پیش‌بینی پاسخ لرزه‌ای خاک‌ریزها و سدهای خاکی با شبکه عصبی مصنوعی

نوع مقاله : Articles

نویسندگان

1 دانشکده عمران، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی، تهران، ایران

2 پژوهشکده مهندسی ژئوتکنیک، پژوهشگاه بین‌المللی زلزله‌شناسی و مهندسی زلزله، تهران، ایران

3 پژوهشکده مدیریت خطرپذیری و بحران، پژوهشگاه بین‌المللی زلزله‌شناسی و مهندسی زلزله، تهران، ایران

چکیده

مهندسی ژئوتکنیک لرزه‌ای غالباً به‌عنوان یک شاخه علمی غیردقیق به حساب می‌آید، زیرا در طی مراحل طراحی سازه‌های ژئوتکنیکی با عدم قطعیت‌های اجتناب‌ناپذیر و ساده‌سازی‌هایی مواجه هستیم که ناچاریم آنها را بپذیریم. بنابراین پیش‌بینی‌های نسبتاً دقیق با استفاده از تکنیک‌های محاسبات نرم (SC) پیشرفته می‌تواند دشواری‌های کار در روش‌های حل متعارف را هموار کند. شبکه‌های عصبی مصنوعی (ANN) یکی از مشهورترین تکنیک‌های محاسبات نرم است که در زمینه‌های مختلف علمی و فناوری استفاده می‌شود. کاربرد این تکنیک در زمینه‌های کاربردی مهندسی زلزله در سازه‌ها نیز هم‌زمان با توسعه آن در سایر زمینه‌های علمی افزایش یافته است. این مقاله روی کاربرد شبکه عصبی مصنوعی بر روی شبیه‌سازی پاسخ لرزه‌ای خاک‌ریزها و سدهای خاکی متمرکز شده است. پاسخ دینامیکی خاک‌ریزها و سدهای خاکی با استفاده از روش اجزای محدود و با استفاده از مدل معادل خطی ارزیابی شده است. در پژوهش حاضر، این فرآیند نسبتاً زمان‌بر با پیش‌بینی‌های سریع شبکه‌های عصبی مصنوعی که به‌طور صحیح آموزش دیده است جایگزین شده است. در اینجا ورودی‌های مدل شبکه عصبی مصنوعی پارامترهای لرزه‌ای تحریک‌های زلزله وارد بر خاک‌ریز یا سد خاکی بوده و خروجی آن در این پژوهش شتاب افقی حداکثر تاج سد است. بررسی‌های انجام شده در این پژوهش نشان داد که مدل شبکه عصبی مصنوعی کلی ارائه شده جهت پیش‌بینی پاسخ لرزه‌ای خاک‌ریزها و سدهای خاکی می‌تواند نسبت به مدل جزئی پیشنهاد شده در پژوهش‌های پیشین کاربردی‌تر باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting the Seismic Response of Embankments and Earth Dams with Artificial Neural Network

نویسندگان [English]

  • Amin Rezaeian 1
  • Mohammad Davoodi 2
  • Mohammad Kazem Jafari 3
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Geotechnical Engineering Research Center, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran
3 Earthquake Risk Management Research Center, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran
چکیده [English]

The artificial neural network (ANN), one of the most powerful tools of artificial intelligence, was used to control the seismic responses of the cross section, and to predict earth dam seismic responses rather than utilizing dynamic time-based analyses. In terms of the artificial neural network, apart from the aforementioned ANN-based applications in various engineering problems, an increasing number of articles have been published over the last decade where the efficient implementation of ANNs in geotechnical earthquake engineering is presented. Most of these studies focus on liquefaction potential under seismic excitations, which is an extremely computationally intensive issue and therefore suitable for ANNs. In some of the studies in this field, the applicability of ANNs in soil dynamic analysis was examined. In addition to the generation of spectrum compatible accelerograms, prediction of earthquake parameters, wave propagation approximations, estimation of peak ground acceleration using microtremors, and the detection of earthquake electric field patterns. Not much has been done into predicting the seismic responses of embankments and earth dams using artificial neural networks tools. The first study on the domain was carried out by Tsompanakis et al. [1] in 2009 in which using the artificial neural network, a seismic response (maximum horizontal acceleration) of homogeneous and symmetric embankment in different locations was predicted. Although being a case study on a simple embankment, earthquake records (accelerograms) were used, which contained a variety of frequency concepts, simplicity or complexity, and various PGA levels, in addition to a general neural network that could be used in all PGA levels the seismic response was not accurately predicted. The objective of the present study was to investigate the ability of the artificial neural network to reach the dynamic response of the embankment and earth dams under seismic loads and different levels of intensity. In other words, it was planned to reduce the heavy cost of calculating the applicable issue in seismic geotechnical engineering. To fulfill the aim, a comprehensive study was carried out and various parameters were evaluated. According to the results, the artificial neural network could reach a good approximation of the seismic response of embankments and earth dams. Although comparing the results of artificial neural networks segmented from generic artificial neural network showed the accuracy of the estimated artificial neural network structured for each PGA level, especially in the linear region, the study was successful in eliminating the defects of previous research, making a perfect arrangement for a generic artificial neural network to be used for this type of neural network to achieve a favorable estimation of all PGA levels. Finally, it can be concluded that considering the complexities of the problem and the performance of the artificial neural networks in solving them, as well as the history of successful performance reports on this tool in other similar applications, the application of Artificial Neural Networks proved useful for estimating and predicting seismic responses of embankments and earth dams. Using artificial neural networks to meet this purpose would reduce the cost of calculating the seismic response assessment of embankments and earth dams. Therefore, applying this tool in the complicated field of seismic geotechnical engineering may be highly promising. Overall, future developments in this research field and promotions in artificial neural network training in other situations by changing the dimensions and properties of materials and load conditions would realize this idea and create a more general neural network for this application.
Reference
1. Tsompanakis, Y., Lagaros, N.D., and Stavroulakis, G.E. (2008) Soft computing techniques in parameter identification and probabilistic seismic analysis of structures. Advances in Engineering Software, 39(7), 612–624.

