Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data
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- 作者：Tienfuan Kerh ; Yu-Hsiang Su ; Ayman Mosallam
- 关键词：Seismic record ; Geological condition ; Genetic algorithm ; Neural network ; Weight ; based model ; Unmonitored site
- 刊名：Neural Computing and Applications
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：28
- 期：3
- 页码：437-448
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Visi
- 出版者：Springer London
- ISSN：1433-3058
- 卷排序：28
文摘
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.