An Artificial Neural Network for Predicting the Near-fault Directivity-pulse Period

References

• Abrahamson, N. A. 2000. Effects of rupture directivity on probabilistic seismic hazard analysis. In Proceedings of the 6th international conference on seismic zonation, Vol. 1, CA: Palm Springs, pp. 151–56.
   Google Scholar
• Adeli, H. 2001. Neural networks in civil engineering: 1989–2000. Computer‐Aided Civil and Infrastructure Engineering 16 (2): 126–42.
   Web of Science ®Google Scholar
• Alavi, A. H., and A. H. Gandomi. 2011. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Computers & Structures 89: 2176–94.
   Web of Science ®Google Scholar
• Alavi, B., and H. Krawinkler. 2000. Design considerations for near-fault ground motions. In Proceedings of the US–Japan Workshop on the Effects of Near-Fault Earthquake Shaking, pp. 20–21. San Francisco, CA.
   Google Scholar
• Allmann, B. P., and P. M. Shearer. 2009. Global variations of stress drop for moderate to large earthquakes. Journal of Geophysical Research: Solid Earth 114 (B1), pp. 1–22.
   Google Scholar
• Ancheta, T. D., R. B. Darragh, J. P. Stewart, E. Seyhan, W. J. Silva, B. S.-J. Chiou, K. E. Wooddell, R. W. Graves, A. R. Kottke, D. M. Boore, et al. 2013. PEER NGA-West2 Database. PEER Report No. 2013/03, Pacific Earthquake Engineering Research Center, University of California, Berkeley, 134 pp.
   Google Scholar
• Archer, E., I. M. Park, and J. Pillow. 2013. Bayesian and Quasi-Bayesian estimators for mutual information from discrete data. Entropy 15 (12): 1738–55.
   Google Scholar
• Baker, J. W. 2007. Quantitative classification of near-fault ground motions using wavelet analysis. Bulletin of the Science Society of America 97 (5): 1486–501.
   Google Scholar
• Battiti, R. 1994. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks 5 (4): 537–50.
   PubMed Web of Science ®Google Scholar
• Bishop, C. M. 1995. Neural networks for pattern recognition. New York, NY: Oxford university press.
   Google Scholar
• Bozorgnia, Y., N. A. Abrahamson, L. A. Atik, T. D. Ancheta, G. M. Atkinson, J. W. Baker, … R. Darragh. 2014. NGA-West2 research project. Earthquake Spectra 30 (3): 973–87.
   Web of Science ®Google Scholar
• Bray, J. D., and A. Rodriguez-Marek. 2004. Characterization of forward-directivity ground motions in the near-fault region. Soil Dynamics and Earthquake Engineering 24 (11): 815–28.
   Web of Science ®Google Scholar
• Bray, J. D., A. Rodriguez-Marek, and J. L. Gillie. 2009. Design ground motions near active faults. Bulletin of the New Zealand Society for Earthquake Engineering 42 (1): 1.
   Google Scholar
• Chen, K. Y. 2007. Forecasting systems reliability based on support vector regression with genetic algorithms. Reliability Engineering & System Safety 92: 423–32.
   Web of Science ®Google Scholar
• Chen, K. Y., and C. H. Wang. 2007. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management 28: 215–26.
   Web of Science ®Google Scholar
• Cherkassky, V., and Y. Ma. 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17: 113–26.
   PubMed Web of Science ®Google Scholar
• Cork, T. G., J. H. Kim, G. P. Mavroeidis, J. K. Kim, B. Halldorsson, and A. S. Papageorgiou. 2016. Effects of tectonic regime and soil conditions on the pulse period of near-fault ground motions. Soil Dynamics and Earthquake Engineering 80: 102–18.
   Web of Science ®Google Scholar
• Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20: 273–97.
   Web of Science ®Google Scholar
• Costarelli, D., and R. Spigler. 2013. Multivariate neural network operators with sigmoidal activation functions. Neural Networks 48: 72–77.
   PubMed Web of Science ®Google Scholar
• Cotton, F., and M. Campillo. 1994. Application of seismogram synthesis to the study of earthquake source from strong motion records. Annals of Geophysics 37 (6): 1539–64.
   Google Scholar
• Dabaghi, M., and A. Der Kiureghian. 2017. Stochastic model for simulation of near‐fault ground motions. Earthquake Engineering & Structural Dynamics 46 (6): 963–84.
   Web of Science ®Google Scholar
• Derras, B., F. Cotton, S. Drouet, and P. Y. Bard 2017. Magnitude dependence of stress drop: What does the observed magnitude scaling of ground motions tell us? In Sixteenth World Conference on Earthquake Engineering, pp. 4505. Santiago, Chile.
   Google Scholar
• Duan, W. Y., Y. Han, L. M. Huang, B. B. Zhao, M. H. Wang. 2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering. 124: 54–73.
