Evaluation of seismic design values in the Taiwan building code by using artificial neural network

CMES - Computer Modeling in Engineering and Sciences

Volumn 26, Issue 1, 2008, Pages 1-12

  Kerh, Tienfuan ^a,b   Lai, J S ^a   Gunaratnam, D ^b   Saunders, R ^b  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Building code; Curve fitting model; Design value criteria; Neural network; Peak ground acceleration; Potential hazardous locations

Indexed keywords

ACCELERATION; BUILDING CODES; BUILDINGS; CODES (STANDARDS); CODES (SYMBOLS); CURVE FITTING; DIFFERENTIAL AMPLIFIERS; EQUATIONS OF MOTION; IMAGE CLASSIFICATION; MATHEMATICAL MODELS; NUMERICAL ANALYSIS; PARAMETER ESTIMATION; PROGRAMMED CONTROL SYSTEMS; SEISMIC DESIGN; SEISMOLOGY;

ARTIFICIAL NEURAL NETWORK (ANNS); DESIGN VALUES; EARTHQUAKE RECORDS; EPICENTER DISTANCE; FOCAL DEPTH; FOCAL DISTANCES; GOVERNMENT AGENCIES; HORIZONTAL ACCELERATIONS; INPUT PARAMETERS; MATHEMATICAL EQUATIONS; NEURAL NETWORK (NN) MODELS; PEAK GROUND ACCELERATION (PGA); SEISMIC ZONES; SPATIAL RELATIONSHIP ('MA' PERCEPTIVE DISTANCE); STRONG GROUND MOTION; STRONG MOTION;

NEURAL NETWORKS;

EID: 46449124161     PISSN: 15261492     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (40)

