Data mining by neural network for identifying potentially hazardous bridges due to strong ground motions

Authors:
T. Kerh;C. H. Huang;D. Gunaratnam
Affiliations:
Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan;Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan;Faculty of Architecture, Design and Planning, University of Sydney, NSW, Australia
Venue:
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Year:
2011

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Abstract

The present study initially selects twenty-one bridges with lengths over 500m in the Formosa freeway of Taiwan, and collects a series of recorded seismic data from checking stations near these bridges. Then, three seismic parameters including focal depth, epicenter distance, and local magnitude, are used as the input data sets, and a model for estimating the key seismic parameter - peak ground acceleration - for each of the bridge sites is developed by using the neural network approach. This model is finally combined with a simple distribution method and a new weight-based method to estimate peak ground acceleration at each of the bridges along the freeway. Based on the seismic design value in the current building code as the evaluation criteria, the model identifies five bridges, out of all the bridges investigated, as having the potential to be subjected to significantly higher horizontal peak ground accelerations than that recommended for design in the building code. The method presented in this study hence provides a valuable reference for dealing with nonlinear seismic data by developing neural network model, and the approach presented is also applicable to other areas of interest around the world.