   Google Scholar
• Fausett, L. V. 1994. Fundamentals of neural networks: Architectures, algorithms, and applications. Englewood Cliffs: Prentice-Hall.
   Google Scholar
• Fayjaloun, R., M. Causse, C. Voisin, C. Cornou, and F. Cotton. 2016. Spatial Variability of the Directivity Pulse Periods Observed during an Earthquake. Bulletin of the Science Society of America 107 (1): 308–18.
   Google Scholar
• Fraser, A. M., and H. L. Swinney. 1986. Independent coordinates for strange attractors from mutual information. Physical Review. A 33: 1134.
   PubMed Web of Science ®Google Scholar
• Frénay, B., G. Doquire, and M. Verleysen. 2013. Is mutual information adequate for feature selection in regression? Neural Networks 48: 1–7.
   PubMed Web of Science ®Google Scholar
• Gandomi, A. H., and D. A. Roke 2013. Intelligent formulation of structural engineering systems. In: Seventh MIT Conference on Computational Fluid and Solid Mechanics–Focus: Multiphysics & Multiscale, Massachusetts Institute of Technology Cambridge, MA, pp. 12–14.
   Google Scholar
• Gandomi, A. H., and D. A. Roke. 2015. Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software 88: 63–72.
   Web of Science ®Google Scholar
• Gandomi, M., M. Soltanpour, M. R. Zolfaghari, and A. H. Gandomi. 2016. Prediction of peak ground acceleration of Iran’s tectonic regions using a hybrid soft computing technique. Geoscience Frontiers 7: 75–82.
   Web of Science ®Google Scholar
• Giraudo, M. T., L. Sacerdote, and R. Sirovich. 2013. Non–parametric estimation of mutual information through the entropy of the linkage. Entropy 15 (12): 5154–77.
   Web of Science ®Google Scholar
• Goh, A. T. 1995. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering 9 (3): 143–51.
   Google Scholar
• Golbraikh, A., and A. Tropsha. 2002. Beware of q2! Journal of Molecular Graphics & Modelling 20: 269–76.
   PubMed Web of Science ®Google Scholar
• Hagan, M. T., H. B. Demuth, M. H. Beale, and O. De-Jesús. 1996. Neural network design. Boston: Pws Pub.
   Google Scholar
• Halldorsson, B., G. P. Mavroeidis, and A. S. Papageorgiou 2003 Estimation of near-fault velocity pulses for intra-plate earthquake sources. Proceedings of the eastern section annual meeting of the Seismological Society of America, Toronto, Canada.
   Google Scholar
• Hastie, T., R. Tibshirani, and J. Friedman. 2009. Unsupervised learning. In The elements of statistical learning, pp. 485–585. New York, NY: Springer.
   Google Scholar
• Haykin, S. 2004. Neural networks: A comprehensive foundation. Neural Networks 2: 41.
   Google Scholar
• Hecht-Nielsen, R.1992. Theory of the backpropagation neural network. In Neural networks for perception, pp. 65–93. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2 (5): 359–66.
   Web of Science ®Google Scholar
• Hung, S. L., and H. Adeli. 1994. Object-oriented backpropagation and its application to structural design. Neurocomputing 6: 45–55.
   Web of Science ®Google Scholar
• Huoh, Y. J. 2013. Sensitivity Analysis of Stochastic Simulators with Information Theory. PhD diss., Berkeley: University of California.
   Google Scholar
• Iervolino, I., and C. A. Cornell. 2008. Probability of occurrence of velocity pulses in near-source ground motions. Bulletin of the Science Society of America 98 (5): 2262–77.
   Google Scholar
• Insom, P., C. Cao, P. Boonsrimuang, D. Liu, A. Saokarn, P. Yomwan, Y. Xu. 2015. A support vector machine-based particle filter method for improved flooding classification. IEEE Geoscience and Remote Sensing Letters. 12: 1943–47.
   Google Scholar
• Joshi, V. A. 2013. Near-fault forward-directivity aspects of strong ground motions in the 2010-11 Canterbury earthquakes. Master’s Thesis, University of Canterbury, Christchurch, New Zealand.
   Google Scholar
• Joyner, W. B., and D. M. Boore. 1981. Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California, earthquake. Bulletin of the Science Society of America 71 (6): 2011–38.
   Google Scholar
• Kanamori, H., and E. E. Brodsky. 2004. The physics of earthquakes. Reports on Progress in Physics 67 (8): 1429.
   Web of Science ®Google Scholar
• Kaneko, Y., and P. M. Shearer. 2014. Seismic source spectra and estimated stress drop derived from cohesive-zone models of circular sub-shear rupture. Geophysical Journal International 197 (2): 1002–15.
