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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 394382, 13 pages
http://dx.doi.org/10.1155/2012/394382
Research Article

Development of Neural Network Model for Predicting Peak Ground Acceleration Based on Microtremor Measurement and Soil Boring Test Data

1Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 91207, Taiwan
2Faculty of Architecture, Design and Planning, University of Sydney, Sydney, NSW 2006, Australia

Received 20 August 2012; Accepted 20 September 2012

Academic Editor: Ju H. Park

Copyright © 2012 T. Kerh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It may not be possible to collect adequate records of strong ground motions in a short period of time; hence microtremor survey is frequently conducted to reveal the stratum structure and earthquake characteristics at a specified construction site. This paper is therefore aimed at developing a neural network model, based on available microtremor measurement and on-site soil boring test data, for predicting peak ground acceleration at a site, in a science park of Taiwan. The four key parameters used as inputs for the model are soil values of the standard penetration test, the medium grain size, the safety factor against liquefaction, and the distance between soil depth and measuring station. The results show that a neural network model with four neurons in the hidden layer can achieve better performance than other models presently available. Also, a weight-based neural network model is developed to provide reliable prediction of peak ground acceleration at an unmeasured site based on data at three nearby measuring stations. The method employed in this paper provides a new way to treat this type of seismic-related problem, and it may be applicable to other areas of interest around the world.