GA-BP artificial neural networks for predicting the seismic response of arch dams

YU Jingchi; JIN Aiyun; PAN Jianwen; WANG Jinting; ZHANG Chuhan

doi:10.16511/j.cnki.qhdxxb.2022.25.028

PDF(12383 KB)

Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (8) : 1321-1329. DOI: 10.16511/j.cnki.qhdxxb.2022.25.028

Intelligent Prediction and Feedback

GA-BP artificial neural networks for predicting the seismic response of arch dams

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Arch dams may be subjected to strong earthquakes during their lifecycle and their seismic response has attracted extensive attention in dam engineering. Nonlinear finite element seismic response analyses of arch dams require large amounts of computational effort. This paper presents a back propagation (BP) genetic algorithm (GA) method for predict the seismic responses of arch dams which replaces some of the finite element analysis calculations and significantly reduces the computational cost compared with the finite element method. A BP neural network was trained and validated for the Dagangshan arch dam based on 390 nonlinear dynamic response cases calculated using the finite element method with the structural response as the BP neural network output and the seismic intensity parameter, IM, as the input. The results show that the GA-BP neural network can properly predict the dam seismic response and give reasonable seismic response curves using 30% of the 390 cases for training which shows that the GA-BP neural network can save 70% of the nonlinear finite element cost.

Key words

arch dam / seismic fragility analysis / artificial neural network / genetic algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

YU Jingchi, JIN Aiyun, PAN Jianwen, WANG Jinting, ZHANG Chuhan. GA-BP artificial neural networks for predicting the seismic response of arch dams[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1321-1329 https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.028

