A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China
- 56 Downloads
Xigeda formation is a type of hundred-meter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design, and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua (FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loading-test pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neural-network-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.
KeywordsBack-propagation neural network Displacement back analysis Geomechanical parameters Landslide Numerical analysis Uniform design Xigeda formation
Unable to display preview. Download preview PDF.
This work was supported by the “Light of West China” Program of Chinese Academy of Sciences (Grant No.Y6R2250250), the National Basic Research Program of China (973 Program, Grant No.2013CB733201), the One-Hundred Talents Program of Chinese Academy of Sciences (Lijun Su), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No.QYZDB-SSW-DQC010) and the Youth Fund of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (Grant No. Y6K2110110).
- Cundall PA (1976) Explicit finite difference methods in geomechanics. In Numerical Methods in Engineering, Proceedings of the International Conference on Numerical Methods in Geomechanics, Blacksburg, Virginia, USA 1: 132–150Google Scholar
- Das SK (2013) Artificial neural networks in geotechnical engineering: modeling and application issues. Chapter 10, Metaheuristics in Water, Geotechnical and Transport Engineering, Editors X Yang, AH Gandomi, S Talatahari, AH Alavi, Elsevier, London, ISBN: 978-0-12-398296-4 pp. 231–270. https://doi.org/10.1016/B978-0-12-398296-4.00010-6Google Scholar
- Fang KT (1980) The uniform design: application of numbertheoretic methods in experimental design. Acta Mathematicae Applicatae Sinica 3(4):363–372Google Scholar
- Fausett LV (1994) Fundamentals of neural networks: architectures, algorithms and applications. Prentice-Hall, Inc., USA.Google Scholar
- Feng XT, Zhang ZQ, Sheng Q (2000) Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method. International Journal of Rock Mechanics and Mining Sciences 37(7): 1039–1054. https://doi.org/10.1016/S1365-1609(00)00035-6CrossRefGoogle Scholar
- Kong P, Granger DE, Wu FY, Caffee MW, Wang YJ, Zhao XT, Zheng Y (2009) Cosmogenic nuclide burial ages and provenance of the Xigeda paleo-lake: Implications for evolution of the Middle Yangtze River. Earth and Planetary Science Letters 278(1–2): 131–141. https://doi.org/10.1016/j.epsl.2008.12.003CrossRefGoogle Scholar
- Matlab (2011) Matrix Laboratory: Version R2011b. MathWorks Inc., USAGoogle Scholar
- Moreira N, Miranda T, Pinheiro M, Fernandes P, Dias D, Costa L, Sena-Cruz J (2013) Back analysis of geomechanical parameters in underground works using an evolution strategy algorithm. Tunnelling and Underground Space Technology 33: 143–158. https://doi.org/10.1016/j.tust.2012.08.011CrossRefGoogle Scholar
- Wang Y, Fang KT (1981) A note on uniform distribution and experimental design. Chinese Science Bulletin 26(6): 485–489. https://doi.org/10.1142/9789812701190_0035Google Scholar
- Zhang LQ, Yue ZQ, Yang ZF, Qi JX, Liu FC (2006) A displacement-based back-analysis method for rock mass modulus and horizontal in situ stress in tunneling-illustrated with a case study. Tunnelling and Underground Space Technology 21(6): 636–649. https://doi.org/10.1016/j.tust.2005.12.001CrossRefGoogle Scholar