Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

2011 ◽  
Vol 89 (23-24) ◽  
pp. 2176-2194 ◽  
Author(s):  
Amir Hossein Alavi ◽  
Amir Hossein Gandomi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Ground Motion ◽  
Hybrid Method ◽  
Ground Motion Parameters ◽  
Motion Parameters ◽  
Artificial Neural

scholarly journals An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms

2010 ◽  
Vol 10 (12) ◽  
pp. 2527-2537 ◽  
Author(s):  
G-A. Tselentis ◽  
L. Vladutu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Ground Motion ◽  
Classification Problems ◽  
Attenuation Relations ◽  
Ground Motion Parameters ◽  
Ground Motion Attenuation ◽  
Motion Parameters ◽  
Artificial Neural

Abstract. Complex application domains involve difficult pattern classification problems. This paper introduces a model of MMI attenuation and its dependence on engineering ground motion parameters based on artificial neural networks (ANNs) and genetic algorithms (GAs). The ultimate goal of this investigation is to evaluate the target-region applicability of ground-motion attenuation relations developed for a host region based on training an ANN using the seismic patterns of the host region. This ANN learning is based on supervised learning using existing data from past earthquakes. The combination of these two learning procedures (that is, GA and ANN) allows us to introduce a new method for pattern recognition in the context of seismological applications. The performance of this new GA-ANN regression method has been evaluated using a Greek seismological database with satisfactory results.

Simulation of the Mean-Zero-Up-Crossing Wave Period Using Artificial Neural Networks Trained With Simulated Annealing Algorithm

2006 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
F. Aryana ◽  
S. F. Yasseri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
North East ◽  
The North ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Annealing Algorithm ◽  
Artificial Neural

The aim of this work is to simulate the 3-houly mean zero-up-crossing wave periods (Tzs) of the sea-states of a future period for a location in the North East Pacific. Seven multi-layer artificial neural networks (ANNs) were trained with simulated annealing algorithm. The output of each ANN was used for estimating each of the 7 parameters of a new distribution, described in Appendix A, called hepta-parameter spline proposed for the conditional distribution of the Tz given some significant wave heights and mean zero-up-crossing wave periods. After estimating the parameters of the conditional distributions, the Tzs have been forecasted from the hepta-parameter spline distributions corresponding to the Tzs of the period.

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