivision zones have higher horizontal peak ground accelerations than the seismic design value as required in the building code. The obtained information might be helpful in relevant engineering applications for the studied region, and the proposed method for treating this type of nonlinear seismic data might be applicable in other areas of interest worldwide.

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Section: Prediction Of Pga At the Unmeasured Sitesupporting

confidence: 54%

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

Kerh

Mosallam

2015

Neural Comput & Applic

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“…Powerful capabilities of Artificial Neural Networks (ANN) have been utilized for various civil and structural engineering applications [Adeli, 2001]. Neural network approach has been applied [Kerh and Ting, 2005] for the prediction of peak ground acceleration at stations along Taiwan high-speed rail system. A new method for the generation of artificial ground motion has been proposed by Ghaboussi and Lin [1998] using neural networks.…”

Section: Introductionmentioning

confidence: 99%

ANN-based Methodology to Determine Dynamic to Static Eccentricity Ratio of Torsionally Coupled Buildings for Site-Specific Earthquakes

Kamatchi

Ramana

Nagpal

et al. 2014

Journal of Earthquake Engineering

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Although excessive damage of torsionally coupled buildings situated on soil sites has been reported during past earthquakes, no site-specific study is available having depth of soil stratum above rock as a parameter. In this article, an Artificial Neural Network-based methodology to determine dynamic to static eccentricity ratio of torsionally coupled buildings is proposed. Twelve neural networks are developed using 648,000 patterns from the analyses of shear type single-story building models. The methodology is found to be appropriate to determine dynamic to static eccentricity ratio for multi-story uniform, non uniform framed, and shear wall buildings for site-specific earthquake.

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“…Neural Network (NN) modeling has been widely used as an alternative approach for establishing nonlinear empirical equations in engineering problems for the last two decades engineering [8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation

Alacali

Akbaş

Doran

2011

Applied Soft Computing

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Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Cited by 64 publications

References 15 publications

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

ANN-based Methodology to Determine Dynamic to Static Eccentricity Ratio of Torsionally Coupled Buildings for Site-Specific Earthquakes

Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation

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