Fuzzy-valued intensity measures for near-fault pulse-like ground motions

Computer-Aided Civil and Infrastructure Engineering

Volumn 28, Issue 10, 2013, Pages 780-795

  Li, Hao ^a   Yi, Wei Jian ^b   Yuan, Xian Xun ^c  

^a JIANGXI SCIENCE AND TECHNOLOGY NORMAL UNIVERSITY   (China)

^b HUNAN UNIVERSITY   (China)

^c RYERSON UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONCRETE FRAME STRUCTURES; PERFORMANCE BASED SEISMIC DESIGN; PREDICTION ACCURACY; PULSE-LIKE GROUND MOTIONS; PUSH-OVER ANALYSIS; REINFORCED CONCRETE FRAME STRUCTURES; SPECTRAL DISPLACEMENT; STIFFNESS DEGRADATION;

CONCRETES; REINFORCED CONCRETE; STRUCTURAL FRAMES; TALL BUILDINGS;

LOADING;

CONCRETE STRUCTURE; FUZZY MATHEMATICS; GROUND MOTION; MULTISTOREY BUILDING; REINFORCED CONCRETE; SEISMIC DESIGN; STIFFNESS;

EID: 84885467800     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/mice.12053     Document Type: Article

References (66)

1. Adeli, H., Weaver, W., Jr. & Gere, J. M. (1978), Algorithms for nonlinear structural dynamics, Journal of Structural Division, ASCE, 104(ST2), 263-80.
2. Alavi, B. & Krawinkler, H. (2001), Effects of near fault ground motions on frame structures. Report No. 138, Stanford, California, The John A. Blume Earthquake Engineering Center, Department of Civil Engineering, Stanford University, 156-88.
3. Arias, A. (1970), A measure of earthquake intensity, in R. J. Hansen (ed.), Seismic Design for Nuclear Power Plants, MIT Press, Cambridge, MA, pp. 438-83.
4. Baker, J. W. & Cornell, C. A. (2005), A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon, Earthquake Engineering and Structural Dynamics, 34(10), 1193-217.
5. Baker, J. W. & Cornell, C. A. (2006), Spectral shape, epsilon and record selection, Earthquake Engineering and Structural Dynamics, 35(9), 1077-95.
6. Baker, J. W. & Cornell, C. A. (2008), Vector-valued intensity measures for pulse-like near-fault ground motions, Engineering Structures, 30(4), 1048-57.
7. Bazzurro, P., Cornell, C. A., Shome, N., & Carballo, J. E. (1998), Three proposals for characterizing MDOF nonlinear seismic response, Journal of Structural Engineering, 124 (11), 1281-89.
8. Chopra, A. K. (2012), Dynamics of Structures: Theory and Applications to Earthquake Engineering, 4th edn., Prentice Hall, Upper Saddle River, NJ.
9. Chopra, A. K. & Goel, R. K. (2002), A modal pushover analysis procedure for estimating seismic demands for buildings, Earthquake Engineering and Structural Dynamics, 31(3), 561-82.
10. Cordova, P. P., Deierlein, G. G., Mehanny, S. S. F., & Cornell, C. A. (2001), Development of two-parameter seismic intensity measure and probabilistic assessment procedure, in Proceedings of the 2nd U.S. - Japan Workshop on Performance-Based Earthquake Engineering Methodology for Reinforced Concrete Building Structures, Sapporo, Japan.
11. Cornell, C. A. (1968), Engineering seismic risk analysis, Bulletin of the Seismological Society of America, 58(5), 1583-1606.
12. Freitag, S., Graf, W. & Kaliske, M. (2011), Recurrent neural networks for fuzzy data, Integrated Computer-Aided Engineering, 18(3), 265-80.
13. Ghodrati Amiri, G., Abdolahi Rad, A., Aghajari, S. & Khanmohamadi Hazaveh, N. (2012), Generation of near-field artificial ground motions compatible with median-predicted spectra using PSO-based neural network and wavelet analysis, Computer-Aided Civil and Infrastructure Engineering, 27(9), 711-30.
14. Giovenale, P., Cornell, A. C. & Esteva, L. (2004), Comparing the adequacy of alternate ground motion intensity measures for the estimation of structural responses, Earthquake Engineering and Structural Dynamics, 33(8), 951-79.
15. Gupta, B. & Kunnath, S. K. (2000), Adaptive spectra-based pushover procedure for seismic evaluation of structures, Earthquake Spectra, 16(2), 367-91.
16. Hall, J. F., Heaton, T. H. & Halling, M. W. (1995), Near-source ground motion and its effects on flexible buildings, Earthquake Spectra, 11(4), 569-605.
17. Housner, G. W. (1952), Spectrum intensities of strong motion earthquakes, in Proceedings of the Symposium on Earthquake and Blast Effects on Structures, CA.
18. Housner, G. W. (1975), Measure of severity of earthquake ground shaking, in Proceedings of the U.S. National Conference on Earthquake Engineering, Ann Arbor, MI.