References

[1] CHEN H J, XU W Y, WU Q X, et al. Reliability analysis of arch dam subjected to seismic loads[J]. Arabian Journal for Science and Engineering, 2014, 39(11):7609-7619.
[2] YAO X W, ELNASHAI A S, JIANG J Q. Analytical seismic fragility analysis of concrete arch dams[C]//Proceedings of the 15th World Conference on Earthquake Engineering. Lisbon, Portugal:WCEE, 2012.
[3] WANG J T, ZHANG M X, JIN A Y, et al. Seismic fragility of arch dams based on damage analysis[J]. Soil Dynamics and Earthquake Engineering, 2018, 109:58-68.
[4] ANDERSON D, MCNEILL G. Artificial neural networks technology[R]. New York:Data & Analysis Center for Software, 1992.
[5] LIU K, GUO W Y, SHEN X L, et al. Research on the forecast model of electricity power industry loan based on GA-BP neural network[J]. Energy Procedia, 2012, 14:1918-1924.
[6] FENG G L, LI L. Application of genetic algorithm and neural network in construction cost estimate[C]//Proceedings of the 2012 2nd International Conference on Computer and Information Application. Paris, France:Atlantis Press, 2012:1036-1039.
[7] 余功栓. 人工智能技术在大坝安全分析中的应用[D]. 杭州:浙江大学, 2004. YU G S. Application of artificial intelligent in safety analysis of embankment[D]. Hangzhou:Zhejiang University, 2004. (in Chinese)
[8] DING S F, SU C Y, YU J Z. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial Intelligence Review, 2011, 36(2):153-162.
[9] MATA J. Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models[J]. Engineering Structures, 2011, 33(3):903-910.
[10] SONG L F, XU B, KONG X J, et al. Reliability analysis of 3D rockfill dam slope stability based on the Copula function[J]. International Journal of Geomechanics, 2021, 21(3):04021001.
[11] LIU J, WANG G Y, CHEN Y. Research and application of GA neural network model on dam displacement forecasting[C]//11th Biennial ASCE Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments. Long Beach, California, USA:ASCE, 2008, 323:1-9.
[12] 苏怀智, 吴中如, 温志萍, 等. 遗传算法在大坝安全监控神经网络预报模型建立中的应用[J]. 水利学报, 2001(8):44-48. SU H Z, WU Z R, WEN Z P, et al. The application of genetic algorithm in establishment of neural network forecast model for dam safety monitoring[J]. Journal of Hydraulic Engineering, 2001(8):44-48. (in Chinese)
[13] MCCULLOCH W S, PITTS W. A logical calculus of the ideas immanent in nervous activity[J]. The Bulletin of Mathematical Biophysics, 1943, 5(4):115-133.
[14] HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the National Academy of Sciences of the United States of America, 1982, 79(8):2554-2558.
[15] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536.
[16] 刘春艳, 凌建春, 蔻林元, 等. GA-BP神经网络与BP神经网络性能比较[J]. 中国卫生统计, 2013, 30(2):173-176, 181. LIU C Y, LING J C, KOU L Y, et al. Performance comparison between GA-BP neural network and BP neural network[J]. Chinese Journal of Health Statistics, 2013, 30(2):173-176, 181. (in Chinese)
[17] 金爱云. 高拱坝地震易损性研究[D]. 北京:清华大学, 2020. JIN A Y. Seismic fragility analysis of high arch dams[D]. Beijing:Tsinghua University, 2020. (in Chinese)
[18] TOTHONG P, LUCO N. Probabilistic seismic demand analysis using advanced ground motion intensity measures[J]. Earthquake Engineering & Structural Dynamics, 2007, 36(13):1837-1860.
[19] HARIRI-ARDEBILI M A, SAOUMA V E. Probabilistic seismic demand model and optimal intensity measure for concrete dams[J]. Structural Safety, 2016, 59:67-85.
[20] ARIAS A. A measure of earthquake intensity[M]//HANSEN R J. Seismic Design for Nuclear Power Plants. Cambridge, MA, USA:MIT Press, 1970:438-483.
[21] 崔恩文. 基于速度谱强度高铁列车地震报警阈值研究[D]. 哈尔滨:中国地震局工程力学研究所, 2014. CUI E W. Study on spectral intensity of speed based earthquake alarm threshold of high speed trains[D]. Harbin:China Earthquake Administration, 2014. (in Chinese)
[22] YANG D X, PAN J W, LI G. Non-structure-specific intensity measure parameters and characteristic period of near-fault ground motions[J]. Earthquake Engineering & Structural Dynamics, 2009, 38(11):1257-1280.
[23] ZHANG C H, JIN F, WANG J T, et al. Nonlinear behavior and seismic safety evaluation of concrete dams[M]. Beijing:Tsinghua University Press, 2012.
[24] 潘坚文, 王进廷, 张楚汉. 超强地震作用下拱坝的损伤开裂分析[J]. 水利学报, 2007, 38(2):143-149. PAN J W, WANG J T, ZHANG C H. Analysis of damage and cracking in arch dams subjected to extremely strong earthquake[J]. Journal of Hydraulic Engineering, 2007, 38(2):143-149. (in Chinese)
[25] LIU J B, LI B. A unified viscous-spring artificial boundary for 3-D static and dynamic applications[J]. Science in China Series E-Engineering & Materials Science, 2005, 48(5):570-584.
[26] LEE J, FENVES G L. Plastic-damage model for cyclic loading of concrete structures[J]. Journal of Engineering Mechanics, 1998, 124(8):892-900.
[27] 潘坚文. 高混凝土坝静动力非线性断裂与地基辐射阻尼模拟研究[D]. 北京:清华大学, 2010. PAN J W. Nonlinear static and seismic fracture analysis of high concrete dams and modeling of radiation damping for foundation[D]. Beijing:Tsinghua University, 2010. (in Chinese)
[28] SHOME N, CORNELL C A. Probabilistic seismic demand analysis of nonlinear structures[D]. Stanford:Stanford University, 1999.
[29] 吕大刚, 于晓辉, 潘峰, 等. 基于改进云图法的结构概率地震需求分析[J]. 世界地震工程, 2010, 26(1):7-15. LÜ D G, YU X H, PAN F, et al. Probabilistic seismic demand analysis of structures based on an improved cloud method[J]. World Earthquake and Engineering, 2010, 26(1):7-15. (in Chinese)