Ground Motion Prediction Model Using Artificial Neural Network

2017 ◽  
Vol 175 (3) ◽  
pp. 1035-1064 ◽  
Author(s):  
J. Dhanya ◽  
S. T. G. Raghukanth
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Artificial Neural

Ground Motion Prediction Model for the Peak Inelastic Displacement of Single-Degree-of-Freedom Bilinear Systems

2018 ◽  
Vol 34 (3) ◽  
pp. 1177-1199 ◽  
Author(s):  
Pablo Heresi ◽  
Héctor Dávalos ◽  
Eduardo Miranda
Keyword(s):  
Prediction Model ◽  
Ground Motion ◽  
Degree Of Freedom ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Vibration Period ◽  
Single Degree Of Freedom ◽  
Inelastic Displacement ◽  
Single Degree ◽  
Proposed Model

This paper presents a ground motion prediction model (GMPM) for estimating medians and standard deviations of the random horizontal component of the peak inelastic displacement of 5% damped single-degree-of-freedom (SDOF) systems, with bilinear hysteretic behavior and 3% postelastic stiffness ratio, directly as a function of the earthquake magnitude and the distance to the source. The equations were developed using a mixed effects model, with 1,662 recorded ground motions from 63 seismic events. In the proposed model, the median is computed as a function of the vibration period and the normalized strength of the system, as well as the event magnitude and the Joyner-Boore distance to the source. The standard deviation of the model is computed as a function of the vibration period and the normalized strength of the system. The proposed model has the advantage of not requiring an auxiliary elastic GMPM to predict the median and dispersion of peak inelastic displacement.

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