19. Iervolino, I., Chioccarelli, E. & Baltzopoulos, G. (2012), Inelastic displacement ratio of near-source pulse-like ground motions, Earthquake Engineering and Structural Dynamics, 41(15), 2351-57.
20. Jiang, X. & Adeli, H. (2003), Fuzzy clustering approach for accurate embedding dimension identification in chaotic time series, Integrated Computer-Aided Engineering, 10(3), 287-302.
21. Kalkan, E. & Chopra, A. K. (2011), Modal-pushover-based ground-motion scaling procedure, Journal of Structural Engineering, ASCE, 137(3), 298-310.
22. Kennedy, R. P., Short, S. A., Merz, K. L., Tokarz, F. J., Idriss, I. M., Power, M. S. & Sadigh, K. (1984), Engineering Characterization of Ground Motion-Task 1: Effects of Characteristics of Free-Field Motion on Structural Response NUREG/CR-3805, U.S. Regulatory Commission, Washington, DC.
23. Klir, G. J. & Yuan, B. (1995), Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ.
24. Krawinkler, H. & Seneviratna, G. D. P. K. (1998), Pros and cons of a pushover analysis of seismic performance evaluation, Engineering Structures, 20(4-6), 452-64.
25. Lee, J. & Pinheiro dos Santos, W. (2011), An adaptative fuzzy-based system to simulate, quantify, and compensate color blindness, Integrated Computer-Aided Engineering, 18(1), 29-40.
26. Li, H. (2011). Study and uncertainty analysis on seismic performance of concrete structures. Ph.D. thesis, Hunan University, Changsha, China (in Chinese).
27. Lin, L., Naumoski, N., Saatcioglu, M., & Foo, S. (2011), Improved intensity measures for probabilistic seismic demand analysis. Part 1 and Part 2, Canadian Journal of Civil Engineering, 38(1), 79-99.
28. Liu, F. & Er, M. J. (2012), A novel efficient learning algorithm for self-generating fuzzy neural network with applications, International Journal of Neural Systems, 22(1), 21-35.
29. Luco, N. & Cornell, C. A. (2007), Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions, Earthquake Spectra, 23(2), 357-92.
30. Ma, Z. M., Zhang, F., Yan, L. & Cheng, J. (2011), Extracting knowledge from fuzzy relational databases with description logic, Integrated Computer-Aided Engineering, 18(2), 181-200.
31. Makris, N. & Roussos, Y. (1998), Rocking Response and Overturning of Equipment under Horizontal Pulse-Type Motion. PEER Report 1998-05, Available at: http://peer.berkeley.edu/publications/peer_reports/reports_1998/9805.pdf, accessed April, 2012.
32. McGuire, R. K. (2004), Seismic Hazard and Risk Analysis, Earthquake Engineering Research Institute, Oakland, CA.
33. Mehanny, S. S. F. (1999), Modeling and Assessment of Seismic Performance of Composite Frames with Reinforced Concrete Columns and Steel Beams, Stanford University, CA.
34. Mehanny, S. S. F. (2009), A broad-range power-law form scalar-based seismic intensity measure, Engineering Structures, 31(7), 1354-68.
35. Menun, C. & Fu, Q. (2002), An analytical model for near-fault ground motions and the response of SDOF systems, in Proceedings of the 7th US National Conference on Earthquake Engineering, Boston, MA.
36. Montejo, L. A. & Kowalsky, M. J. (2008), Estimation of frequency-dependent strong motion duration via wavelets and its influence on nonlinear seismic response, Computer-Aided Civil and Infrastructure Engineering, 23(4), 253-64.
37. Padgett, J. E., Nielson, B. G. & DesRoches, R. (2008), Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios, Earthquake Engineering & Structural Dynamics, 37(5), 711-25.
38. Panakkat, A. & Adeli, H. (2007), Neural network models for earthquake magnitude prediction using multiple seismicity indicators, International Journal of Neural Systems, 17(1), 13-33.
39. Panakkat, A. & Adeli, H. (2008), Recent efforts in earthquake prediction (1990-2007), Natural Hazards Review, 9(2), 70-80.
40. Panakkat, A. & Adeli, H. (2009), Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators, Computer-Aided Civil and Infrastructure Engineering, 24(4), 280-92.
41. Park, Y. J., Ang, A. H. S. & Wen, Y. K. (1985), Seismic damage analysis of reinforced-concrete buildings, Journal of Structural Engineering, 111(4), 740-57.
42. Reinhorn, A. M. (2006), IDARC 2D Version 6.1 User's Guide. Available at: http://civil.eng.buffalo.edu/idarc2d50/, accessed April, 2013.
43. Reuter, U. (2011), A fuzzy approach for modelling non-stochastic heterogeneous data in engineering based on cluster analysis, Integrated Computer-Aided Engineering, 18(3), 281-89.