1. Benitez, J. M.; Castro, J. L.; Requena, I. (1997): Are artificial neural networks black boxes? IEEE Transactions of Neural Net, vol. 8(5), pp 1156-1164.
2. Central Weather Bureau. (2005). One hundred questions of earthquake. http://www.cwb.gov.tw. Ministry of Transportation, Taiwan.
3. Chang, F. J.; Chang, Y. T. (2006): Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, vol. 29, pp. 1-10.
4. Chu, D.; Kerh, T.; Wu, C.H. (2003): Analysis of strong motion information at checking stations of Kaohsiung area by back-propagation neural networks. J. Civil Engineering Technology, vol. 7(2), pp. 45-62.
5. Construction and Planning Agency. (2006): Building Code, http://www.cpami.gov.tw. Ministry of the Interior, Taiwan.
6. Conte, J. P.; Peng, B.F. (1997): Fully nonstationary analytical earthquake ground-motion model. J. Engineering Mechanics, ASCE, vol. 123(1), pp. 15-24.
7. Der Kiureghian, A,; Crempien, J. (1989): An evolutionary model for earthquake ground motion. Structure Safety, vol. 6, pp. 235-246.
8. Ghaboussi J.; Lin, C. J. (1998): New method of generating spectrum compatible accelerograms using neural networks. Earthquake Engineering and Structural Dynamics, vol. 27, pp. 377-396.
9. Hagan, M. T.; Demuth, H. B.; Beale, M. (2004): Neural network design. Princeton International Publishing Co., Ltd.
10. Katayama, T. (1982): An engineering prediction model of acceleration response spectra and its application to seismic hazard mapping. Earthquake Engineering and Structural Dynamics, vol. 10, pp. 149-163.
11. Kerh, T.; Chu, D. (2002): Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion. Advances in Engineering Software, vol. 33, pp. 733-742.
12. Kerh, T.; Lee, J. H.; Chen, L.Y. (1996): Estimation of strong motion using microtremor measurement at the main lines of Kaohsiung mass rapid transit. J. Civil Engineering Technology, vol. 7, pp. 15-40.
13. Kerh, T.; Ting, S. B. (2005): Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. Engineering Applications of Artificial Intelligence, vol. 18, pp. 857-866.
14. Kuźniar, K.; Maciag, E.; Waszczyszyn, Z. (2005): Computation of response spectra from mining tremors using neural networks. Soil Dynamics and Earthquake Engineering, vol. 25, pp. 331-339.
15. Lee, G. C.; Loh, C. H. (2000): The Chi-Chi, Taiwan, earthquake of September 21, 1999. Reconnaissance Report, Multidisciplinary Center for Earthquake Engineering Research, Buffalo, N.Y.
16. Lee, S. C.; Han, S. W. (2002): Neural-network-based models for generating artificial earthquakes and response spectra. Computers and Structures, vol. 80, pp.1627-1638.
17. Lermo, J.; Chávez-Gaírcía, F. J. (1994): Are microtremors useful in site response evaluation? B. Seismological Society of America, vol. 84, pp. 1359-1364.
18. Liang, J. W.; Zhang, Y.; Lee, V.W. (2005): Scattering of plane p waves by a semi-cylindrical hill: analytical solution. Int. J. Earthquake Engineering and Engineering Vibration, vol. 4(1), pp. 27-36.
19. Lin, J. T. (2000): Distinguishing and prediction of marketing consumption on radio communication by neural networks," Master Thesis of Management, Yuanchi University, Taiwan, pp. 50-53.
20. Lin, Y. K.; Yong, Y. (1987): Evolutionary Kanai-Tajimi earthquake models. J. Engineering Mechanics, ASCE, vol. 111(8), pp.1119-1137.
21. Lu, Y. (2005): Underground blast induced ground shock and its modelling using artificial neural network. Computers and Geotechnics, vol. 32, pp. 164-178.
22. Luco, J. E.; Wong, H. L. (1987): Seismic response of foundations embedded in a layered half-space. Int. J. Earthquake Engineering and Structural Dynamics, vol. 15(2), pp. 233-247.
23. Ma, K.F. (1999): Teleseismic and near source strong motion waveforms investigation of the September 21, 1999 Ji-Ji Taiwan earthquake. Proceedings of the Hazard Investigation on Ji-Ji Earthquake 1999, vol. II, pp. 1-15.
24. Mandal, S.; Rao, S.; Raju, D.H. (2005): Ocean wave parameters estimation using backpropagation neural networks. Marine Structures, vol. 18, pp. 301-318.
25. McCulloch, W.; Pitts, W. (1943): A logical calculus of the ideas immanent in nervous activity. B. Mathematical Biophysics, vol.7, pp. 115-133.
26. Nakamura, Y.; Takizawa, T. (1990): The surface layer thickness and the shearing wave velocity of basement and surface estimated by microtremor measurement. Railway Technical Research Institute Report, vol. 4(9), pp. 29-35.
27. Noroozi, S.; Sewell, P.; Vinney, J. (2006): The application of a hybrid inverse boundary element problem engine for the solution of potential problems. CMES: Computer Modeling in Engineering and Sciences, vol. 14(3), pp. 171-180.
28. Oishi, A.; Yoshimura, S. (2007): A new local contact search method using a multi-layer neural network. CMES: Computer Modeling in Engineering and Sciences, vol. 21(2), pp. 93-104.
29. Ozerdem, M. S.; Ustundag, B.; Demirer, R.M. (2006): Self-organized maps based neural networks for detection of possible earthquake precursory electric field patterns. Advances in Engineering Software, vol. 37, pp. 207-217.
30. Pu, Y.; Mesbahi, E. (2006): Application of artificial neural networks to evaluation of ultimate strength of steel panels. Engineering Structures, vol. 28, pp. 1190-1196.
31. Sarghini, F.; de Felice, G.; Santini, S. (2003): Neural networks based subgrid scale modeling in large eddy simulations. Computers and Fluids, vol. 32, pp. 97-108.
32. Seismological Center. (2007): Earthquake records. http://scman.cwb.gov.tw. Central Weather Bureau, Taiwan.
33. Shanga, Z. (2005): Application of artificial intelligence CFD based on neural network in vaporwater two-phase flow. Engineering Applications of Artificial Intelligence, vol. 18, pp. 663-671.
34. Trifunac, M. D. (2005): On publication rates in earthquake engineering. Soil Dynamics and Earthquake Engineering, vol. 25(6), pp. 413-420.
35. Trifunac, M. D.; Lee, V.W. (1989): Empirical models for scaling Fourier amplitude spectra of strong ground acceleration in terms of earthquake magnitude, source to station distance, site intensity and recording site conditions. Soil Dynamics and Earthquake Engineering, vol. 8(3), pp. 110-125.
36. Wikipedia Encyclopedia. (2008): Information technology, http://en.wikipedia.org/wiki/Information_technology, Wikimedia Foundation, Inc., U.S.
37. Wun, S.; Chou, L; Lee, S.; Chang, B. Z. (2003): MATLAB neural networks simulation and application. Science Publishing Company, China.
38. Yeh, Y. C. (1997): Applied neural networks. Rulin Book Publishing Company, Taiwan.
39. Yeh C. H.; Wen, Y.K. (1990): Modeling of non-stationary ground motion and analysis of inelastic structural response. Structure Safety, vol. 8, pp. 281-298.
40. Zhang, L.; Subbarayan, G. (2002): An evaluation of back-propagation neural networks for the optimal design of structural systems: part ii. numerical evaluation. Computer Methods in Applied Mechanics and Engineering, vol. 191, pp. 2887-2904.