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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 242941, 12 pages
Seismic Design Value Evaluation Based on Checking Records and Site Geological Conditions Using Artificial Neural Networks
1Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 91207, Taiwan
2Faculty of Architecture, Design and Planning, University of Sydney, Sydney, NSW 2006, Australia
Received 8 February 2013; Accepted 25 April 2013
Academic Editor: Fuding Xie
Copyright © 2013 Tienfuan Kerh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Central Weather Bureau, “What is the zone division standard of anti-earthquake for general building in Taiwan?” Hundred Questions of Earthquake, 2005, http://scman.cwb.gov.tw/eqv5/eq100/100/058.htm.
- Construction and Planning Agency, “Revision of building anti-earthquake design code and explanation,” Ministry of the Interior, 2006, http://www.cpami.gov.tw/web/index.php?option=com_content&task=view&id=976&Itemid=95.
- S. E. Hough, T. Taniguchi, and J. R. Altidor, “Estimation of peak ground acceleration from horizontal rigid body displacement: a case study in Port-au-Prince, Haiti,” Bulletin of the Seismological Society of America, vol. 102, no. 6, pp. 2704–2713, 2012.
- H. Adeli and A. Panakkat, “A probabilistic neural network for earthquake magnitude prediction,” Neural Networks, vol. 22, no. 7, pp. 1018–1024, 2009.
- K. V. Yuen, Bayesian Methods for Structural Dynamics and Civil Engineering, John Wiley & Sons, 2010.
- K. V. Yuen and H. Q. Mu, “Peak ground acceleration estimation by linear and nonlinear models with reduced order Monte Carlo simulation,” Computer-Aided Civil and Infrastructure Engineering, vol. 26, no. 1, pp. 30–47, 2011.
- E. I. Alves, “Earthquake forecasting using neural networks: results and future work,” Nonlinear Dynamics, vol. 44, no. 1–4, pp. 341–349, 2006.
- G. Ghodrati Amiri and A. Bagheri, “Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms,” Structural Engineering and Mechanics, vol. 28, no. 2, pp. 153–166, 2008.
- C. R. Arjun and A. Kumar, “Neural network estimation of duration of strong ground motion using Japanese earthquake records,” Soil Dynamics and Earthquake Engineering, vol. 31, no. 7, pp. 866–872, 2011.
- M. H. Baziar and A. Ghorbani, “Evaluation of lateral spreading using artificial neural networks,” Soil Dynamics and Earthquake Engineering, vol. 25, no. 1, pp. 1–9, 2005.
- H. Dai and C. MacBeth, “Application of back-propagation neural networks to identification of seismic arrival types,” Physics of the Earth and Planetary Interiors, vol. 101, no. 3-4, pp. 177–188, 1997.
- S. R. García, M. P. Romo, and J. M. Mayoral, “Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks,” Geofisica Internacional, vol. 46, no. 1, pp. 51–63, 2007.
- S. C. Lee and S. W. Han, “Neural-network-based models for generating artificial earthquakes and response spectra,” Computers and Structures, vol. 80, no. 20-21, pp. 1627–1638, 2002.
- C. C. J. Lin and J. Ghaboussi, “Generating multiple spectrum compatible accelerograms using stochastic neural networks,” Earthquake Engineering and Structural Dynamics, vol. 30, no. 7, pp. 1021–1042, 2001.
- A. Panakkat and H. Adeli, “Neural network models for earthquake magnitude prediction using multiple seismicity indicators,” International Journal of Neural Systems, vol. 17, no. 1, pp. 13–33, 2007.
- A. Panakkat and H. Adeli, “Recent efforts in earthquake prediction (1990-2007),” Natural Hazards Review, vol. 9, no. 2, pp. 70–80, 2008.
- A. Panakkat and H. Adeli, “Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators,” Computer-Aided Civil and Infrastructure Engineering, vol. 24, no. 4, pp. 280–292, 2009.
- G. A. Tselentis and L. Vladutu, “An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms,” Natural Hazards and Earth System Science, vol. 10, no. 12, pp. 2527–2537, 2010.
- T. Kerh, J. S. Lai, D. Gunaratnam, and R. Saunders, “Evaluation of seismic design values in the Taiwan building code by using artificial neural network,” Computer Modeling in Engineering and Sciences, vol. 26, no. 1, pp. 1–12, 2008.
- T. Kerh and D. Chu, “Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion,” Advances in Engineering Software, vol. 33, no. 11-12, pp. 733–742, 2002.
- T. Kerh, D. Gunaratnam, and Y. Chan, “Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake,” Neural Computing and Applications, vol. 19, no. 4, pp. 521–529, 2010.
- T. Kerh, Y. Chan, and D. Gunaratnam, “Treatment and assessment of nonlinear seismic data by a genetic algorithm based neural network model,” International Journal of Nonlinear Sciences and Numerical Simulation, vol. 10, no. 1, pp. 45–56, 2009.
- C. T. Huang, “A study on the earthquake potential damage and evaluation method—earthquake spectral research of considering regional site effect,” Project of National Science Council NSC 90-2625-Z-011-001, 2002.
- G. L. Wun, W. Y. Gien, and Y. W. Chang, “Strong ground motion site effect in Taiwan area,” Earthquake Technology Report MOTC-CWB-93-E-09, Central Weather Bureau, 2004.
- K. Günaydın and A. Günaydın, “Peak ground acceleration prediction by artificial neural networks for northwestern Turkey,” Mathematical Problems in Engineering, vol. 2008, Article ID 919420, 20 pages, 2008.
- B. Derras, “Peak ground acceleration prediction using artificial neural networks approach: application to the Kik-Net data,” International Review of Civil Engineering, vol. 1, no. 3, pp. 243–252, 2010.
- Central Geological Survey, “Taiwan fault distribution map,” 2011, Ministry of Economic Affairs, http://fault.moeacgs.gov.tw/TaiwanFaults/Default.aspx?LFun=2.
- H. Tsai and G. F. Yang, Faults and Earthquakes of Taiwan, Walkers Cultural Enterprise Ltd, Taipei, Taiwan, 2004.
- Encyclopedia of Taiwan, “Geology,” 2012, http://taiwanpedia.culture.tw/web/index.
- K. Kuźniar, E. Maciag, and Z. Waszczyszyn, “Computation of response spectra from mining tremors using neural networks,” Soil Dynamics and Earthquake Engineering, vol. 25, no. 4, pp. 331–339, 2005.
- Y. Lu, “Underground blast induced ground shock and its modelling using artificial neural network,” Computers and Geotechnics, vol. 32, no. 3, pp. 164–178, 2005.
- S. Mandal, S. Rao, and D. H. Raju, “Ocean wave parameters estimation using backpropagation neural networks,” Marine Structures, vol. 18, no. 3, pp. 301–318, 2005.
- F. Sarghini, G. Felice, and S. Santini, “Neural networks based subgrid scale modeling in large eddy simulations,” Computers and Fluids, vol. 32, no. 1, pp. 97–108, 2003.
- E. Harmandar, E. Cakti, and M. Erdik, “A method for spatial estimation of peak ground acceleration in dense arrays,” Geophysical Journal International, vol. 191, no. 3, pp. 1272–1284, 2012.
- T. Kerh, C. H. Huang, and D. Gunaratnam, “Neural network approach for analyzing seismic data to identify potentially hazardous bridges,” Mathematical Problems in Engineering, vol. 2011, Article ID 464353, 15 pages, 2011.
- T. Kerh, T. Ku, and D. Gunaratnam, “Comparative evaluations of the seismic key parameter by artificial neural network model and ambient vibration survey,” Disaster Advances, vol. 4, no. 2, pp. 5–12, 2011.
- T. Kerh and S. B. Ting, “Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system,” Engineering Applications of Artificial Intelligence, vol. 18, no. 7, pp. 857–866, 2005.