   Web of Science ®Google Scholar
• Kardoutsou, V., P. Mimoglou, I. Taflampas, and I. N. Psycharis 2015. Relation of pulse period with near-fault strong motion parameters. Proceedings of the 6th International Conference on Earthquake Geotechnical Engineering, Christchurch, New Zealand.
   Google Scholar
• Kecman, V. 2001. Learning and soft computing: Support vector machines, neural networks, and fuzzy logic models. Cambridge: MIT press.
   Google Scholar
• Keerthi, S. S., and C. J. Lin. 2003. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15: 1667–89.
   PubMed Web of Science ®Google Scholar
• Kraskov, A., H. Stögbauer, and P. Grassberger. 2004. Estimating mutual information. Physical Review E 69 (6): 066138.
   Web of Science ®Google Scholar
• Looney, C. G. 1996. Advances in feedforward neural networks: Demystifying knowledge acquiring black boxes. IEEE Transactions on Knowledge and Data Engineering 8: 211–26.
   Web of Science ®Google Scholar
• Ma, J., and Z. Sun. 2011. Mutual information is copula entropy. Tsinghua Science and Technology 16 (1): 51–54.
   Google Scholar
• Mavroeidis, G. P., and A. S. Papageorgiou. 2003. A mathematical representation of near-fault ground motions. Bulletin of the Science Society of America 93 (3): 1099–131.
   Google Scholar
• McClelland, J. L., D. E. Rumelhart, and P. D. P. Research Group. 1986. Parallel distributed processing. Explorations in the Microstructure of Cognition 2: 216–71.
   Google Scholar
• Mehrotra, K., C. K. Mohan, and S. Ranka. 1997. Elements of artificial neural networks. Cambridge: MIT press.
   Google Scholar
• Negnevitsky, M. 2005. Artificial intelligence: A guide to intelligent systems. Pearson Education Limited, London.
   Google Scholar
• Nejad, M. M., M. S. Momeni, and K. N. Manahiloh. 2018. Shear wave velocity and soil type microzonation using neural networks and geographic information system. Soil Dynamics and Earthquake Engineering 104: 54–63.
   Web of Science ®Google Scholar
• Pham, B. T., D. T. Bui, H. R. Pourghasemi, I. Prakash, M. B. Dholakia. 2017. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology. 128: 255–73.
   Google Scholar
• Rahman, M. S., and J. Wang. 2002. Fuzzy neural network models for liquefaction prediction. Soil Dynamics and Earthquake Engineering 22 (8): 685–94.
   Web of Science ®Google Scholar
• Reyes, J., A. Morales-Esteban, and F. Martínez-Álvarez. 2013. Neural networks to predict earthquakes in Chile. Applied Soft Computing 13: 1314–28.
   Web of Science ®Google Scholar
• Rodriguez-Marek, A., and J. D. Bray. 2006. Seismic site response for near-fault forward directivity ground motions. Journal of Geotechnical and Geoenvironmental Engineering 132 (12): 1611–20.
   Web of Science ®Google Scholar
• Rossi, F., A. Lendasse, D. François, V. Wertz, and M. Verleysen. 2006. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometrics and Intelligent Laboratory Systems 80 (2): 215–26.
   Web of Science ®Google Scholar
• Rowshandel, B. 2006. Incorporating source rupture characteristics into ground-motion hazard analysis models. Seismological Research Letters 77 (6): 708–22.
   Web of Science ®Google Scholar
• Roy, P. P., and K. Roy. 2008. On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science 27: 302–13.
   Google Scholar
• Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1985. Learning internal representations by error propagation. In Parallel Distributed Processing, ed. D. E. Rumelhart and J. L. Mcclelland, 318–62. USA: MIT Press.
   Google Scholar
• Schölkopf, B. 2001. The kernel trick for distances. In Advances in neural information processing systems, 301–07. MIT Press: Cambridge, MA, USA.
   Google Scholar
• Schölkopf, B., P. Bartlett, A. Smola, and R. Williamson 1998. Support vector regression with automatic accuracy control. International Conference on Artificial Neural Networks, Springer-Verlag, Berlin, Germany, pp. 111–16.
   Google Scholar
• Shahi, S. K., and J. W. Baker. 2011. An empirically calibrated framework for including the effects of near-fault directivity in probabilistic seismic hazard analysis. Bulletin of the Science Society of America 101 (2): 742–55.
   Google Scholar
• Shahi, S. K., and J. W. Baker. 2014. An efficient algorithm to identify strong‐velocity pulses in multicomponent ground motions. Bulletin of the Science Society of America 104 (5): 2456–66.
   Google Scholar
• Shang, X., X. Li, A. Morales-Esteban, and G. Chen. 2017. Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis. Soil Dynamics and Earthquake Engineering 99: 142–49.