کلیدواژه‌ها [English]

  • Embankment
  • Earth dams
  • Dynamic Analysis
  • linear equivalent method
  • Artificial neural network
1.    Tsompanakis, Y., Lagros, N., Psarropoulos, P., and  Georgopoulos, E. (2009) Simulating the seismic response of embankments via artificial neural. Advances in Engineering Software, 40(8), 640-651.
2.    Lagaros, N.D. and Tsompanakis, Y. (eds.) (2006) Intelligent Computational Paradigms in Earthquake Engineering. Idea Publishers.
3.    Papadrakakis, M., Lagaros, N.D., and Tsompanakis, Y. (1998) Structural optimization using evolution strategies and neural networks. Computer Methods in Applied Mechanics and Engineering, 156(1-4), 309-333.
4.    Tsompanakis, Y., Lagaros, N.D., and Stavroulakis, G.E. (2008) Soft computing techniques in parameter identification and probabilistic seismic analysis of structures. Advances in Engineering Software, 39(7), 612-624.
5.    Stavroulakis, G.E., Foutsitzi, G., Hadjigeorgiou, E., Marinova, D., and Baniotopoulos, C.C. (2005) Design and robust optimal control of smart beams with application on vibration suppression. Advances in Engineering Software, 36(11-12), 806-813.
6.    Gonzlez, M.P. and Zapico, J.L. (2008) Seismic damage identification in buildings using neural networks and modal data. Computers and Structures, 86(3-5), 416-426.
7.    Fang, X. and Luo, H., and Tang, J. (2005) Structural damage detection using neural network with learning rate improvement. Computers and Structures, 83(25-26), 2150-2161.
8.    Kao, C.Y. and Shih-Lin, H. (2003) Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Computers and Structures, 81(28-29), 2631-2644.
9.    Kallassy, A. (2003) A new neural network for response estimation. Computers and Structures, 81(26-27), 2417-2429.
10.    Chen, Q., Chan, Y.W., and Worden, K. (2003) Structural fault diagnosis and isolation using neural networks based on response-only data. Computers and Structures, 81(22-23), 2165-2172.
11.    Kuz´niar, K. and Waszczyszyn, Z. (2003) Neural simulation of dynamic response of prefabricated buildings subjected to paraseismic excitations. Computers and Structures, 81(24-25), 2353-2360.
12.    Cardoso, J.B., Almeida, J.R., Dias, J.M., and Coelho, P.G. (2008) Structural reliability analysis using Monte Carlo simulation and neural networks. Advances in Engineering Software, 39(6), 505-513.
13.    Chau, K.W. (2007) Reliability and performance-based design by artificial neural network. Advances in Engineering Software, 38(3), 145-149.
14.    Chau, K.W. (2007) Application of a PSO-based neural network in analysis of outcomes of construction claims. Automat Construct, 16(5),   642-646.
15.    Wu, C.L. and Chau, K.W. (2006) A flood forecasting neural network model with genetic algorithm. International Journal Environmental Pollution, 28(3-4), 261-273.
16.    Chouicha, M.A., Siller, T.J., and Charlie, W.A. (1994) An expert-system approach to liquefaction analysis: 2. Evaluation. Computers and Geotechnics, 16(1), 37-69.
17.    Goh, A.T.C. (1994) Seismic liquefaction potential assessed by neural networks. ASCE Journal of Geotechnical Engineering, 120(9), 1467-1480.
18.    Wang, J. and Rahman, M.S. (1999) A neural network model for liquefaction-induced horizontal ground displacement. Soil Dynamics and Earthquake Engineering, 18(8), 555-568.
19.    Baziar, M.H. and Nilipour, N. (2003) Evaluation of liquefaction potential using neural networks and CPT results. Soil Dynamics and Earthquake Engineering, 23(7), 631-636.
20.    Hurtado, J.E., Londono, J.M., and Meza, M.A. (2001) On the applicability of neural networks for soil dynamic amplification analysis. Soil Dynamics and Earthquake Engineering, 21(7), 579-591.
