A Robust Neural Network Model for Pattern Recognition of Pressure Transient Test Data

1993 ◽  
Author(s):  
Iraj Ershaghi ◽  
Xuehai Li ◽  
Mahnaz Hassibi ◽  
Yusuf Shikari
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Model ◽  
Test Data ◽  
Neural Network Model ◽  
Pressure Transient ◽  
Pressure Transient Test

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

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
T. Kerh ◽  
J. S. Lin ◽  
D. Gunaratnam
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Test Data ◽  
Neural Network Model ◽  
Peak Ground Acceleration ◽  
Soil Depth ◽  
Standard Penetration Test ◽  
Ground Acceleration ◽  
Short Period ◽  
Microtremor Measurement

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.

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