   Web of Science ®Google Scholar
• Shannon, C., and W. Weaver. 1964. The Mathematical Theory of Communication. Champaign: University Illinois Press.
   Google Scholar
• Shannon, C. E. 1948. A mathematical theory of communication. Bell System Technical Journal 27 (3): 379–423.
   Google Scholar
• Shawe-Taylor, J., and N. Cristianini. 2004. Kernel methods for pattern analysis. New York, NY: Cambridge university press.
   Google Scholar
• Smola, A. J., and B. Schölkopf. 1998. Learning with kernels. Citeseer.Cambridge, MA: MIT Press.
   Google Scholar
• Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14: 199–222.
   Web of Science ®Google Scholar
• Somerville, P., K. Irikura, R. Graves, S. Sawada, D. Wald, N. Abrahamson, … A. Kowada. 1999. Characterizing crustal earthquake slip models for the prediction of strong ground motion. Seismological Research Letters 70 (1): 59–80.
   Google Scholar
• Somerville, P. G.1998. Development of an improved representation of near fault ground motions. SMIP98 Seminar on Utilization of Strong-Motion Data, Oakland, California, pp. 1–20.
   Google Scholar
• Somerville, P. G. 2002. Characterizing near fault ground motion for the design and evaluation of bridges. Proceedings of the Third National Seismic Conference and Workshop on Bridges and Highways, MCEER Buttalo, USA.
   Google Scholar
• Somerville, P. G. 2003. Magnitude scaling of the near fault rupture directivity pulse. Physics of the Earth and Planetary Interiors 137 (1–4): 201–12.
   Web of Science ®Google Scholar
• 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. Seismological Research Letters 68 (1): 199–222.
   Google Scholar
• Spudich, P., J. R. Bayless, J. W. Baker, B. S. Chiou, B. Rowshandel, S. K. Shahi, and P. G. Somerville 2013. Final report of the NGAWest2 directivity working group. PEER Report No. 2013/09, Pacific Earthquake Engineering Research Center, University of California, Berkeley.
   Google Scholar
• Sreenivas, B., B. Kumar, and B. K. Raghu-Prasad. 2008. Investigation of the ductility demand in multi-story buildings subjected to near field ground motions using neural network approach. Journal of Earthquake Engineering 12 (8): 1314–24.
   Web of Science ®Google Scholar
• Sutha, K., and J. J. Tamilselvi. 2015. A review of feature selection algorithms for data mining techniques. International Journal on Computer Science and Engineering 7 (6): 63.
   Google Scholar
• Tang, Y., and J. Zhang. 2011. Response spectrum-oriented pulse identification and magnitude scaling of forward directivity pulses in near-fault ground motions. Soil Dynamics and Earthquake Engineering 31 (1): 59–76.
   Web of Science ®Google Scholar
• Tarbali, K., B. A. Bradley, and J. W. Baker. 2018. Ground motion selection in the near-fault region considering directivity-induced pulse effects. In Earthquake Spectra, 35 (2): 759–786. In-Press.
   Google Scholar
• Thomas, S., G. N. Pillai, K. Pal, and P. Jagtap. 2016. Prediction of ground motion parameters using randomized ANFIS (RANFIS). Applied Soft Computing 40: 624–34.
   Web of Science ®Google Scholar
• Tothong, P., C. A. Cornell, and J. W. Baker. 2007. Explicit directivity-pulse inclusion in probabilistic seismic hazard analysis. Earthquake Spectra 23 (4): 867–91.
   Web of Science ®Google Scholar
• Vafaei, M., A. Adnan, and A. B. Abd-Rahman. 2013. Real-time seismic damage detection of concrete shear walls using artificial neural networks. Journal of Earthquake Engineering 17: 137–54.
   Web of Science ®Google Scholar
• Vapnik, V. 1995. The nature of statistical learning theory. Springer science & business media, Berlin, Heidelberg, Germany.
   Google Scholar
• Vapnik, V., S. E. Golowich, and A. J. Smola. 1997. Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems, Mozer, M.C., Jordan, M.I., Petsche, T., Eds.; Cambridge, MA, USA: MIT Press, pp. 281–87.
   Google Scholar
• Werbos, P. J. 1990. Backpropagation through time: What it does and how to do it. Proceedings of the IEEE 78 (10): 1550–60.
   Web of Science ®Google Scholar
• Yousefi-Dadras, E., A. Yazdani, A. Nicknam, and S. N. Eftekhari. 2018. Incorporating Source Rupture Characteristics into the Near‐Fault Pulse Prediction Model. Bulletin of the Science Society of America 108 (1): 200–09.
   Google Scholar
• Yu, P. S., S. T. Chen, and I. F. Chang. 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328: 704–16.
   Web of Science ®Google Scholar
• Zaidi, S. 2012. Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler. Chemical Engineering Science 69: 514–21.
   Web of Science ®Google Scholar