Seismic Design Value Evaluation Based on Checking Records and Site Geological Conditions Using Artificial Neural Networks

Yutang Lin ,Rob Saunders,Tienfuan Kerh

doi:10.1155/2013/242941

Abstract

This study proposes an improved computational neural network model that uses three seismic parameters (i.e., local magnitude, epicentral distance, and epicenter depth) and two geological conditions (i.e., shear wave velocity and standard penetration test value) as the inputs for predicting peak ground acceleration—the key element for evaluating earthquake response. Initial comparison results show that a neural network model with three neurons in the hidden layer can achieve relatively better performance based on the evaluation index of correlation coefficient or mean square error. This study further develops a new weight-based neural network model for estimating peak ground acceleration at unchecked sites. Four locations identified to have higher estimated peak ground accelerations than that of the seismic design value in the 24 subdivision zones are investigated in Taiwan. Finally, this study develops a new equation for the relationship of horizontal peak ground acceleration and focal distance by the curve fitting method. This equation represents seismic characteristics in Taiwan region more reliably and reasonably. The results of this study provide an insight into this type of nonlinear problem, and the proposed method may be applicable to other areas of interest around the world.

Highlights

Seismic design values play an important role in constructing buildings to comply with regional safety standards that consider the effects of strong ground motions
This study proposes a new set of input parameters in the neural network model for estimating the peak ground acceleration (PGA) for 86 checking stations spread across the island of Taiwan
The performance analysis above indicates that the neural network model NN2 with five inputting parameters offers reliable and generalizable results in predicting the PGA

Summary

Introduction

Seismic design values play an important role in constructing buildings to comply with regional safety standards that consider the effects of strong ground motions. After a few times of revisions and adjustments, the current building code in Taiwan classifies the entire island into two zones: the earthquake area coefficient of horizontal acceleration is 0.33 g for Zone A and 0.23 g for Zone B [1, 2] These design values can be used to calculate earthquake force and should be examined as often as possible to determine their fit with actual conditions, either from a practical viewpoint or academic viewpoint. Further to say is that three seismic parameters including local magnitude, epicentral distance, and focal depth collected from a series of historical checking records and two site soil test results including standard penetration test value (SPT-N) and shear wave velocity (Vs) are taken for training, validating, and testing the model. The method adopted in this study and the obtained results may be useful in relevant engineering fields and might be applicable to other areas of interest around the world

Research Area and Geological Condition

Development and Performance of Neural Network Model

Evaluation of Seismic Design Value in Subdivision Zone

A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 B1 B2 B3 B4 B5 B6 B7

Conclusion