Premium
Neural networks for quick earthquake damage estimation
Author(s) -
Molas Gilbert L.,
Yamazaki Fumio
Publication year - 1995
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.4290240404
Subject(s) - artificial neural network , ground motion , earthquake simulation , earthquake engineering , computer science , instability , range (aeronautics) , earthquake prediction , peak ground acceleration , set (abstract data type) , data set , sensitivity (control systems) , geology , seismology , engineering , artificial intelligence , physics , electronic engineering , mechanics , aerospace engineering , programming language
This paper proposes the use of neural networks to predict damage due to earthquakes from the indices of recorded ground motion. Since the relationship between ground motion indices and resulting damage is difficult to express in mathematical form, neural networks are conveniently applied for this problem. Simulated earthquake ground motions are used to have a well‐distributed data set and the ductility factor from non‐linear analysis of two single‐degree‐of‐freedom structural models is used to represent the damage. A sensitivity analysis procedure is described to identify qualitatively the input parameters that have a greater influence on the damage. The result of the trained neural network is then verified by using several recorded earthquake ground motions. It is found that some instability in the prediction can occur. Instability occurs when input values exceed the range of the training data. The neural network model using PGA and SI as input give the best performance in the recall tests using actual earthquake ground motion, demonstrating the usefulness of neural network models for the quick estimation of damage through earthquake intensity monitoring.