Development of neural network model for predicting peak ground acceleration based on microtremor measurement and soil boring test data

0Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

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. © 2012 T. Kerh et al.

Cite

CITATION STYLE

APA

Kerh, T., Lin, J. S., & Gunaratnam, D. (2012). Development of neural network model for predicting peak ground acceleration based on microtremor measurement and soil boring test data. Abstract and Applied Analysis, 2012. https://doi.org/10.1155/2012/394382

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free