21.    Garcia, S.R., Romo, M.P., and Sarmiento, N. (2002) Modeling ground motion in Mexico City using artificial neural networks. Geofísica Internacional, 42(2), 173-183.
22.    Paolucci, R., Colli, P., and Giacinto, G. (2002) Assessment of seismic site effects in 2-D alluvial valleys using neural networks. Earthquake Spectra, 16(3), 661-680.
23.    Garcia, S.R. and Romo, M.P. (2004) Dynamic soil properties identification using earthquake records:    a NN approximation. Proceedings of the 13th     World Conference on Earthquake Engineering, Vancouver, B.C., Canada, Paper No.1817.
24.    Kerh, T. and 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.
25.    Lin, C.C.J. and Ghaboussi, J. (2001) Generating multiple spectrum compatible accelerograms     using stochastic neural networks. Earthquake Engineering and Structural Dynamics, 30(7), 1021-1042.
26.    Seung, C.L. and Sang, W.H. (2002) Neural-network-based models for generating artificial earthquakes and response spectra. Computers and Structures, 80(20-21), 1627-1038.
27.    Rajasekaran, S. and David, V.K. (2007) MicroARTMAP for pattern recognition problems. Advances in Engineering Software, 38(10), 698-709.
28.    Ziemianski, L. (2003) Hybrid neural network/ finite-element modelling of wave propagation in infinite domains. Computers and Structures,     81(8-11), 1099-1109.
29.    Kerh, T. and Chu, D. (2002) Neural networks approach and microtremor measurements in estimating peak ground acceleration due to         strong motion. Advances in Engineering Software, 33(11-12), 733-742.
30.    Ozerdem, M.S., Ustundag, B., and Demirer, R.M. (2006) Self-organized maps based neural networks for detection of possible earthquake precursory electric field patterns. Advances in Engineering Software, 37(4), 207-217.
31.    Psarropoulos, P.N., Tsompanakis, Y., and Karabatsos, Y. (2007) Effects of local site conditions on the seismic response of municipal solid waste landfills. Soil Dynamics and Earthquake Engineering, 27(6), 553-563.
32.    Newmark, N.M. (1965) Effects of earthquakes on dams and embankments. Geotechnique, 15(2), 139-160.
33.    Makdisi, F.I. and Seed, H.B. (1978) Simplified procedure for estimating dam and embankment earthquake induced deformations. ASCE Journal of the Geotechnical Engineering Division, 104(7), 849-867.
34.    EN 1998-1:2003 (2003) Eurocode 8: Design of Structures for Earthquake Resistance. Part 1: General Rules, Seismic Actions and Rules for Buildings. Commission of the European Communities, European Committee for Standardization.
35.    EAK 2000 (2000) Greek Seismic Design Code. Greek Ministry of Public Works, Athens, Greece.
36.    Kramer, S.L. (1996) Geotechnical Earthquake Engineering. New Jersey, Prentice Hall.
37.    Dynamic Modelling with QUAKE/W 2007 (2007) User Manual Fourth Edition. Geo-Slope International Ltd. Calgary, Alberta, Canada.
38.    Haykin, S. (1999) Neural Networks. Prentice Hall, New Jersey, USA.
39.    MacKay, D.J.C. (1992) A practical Bayesian framework for back prop networks. Neural Computation, 4(3), 448-472.
40.    Schiffmann, W., Joost, M., and Werner, R. (1993) Optimization of the Back-Propagation Algorithm for Training Multi-Layer Perceptrons. Technical Report, University of Koblenz, Institute of Physics.
41.    Riedmiller, M. and Braun, H. (1993) A direct adaptive method for faster back-propagation learning: the RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, 586-591.
42.    Riedmiller, M. (1994) Advanced supervised learning in multi-layer perceptions – from backpropagation to adaptive learning algorithms. International Journal of Computer Standards and Interfaces - Special Issue on Neural Networks, 16, 265-278.