scholarly journals Assessment the Effective Ground Motion Parameters on Seismic Performance of R/C Buildings using Artificial Neural Network

2014 ◽  
Vol 7 (12) ◽  
pp. 2076-2082 ◽  
Author(s):  
A. Kia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Seismic Performance ◽  
Ground Motion Parameters ◽  
Motion Parameters ◽  
Artificial Neural

Prediction of Ground Motion Intensity Measures Using an Artificial Neural Network

2021 ◽  
Author(s):  
K. P. Sreejaya ◽  
Jahnabi Basu ◽  
S. T. G. Raghukanth ◽  
D. Srinagesh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Intensity Measures ◽  
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.

scholarly journals Observations on the application of artificial neural network to predicting ground motion measures

2012 ◽  
Vol 25 (2) ◽  
pp. 161-175 ◽  
Author(s):  
Hanping Hong ◽  
Taojun Liu ◽  
Chien-Shen Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Motion ◽  
Artificial Neural

scholarly journals Semi-empirical relationships to assess the seismic performance of slopes from an updated version of the Italian seismic database

2020 ◽  
Vol 18 (14) ◽  
pp. 6245-6281
Author(s):  
D. Gaudio ◽  
R. Rauseo ◽  
L. Masini ◽  
S. Rampello
Keyword(s):  
Ground Motion ◽  
Seismic Performance ◽  
Permanent Displacement ◽  
Empirical Relationships ◽  
Site Specific ◽  
Ground Motion Parameters ◽  
Seismic Coefficient ◽  
Motion Parameters ◽  
Semi Empirical ◽  
Parametric Integration

Abstract Seismic performance of slopes can be assessed through displacement-based procedures where earthquake-induced displacements are usually computed following Newmark-type calculations. These can be adopted to perform a parametric integration of earthquake records to evaluate permanent displacements for different slope characteristics and seismic input properties. Several semi-empirical relationships can be obtained for different purposes: obtaining site-specific displacement hazard curves following a fully-probabilistic approach, to assess the seismic risk associated with the slope; providing semi-empirical models within a deterministic framework, where the seismic-induced permanent displacement is compared with threshold values related to different levels of seismic performance; calibrating the seismic coefficient to be used in pseudo-static calculations, where a safety factor against limit conditions is computed. In this paper, semi-empirical relationships are obtained as a result of a parametric integration of an updated version of the Italian strong-motion database, that, in turn, is described and compared to older versions of the database and to well-known ground motion prediction equations. Permanent displacement is expressed as a function of either ground motion parameters, for a given yield seismic coefficient of the slope, or of both ground motion parameters and the seismic coefficient. The first are meant to be used as a tool to develop site-specific displacement hazard curves, while the last can be used to evaluate earthquake-induced slope displacements, as well as to calibrate the seismic coefficient to be used in a pseudo-static analysis. Influence of the vertical component of seismic motion on these semi-empirical relationships is also assessed.

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