44. Ruiz-García, J. (2011), Inelastic displacement ratios for seismic assessment of structures subjected to forward-directivity near-fault ground motions, Journal of Earthquake Engineering, 15(3), 449-68.
45. Samant, A. & Adeli, H. (2001), Enhancing neural network traffic incident detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
46. Sarma, K.C. & Adeli, H. (2002), Life-cycle cost optimization of steel structures, International Journal for Numerical Methods in Engineering, 55(12), 1451-62.
47. Scheffe, H. (1999). The Analysis of Variance, Wiley-Interscience, Classics Edition, New York, NY.
48. Shome, N., Cornell, C. A., Bazzurro, P., & Carballo, J. E. (1998), Earthquakes, records, and nonlinear responses, Earthquake Spectra, 14(3), 469-500.
49. Sirca, G. F., Jr. & Adeli, H. (2004), A neural network-wavelet model for generating artificial accelerograms, International Journal of Wavelets, Multiresolution, and Information Processing, 2(3), 217-35.
50. Somerville, P. G., Smith, N. F., Graves, R. W. & Abrahamson, N. A. (1997), Modification of empirical strong motion attenuation relations to include the amplitude and duration effect of rupture directivity, Seismological Research Letters, 68(1), 199-222.
51. Subramanian, K. & Suresh, S. (2012), Human action recognition using meta-cognitive neuro-fuzzy inference system, International Journal of Neural Systems, 22(6), 1-15.
52. Sucuoglu, H. & Nurtug, A. (1995), Earthquake ground motion characteristics and seismic energy-dissipation, Earthquake Engineering and Structural Dynamics, 24(9), 1195-1213.
53. Tan, P., Tan, Z. K. & Zhou, F. L. (2008), Investigation on characteristics of near-field ground motion and its elastic and inelastic response spectra, South China Journal of Seismology, 28(2), 1-9 (in Chinese).
54. Theodoridis, D., Boutalis, Y. & Christodoulou, M. (2012), Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hoping, International Journal of Neural Systems, 22(2), 1-16.
55. Tothong, P. & Cornell, C. A. (2008), Structural performance assessment under near-source pulse-like ground motions using advanced ground motion intensity measures, Earthquake Engineering and Structural Dynamics, 37(7), 1013-37.
56. Tothong, P. & Luco, N. (2007), Probabilistic seismic demand analysis using advanced ground motion intensity measures, Earthquake Engineering and Structural Dynamics, 36(13), 1837-60.
57. Trifunac, M. D., Ivanovic, S. S. & Todorovska, M. I. (2001), Apparent periods of a building. I and II, Journal of Structural Engineering, 127(5), 517-37.
58. Vamvatsikos, D. & Cornell, C. A. (2002), Incremental dynamic analysis, Earthquake Engineering and Structural Dynamics, 31(3), 491-514.
59. Vamvatsikos, D. & Cornell, C. A. (2005), Developing efficient scalar and vector intensity measures for IDA capacity estimation by incorporating elastic spectral shape information, Earthquake Engineering and Structural Dynamic, 34(13), 1573-1600.
60. Wei, T., Zhao, F. X. & Zhang, Y. S. (2006), Characteristics of near-fault ground motion containing velocity pulses, Acta Seismologica Sinica, 28(6), 629-37.
61. Yan, L. & Ma, Z. M. (2012a), Incorporating fuzzy information into the formal mapping from web data model to extended entity-relationship model, Integrated Computer-Aided Engineering, 19(4), 313-30.
62. Yan, L. & Ma, Z.M. (2012b), Comparison of entity with fuzzy data types in fuzzy object-oriented databases, Integrated Computer-Aided Engineering, 19(2), 199-212.
63. Yi, W. J., Zhang, H. Y. & Kunnath, S. K. (2007), Probabilistic constant-strength ductility demand spectra, Journal of Structural Engineering, 133(4), 567-75.
64. Yuen, K.V. & Mu, H.Q. (2011), Peak ground acceleration estimation by linear and nonlinear models with reduced order Monte Carlo simulation, Computer-Aided Civil and Infrastructure Engineering, 26(1), 30-47.
65. Zhou, Z. & Adeli, H. (2003a), Time-frequency signal analysis of earthquake records using Mexican hat wavelets, Computer-Aided Civil and Infrastructure Engineering, 18(5), 379-89.
66. Zhou, Z. & Adeli, H. (2003b), Wavelet energy spectrum for time-frequency localization of earthquake energy, International Journal of Imaging Systems and Technology, 13(2), 133-40.