A Neural Network Based Methodology for Estimating Bridge Damage after Major Earthquakes
Resource
Journal of the Chinese Institute of Civil and Hydraulic Engineering 25 (4): 415-424
Journal
Journal of the Chinese Institute of Civil and Hydraulic Engineering
Journal Volume
25
Journal Issue
4
Pages
415-424
Date Issued
2002
Date
2002
Author(s)
Abstract
Abstract A neural network based seismic bridge damage estimation system is proposed to estimate damage to the bridges in Taiwan after major earthquakes. The damage estimation system is composed of two parts: a peak ground acceleration (PGA) estimator and a bridge damage estimator. The PGA neural network estimator was first trained by the ground motion data recorded from the eight largest earthquakes in Taiwan over the past ten years. The magnitude, depth, and epicenter coordinates of the earthquake were used as the inputs in this neural network. The damage estimator was trained to learn the relationship among bridge coordinates, structure types, PGAs and damage levels to bridges based on the data collected from the 1999 Chi‐Chi earthquake. Sixty‐four sets of bridge data were used to train a 7–8–8–2 neural network. These two neural networks were then integrated into one system and were tested, using another twenty sets of bridge damage data. The result has demonstrated that the proposed method is able to successfully assess the damage of bridges due to major earthquakes in Taiwan.
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Type
journal article
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