Cluster Analysis of Near-Fault Ground Motions Based on the Intensity of Velocity Pulses
Publication: Journal of Engineering Mechanics
Volume 146, Issue 10
Abstract
In this paper, it is aimed to perform clustering analysis on a large set of near-fault ground motions based on the intensity of velocity pulses. For this purpose, the velocity pulses of these ground motions are extracted based on the asymmetric Gaussian chirplet model (AGCM), the adapted dictionary-free orthogonal matching pursuit (ADOMP) algorithm, and the Newton method. The location of first AGCM atom is considered as the location of real velocity pulse, and its combination with adjacent atoms reconstructs the real velocity pulse of near-fault ground motions. In addition, hierarchical clustering and -means clustering are used to cluster near-fault ground motions into two, three, and five groups based on the features of their velocity pulses. The results showed that the clustering results for two and three groups are more suitable than the results of five groups according to the silhouette and Davies-Bouldin measures. Also, the clustering results of five groups are shown to be appropriate for the design of structures with different importance factors. According to the results, agglomerative clustering with complete linkage provides more appropriate clustering results for moderate and strong pulselike ground motions. The -means and agglomerative clustering with complete linkage provide more uniform clustering results for three and five groups, respectively. A sensitivity analysis showed that the -means provides more stable clustering results when the records for the Chi-Chi earthquake are removed from the data. Also, the clustering results were used to extract the possible relation of the intensity of velocity pulses with the seismological parameters, which were in accordance with the results of previous research.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Alimoradi, A., S. Pezeshk, F. Naeim, and H. Frigui. 2005. “Fuzzy pattern classification of strong ground motion records.” J. Earthquake Eng. 9 (3): 307–332. https://doi.org/10.1080/13632460509350544.
Azarbakht, A., and Z. Minaei. 2017. “Adaptive fuzzy C-mean clustering of ground motion prediction equations.” J. Seismolog. Earthquake Eng. 19 (1): 25–38.
Baker, J. W. 2007. “Quantitative classification of near-fault ground motions using wavelet analysis.” Bull. Seismol. Soc. Am. 97 (5): 1486–1501. https://doi.org/10.1785/0120060255.
Benvegna, F., G. L. Bosco, and D. Tegolo. 2013. “Dissimilarity measures for the identification of earthquake focal mechanisms.” In Proc., Int. Conf. on Image Analysis and Processing. Berlin: Springer.
Boßmann, F., and J. Ma. 2015. “Asymmetric chirplet transform for sparse representation of seismic data.” Geophysics 80 (6): Wd89–Wd100. https://doi.org/10.1190/geo2015-0063.1.
Boßmann, F., and J. Ma. 2016. “Asymmetric chirplet transform. Part 2: Phase, frequency, and chirp rate.” Geophysics 81 (6): V425–V439. https://doi.org/10.1190/geo2015-0696.1.
Bragato, P. L., G. Laurenzano, and C. Barnaba. 2007. “Automatic zonation of urban areas based on the similarity of H/V spectral ratios.” Bull. Seismol. Soc. Am. 97 (5): 1404–1412. https://doi.org/10.1785/0120060245.
Brun, M., C. Sima, J. Hua, J. Lowey, and B. Carroll. 2007. “Model-based evaluation of clustering validation measures.” Pattern Recognit. 40 (3): 807–824. https://doi.org/10.1016/j.patcog.2006.06.026.
Chang, Z., X. Sun, C. Zhai, J. X. Zhao, and L. Xie. 2016. “An improved energy-based approach for selecting pulse-like ground motions.” Earthquake Eng. Struct. Dyn. 45 (14): 2405–2411. https://doi.org/10.1002/eqe.2758.
Chen, G., S. A. Jaradat, N. Banerjee, T. S. Tanaka, M. S. H. Ko, and M. Q. Zhang. 2002. “Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data.” Stat. Sin. 12 (1): 241–262.
Dabaghi, M., and A. Der Kiureghian. 2014. Stochastic modeling and simulation of near-fault ground motions for performance-based earthquake engineering. Berkeley, CA: Pacific Earthquake Engineering Research Center, Univ. of California.
Davies, D. L., and D. W. Bouldin. 1979. “A cluster separation measure.” IEEE Trans. Pattern Anal. Mach. Intell. 1 (2): 224–227. https://doi.org/10.1109/TPAMI.1979.4766909.
Demirli, R., and J. Saniie. 2014. “Asymmetric Gaussian chirplet model and parameter estimation for generalized echo representation.” J. Franklin Inst. 351 (2): 907–921. https://doi.org/10.1016/j.jfranklin.2013.09.028.
Ding, Y., Y. Peng, and J. Li. 2018. “Cluster analysis of earthquake ground-motion records and characteristic period of seismic response spectrum.” J. Earthquake Eng. 24 (6): 1012–1033. https://doi.org/10.1080/13632469.2018.1453420.
Fomel, S., and Y. Liu. 2010. “Seislet transform and seislet frame.” Geophysics 75 (3): V25–V38. https://doi.org/10.1190/1.3380591.
Hayden, C. P., J. D. Bray, and N. A. Abrahamson. 2014. “Selection of near-fault pulse motions.” J. Geotech. Geoenviron. Eng. 140 (7): 04014030. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001129.
Kardoutsou, V., I. Taflampas, and I. N. Psycharis. 2017. “A new pulse indicator for the classification of ground motions.” Bull. Seismol. Soc. Am. 107 (3): 1356–1364. https://doi.org/10.1785/0120160301.
Kaufman, L., and P. J. Rousseeuw. 2009. Vol. 344 of Finding groups in data: An introduction to cluster analysis. New York: Wiley.
Kuyuk, H. S., E. Yildirim, E. Dogan, and G. Horasan. 2012. “Application of -means and Gaussian mixture model for classification of seismic activities in Istanbul.” Nonlinear Processes Geophys. 19 (4): 411–419. https://doi.org/10.5194/npg-19-411-2012.
Lu, Y., and M. Panagiotou. 2013. “Characterization and representation of near-fault ground motions using cumulative pulse extraction with wavelet analysis.” Bull. Seismol. Soc. Am. 104 (1): 410–426. https://doi.org/10.1785/0120130031.
MacQueen, J. 1967. “Some methods for classification and analysis of multivariate observations.” In Vol. 1 of Proc., 5th Berkeley Symp. on Mathematics, Statistics and Probability, 281–296. Colombo, Sri Lanka: Western Management Science Institute.
Mavroeidis, G. P., and A. S. Papageorgiou. 2003. “A mathematical representation of near fault ground motions.” Bull. Seismol. Soc. Am. 93 (3): 1099–1131. https://doi.org/10.1785/0120020100.
Mena, B., and P. M. Mai. 2011. “Selection and quantification of near-fault velocity pulses owing to source directivity.” Georisk 5 (1): 25–43. https://doi.org/10.1080/17499511003679949.
Morales-Esteban, A., F. Martínez-Álvarez, A. Troncoso, J. L. Justo, and C. Rubio-Escudero. 2010. “Pattern recognition to forecast seismic time series.” Expert Syst. Appl. 37 (12): 8333–8342. https://doi.org/10.1016/j.eswa.2010.05.050.
Morikawa, H., and S. Udagawa. 2009. “A method to estimate the phase velocities of microtremors using a time-frequency analysis and its applications.” Bull. Seismol. Soc. Am. 99 (2A): 774–793. https://doi.org/10.1785/0120080100.
Mukhopadhyay, S., and V. K. Gupta. 2013. “Directivity pulses in near-fault ground motions. I: Identification, extraction and modeling.” Soil Dyn. Earthquake Eng. 50 (Jul): 1–15. https://doi.org/10.1016/j.soildyn.2013.02.017.
Pal, N. R., and J. Biswas. 1997. “Cluster validation using graph theoretic concepts.” Pattern Recognit. 30 (6): 847–857. https://doi.org/10.1016/S0031-3203(96)00127-6.
Panella, D. S., M. E. Tornello, and C. D. Frau. 2017. “A simple and intuitive procedure to identify pulse-like ground motions.” Soil Dyn. Earthquake Eng. 94 (Mar): 234–243. https://doi.org/10.1016/j.soildyn.2017.01.020.
