Estimation of the Peak Ground Acceleration using support vector machine and neural radius-based function network models

Document Type : Original Article

Authors

1 َAssistant Professor, Department of Civil Engineering, University of Ayatollah Boroujerdi

2 MSc student, in engineering, Department of Civil Engineering, University of Ayatollah Boroujerdi

Abstract

Prediction of the ground strong motion parameters is one way to evaluate the various earthquakes and to determine the amount of risk in each area which plays an important role in the evaluation of earthquake effects on the engineering projects design. In this study, the support vector machine (SVM) and neural radius-based function (RBF) network models as new artificial intelligence techniques were used to estimate the peak ground acceleration (PGA). For this purpose, the seismic parameters such as the magnitude, epicentral distance, focal depth, earthquake intensity were applied as input parameters of proposed models. Evaluation of obtained results for the estimation of PGA using the SVM and RBF models with empirical attenuation relationships and regression methods indicated that the presented SVM and RBF models can establish an appropriated relationship between the observed and calculated PGA values. Also, proposed models have more accuracy than classical approaches. The determination coefficient is 0.996 and 0.997 for SVM and RBF models, respectively where as the determination coefficient is 0.790 and 0.153 for linear regression and nonlinear regression, respectively.

Keywords

References

[1] برگی، خ. (1388). "اصول مهندسی زلزله"، چاپ چهارم، تهران: مؤسسه انتشارات دانشگاه تهران.
[2] بخشی، ح.، خراسانی، م.، فدوی، م.، قدرتی امیری، غ.، برخورداری، م. (1388). "تخمین پارامترهای شتاب، سرعت و جابه‌جایی ماکزیمم زمین با استفاده از شبکه عصبی مصنوعی"، مجله مدل‌سازی در مهندسی، دوره 7، شماره19، ش.ص. 11-21.
[3] شکیب، ح.، علیرضایی، م. (1390). "اصول مهندسی زلزله"، چاپ اول، تهران: انتشارات آذرین مهر.
[4] Liu, B.Y., Ye, L.Y., Xiao, M.L., & Miao, S. (2006). “Peak Ground Velocity Evaluation by Artificial Neural Network for West America Region”, In International Conference on Neural Information Processing, 942-951.
[5] Arjun, C.R., & Kumar, A. (2009). “Artificial neural network-based estimation of peak ground acceleration”, ISET J. Earthq. Technol, 46(1), 19-28.
[6] Derras, B., & Bekkouche, A. (2011). “Use of the Artificial Neural Network for Peak Ground Acceleration estimation”, Lebanese Science Journal, 12(2), 101-115.
[7] 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, 18(7), 857–866.
[8] Pozos, A., Gomez, R., & Hong, H.P. (2014). “Use of Neural network to predict the peak ground accelerations and pseudo spectral accelerations for Mexican Inslab and Interplate Earthquakes”, Geofísica internacional, 53(1), 39-57.
[9] Barrile, I., Cacciola, M., D’Amico, S., Greco, A., Morabito, F.C., & Parrillo, F. (2006). “Radial Basis Function Neural Networks to Foresee Aftershocks in Seismic Sequences Related to Large Earthquakes”, In International Conference on Neural Information Processing, ICONIP 2006, 909–916.
[10] Han, D., Chan, L., & Zhu, N. (2007). “Flood forecasting using Support Vector Machines”, Journal of Hydroinformatics, 267-276.
[11] Chen, C. S., Cheng, M. Y., & Wu, Y. W.  (2012). “Seismic assessment of school buildings in Taiwan using the evolutionary support vector machine inference system”, Expert Systems with Applications, 39(4), 4102-4110.
[12] Nasrollahnejhad, A., Yari, A., Zahedian, S., & Hoodeh, H. (2013). “Simulating peak ground acceleration by general regression and radial basis function and other neural networks in some regions of the world”, Computer Engineering and Intelligent Systems. Journal, 4(1), 1-6,
[13] Vapnik, V. N. (1995). “The Nature of Statistical Learning Theory”, Springer, New York.
[14] Cortes, C., & Vapnik, V. (1995). “Support vector networks”, Mach, Learn. 20(3), 273-297.
[15] Lee, C.Y., & Chern, S.G. (2013). “Application of a Support Vector Machine for liquefaction assessment”, Journal of Marine Science and Technology, 21(3), 318-324.
[16] وزیری، م. (1395). "ارائه روش تخمین موقعیت برای محیط درونی با استفاده از شبکه عصبی شعاعی محور"، پایان‌نامه کارشناسی ارشد، دانشکده کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر.
[17] Nourani, V., & Komasi, M. (2013). “A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process”, Journal of Hydrology, 490, 41–55.
[18] مقدم، ح.، فنایی، ن. (1385). "بررسی روابط کاهندگی مختلف در پیش‌بینی شتاب زمین لرزه سیلاخور"، دانشکده مهندسی عمران، دانشگاه صنعتی شریف، شماره 35.
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics