Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

It is essential to understand the characteristics of strong motion for reducing the negative impacts in a high-risk area. In this work, a combination of seismic parameters including epicentral distance, focal depth, and magnitude from historical records at 30 checking stations were used in back-propagation neural network model, to estimate peak ground acceleration at ten train stations along the high-speed rail system in Taiwan. The estimation was verified with available microtremor measurement at a specified station, and the calculated horizontal acceleration was checked with the existing building code requirements. A potential hazardous station was identified from the neural network estimation, which exhibited a significantly higher acceleration than that of the design value. The obtained results might be useful for revising the currently applied building code at this region to further fit in the actual earthquake response.

[1]  Shie-Yui Liong,et al.  River Stage Forecasting in Bangladesh: Neural Network Approach , 2000 .

[2]  Dong-Sheng Jeng,et al.  Application of artificial neural networks in tide-forecasting , 2002 .

[3]  F. E. Udwadia,et al.  Comparison of earthquake and microtremor ground motions in El Centro, California , 1973, Bulletin of the Seismological Society of America.

[4]  L. Bodri,et al.  Prediction of extreme precipitation using a neural network: application to summer flood occurence in Moravia , 2000 .

[5]  Anil K. Chopra,et al.  Dynamics of Structures: Theory and Applications to Earthquake Engineering , 1995 .

[6]  Genki Yagawa,et al.  FINITE ELEMENT SOLUTIONS WITH FEEDBACK NETWORK MECHANISM THROUGH DIRECT MINIMIZATION OF ENERGY FUNCTIONALS , 1996 .

[7]  Tienfuan Kerh,et al.  Erratum to Estimation of consolidation settlement caused by groundwater drawdown using artificial neural networks , 2004 .

[8]  T. Kerh,et al.  Analysis of a deformed three-dimensional culvert structure using neural networks , 2000 .

[9]  Abdussamet Arslan,et al.  Neural network-based design of edge-supported reinforced concrete slabs , 1996 .

[10]  Yoshio Hirose,et al.  Backpropagation algorithm which varies the number of hidden units , 1989, International 1989 Joint Conference on Neural Networks.

[11]  Martin T. Hagan,et al.  Neural network design , 1995 .

[12]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[13]  Pc Pandey,et al.  Multilayer perceptron in damage detection of bridge structures , 1995 .

[14]  T. Kerh,et al.  Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion , 2002 .

[15]  Francisco J. Chávez-García,et al.  Are microtremors useful in site response evaluation , 1994 .

[16]  Jacques de Villiers,et al.  Backpropagation neural nets with one and two hidden layers , 1993, IEEE Trans. Neural Networks.

[17]  Shafiqur Rehman,et al.  Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia , 2002 .

[18]  K. Thirumalaiah,et al.  Hydrological Forecasting Using Neural Networks , 2000 .