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

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Abstract

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 shear-wave 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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Acknowledgments

Financial support from the Ministry of Science and Technology is greatly appreciated. The authors also gratefully acknowledge the Central Weather Bureau Seismological Center and National Center for Research on Earthquake Engineering of Taiwan for providing historical seismic records and geological surveys, respectively. In addition, the improvement of English by Mr. Wesley Jantjies with Wallace Academic Editing is acknowledged.

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Authors and Affiliations

  1. Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, 91207, Taiwan

    Tienfuan Kerh & Yu-Hsiang Su

  2. Department of Civil and Environmental Engineering, University of California, Irvine, CA, 92697, USA

    Ayman Mosallam

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  1. Tienfuan Kerh
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  2. Yu-Hsiang Su
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  3. Ayman Mosallam
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Correspondence to Tienfuan Kerh.

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Kerh, T., Su, YH. & Mosallam, A. Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data. Neural Comput & Applic 28, 437–448 (2017). https://doi.org/10.1007/s00521-015-2077-7

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  • DOI: https://doi.org/10.1007/s00521-015-2077-7

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