PEER (Pacific Earthquake Engineering Research). 2014. “PEER ground motion database: NGA-West2.” Accessed November 21, 2019. http://ngawest2.berkeley.edu/.
Pollard, K. S., and M. J. Vander Laan. 2002. “A method to identify significant clusters in gene expression data.” In Proc., U.C. Berkeley Division of Biostatistics Working Paper Series, 107. Berkeley, CA: Univ. of California.
Ramdani, F., O. Kettani, and B. Tadili. 2015. “Evidence for subduction beneath Gibraltar Arc and Andean regions from -means earthquake centroids.” J. Seismolog. 19 (1): 41–53. https://doi.org/10.1007/s10950-014-9449-9.
Rehman, K., P. W. Burton, and G. A. Weatherill. 2014. “K-means cluster analysis and seismicity partitioning for Pakistan.” J. Seismolog. 18 (3): 401–419. https://doi.org/10.1007/s10950-013-9415-y.
Ristevski, B., S. Loshkovska, S. Dzeroski, and I. Slavkov. 2008. “A comparison of validation indices for evaluation of clustering results of DNA microarray data.” In Proc., 2nd Int. Conf. on Bioinformatics and Biomedical Engineering, 587–591. New York: IEEE.
Rousseeuw, P. 1987. “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” J. Comput. Appl. Math. 20 (1): 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
Shahi, S. K., and J. W. Baker. 2014. “An efficient algorithm to identify strong-velocity pulses in multicomponent ground motions.” Bull. Seismol. Soc. Am. 104 (5): 2456–2466. https://doi.org/10.1785/0120130191.
Sharbati, R., R. Rahimi, M. R. Koopialipoor, N. Elyasi, F. Khoshnoudian, H. R. Ramazi, and H. R. Amindavar. 2020. “Detection and extraction of velocity pulses of near-fault ground motions using asymmetric Gaussian chirplet model.” Soil Dyn. Earthquake Eng. 133 (Jun): 106123. https://doi.org/10.1016/j.soildyn.2020.106123.
Somerville, P. G., N. F. Smith, R. W. Graves, and N. A. Abrahamson. 1997. “Modification of empirical strong ground motion attenuation relations to include the amplitude and duration effects of rupture directivity.” Seismol. Res. Lett. 68 (1): 199–222.
Tan, P. N., M. Steinbach, and V. Kumar. 2006. Introduction to data mining. Boston: Addison Wesley.
Ward, J. R., and H. Joe. 1963. “Hierarchical grouping to optimize an objective function.” J. Am. Stat. Assoc. 58 (301): 236–244. https://doi.org/10.1080/01621459.1963.10500845.
Wu, R., J. Luo, and B. Wu. 2014. “Seismic envelope inversion and modulation signal model.” Geophysics 79 (3): WA13–WA24. https://doi.org/10.1190/geo2013-0294.1.
Yaghmaei-Sabegh, S. 2010. “Detection of pulse-like ground motions based on continues wavelet transform.” J. Seismolog. 14 (4): 715–726. https://doi.org/10.1007/s10950-010-9193-8.
Yaghmaei-Sabegh, S. 2017. “A novel approach for classification of earthquake ground-motion records.” J. Seismolog. 21 (4): 885–907. https://doi.org/10.1007/s10950-017-9642-8.
Zamora, M., and R. Riddell. 2011. “Elastic and inelastic response spectra considering near-fault effects.” J. Earthquake Eng. 15 (5): 775–808. https://doi.org/10.1080/13632469.2011.555058.
Zhai, C., Z. Chang, S. Li, Z. Chen, and L. Xie. 2013. “Quantitative identification of near-fault pulse-like ground motions based on energy.” Bull. Seismol. Soc. Am. 103 (5): 2591–2603. https://doi.org/10.1785/0120120320.
Zhai, C., C. Li, S. Kunnath, and W. Wen. 2018. “An efficient algorithm for identifying pulse-like ground motions based on significant velocity half-cycles.” Earthquake Eng. Struct. Dyn. 47 (3): 757–771. https://doi.org/10.1002/eqe.2989.
Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 146 • Issue 10 • October 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Nov 29, 2019
Accepted: May 26, 2020
Published online: Jul 30, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 30, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.