Abstract
Landslides are regarded as significant geological hazards across the world, causing serious economic losses and casualties. The understanding on deformation characteristics and failure mechanisms of landslides plays the vital roles in slope stability evaluation and reinforcement design. In this study, the deformation characteristics and failure mechanism of the Xiaomiaoling talus slope were analyzed based on field monitoring data. In addition, as it was difficult to measure the shear strength parameters of the rock–soil mixture due to its complex spatial structure and variable material composition, a displacement back analysis based on the back-propagation neural network (DBA-BPNN) was proposed to determine the shear strength parameters of the rock–soil mixture. The analytical results show that deformation of the Xiaomiaoling talus slope was that of a typical traction landslide, which has the characteristics of progressive failure, and major slope deformation was triggered by excavation and rainfall. According to field monitoring data, the shear strength parameters of the rock–soil mixture could be determined. The predicted cohesion and internal friction angle of the rock–soil mixture were 10.84 kPa and 19.51°, respectively, and the predicted and test values were in good agreement. The method proposed in this paper can provide references for the design and construction in geotechnical engineering.
References
Cao Y, Yin K, Alexander DE, Zhou C (2016) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13(4):725–736
Chen FF, Li N, Zhang ZQ et al (2006) Actualities and problems of back analysis method in geotechnical engineering. J Water Resour Architect Eng 4(3):54–58
Cui YL, Wei JB, Deng JH et al (2013) Deformation mechanism and stability evaluation of left abutment debris in xiluodu hydropower station. Chin J Rock Mech Eng 32(Supple. 2):3821–3828
Deng JH, Lee CF (2001) Displacement back analysis for a steep at the Three Gorges Project site. Int J Rock Mech Min Sci 38:259–268
Dong Q, Zhu ZW, Liu DY (2010) The progressive failure and stability analysis for avalanche deposit slope. J Xi'an Univ Architect Technol 42(3):358–364
Froude M, Petley D (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181
Gioda G, Sakurai S (1987) Back analysis procedures for the interpretation of field measurements in geomechanics. 11:555–583
Hu XL, Zhang M, Sun M, Huang K, Song Y (2015) Deformation characteristics and failure mode of the Zhujiadian landslide in the Three Gorges Reservoir, China[J]. Bull Eng Geol Environ 74(1):1–12
Huang R (2009) Some catastrophic landslides since the twentieth century in the southwest of China. Landslides 6(1):69–81
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update [J]. Landslides 11(2):167–194
Kavanagh KT, Clough RW (1971) Finite element applications in the characterization of elastic solids. Int J Solids Struct 7(1):11–23
Li DQ, Qi XH, Phoon KK, Phoon KK, Zhang LM, Zhou CB (2014) Effect of spatially variable shear strength parameters with linearly increasing mean trend on reliability of infinite slopes. Struct Saf 49:45–55
Li S, Zhao H, Ru Z, Sun Q (2016) Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope[J]. Eng Geol 203:178–190
Liang ZH, Gong B, Tang CA et al (2014) Displacement back analysis for a high slope of the Dagangshan hydroelectric power station based on BP neural network and particle swarm optimization. Sci World J 2014:1–11
Okui Y, Tokunaga A, Shinji M et al (1997) New back analysis method of slope stability by using field measurements. Int J Rock Mech Min Sci 34(3-4):234
Oreste P (2005) Back-analysis techniques for the improvement of the understanding of rock in underground constructions. Tunn Undergr Space Technol 20(1):7–21
Palis E, Lebourg T, Tric E, Malet JP, Vidal M (2017) Long-term monitoring of a large deep-seated landslide (La Clapiere, South-East French Alps): initial study. Landslides 14(1):155–170
Petley DN (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930
Petley DN, Mantovani F, Bulmer MH, Zannoni A (2005) The use of surface monitoring data for the interpretation of landslide movement patterns. Geomorphology 66(1/4):133–147
Wei J, Zhao Z, Xu C, Wen Q (2019) Numerical investigation of landslide kinetics for the recent Mabian landslide (Sichuan, China). Landslides 16(4):2287–2298
Wu XZ (2015) Development of fragility functions for slope instability analysis. Landslides 11(6):165–175
Xu Q (2012) Theoretical studies on prediction of landslides using slope deformation process data. J Eng Geol 20(2):145–151
Yin Y, Wang H, Gao Y, Li X (2010) Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China. Landslides 7(3):339–349
Zhang YM, Hu XL, Tannant DD, Zhang G, Tan F (2018) Field monitoring and deformation characteristics of a landslide with piles in the Three Gorges Reservoir area. Landslides 15(5):581–592
Funding
The project was supported by the National Natural Science Foundation of China (41672273) and the Fundamental Research Funds for the Central Universities (22120180313). The research was also substantially supported by the Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education (Tongji University).
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Xing, H., Zhang, H., Liu, L. et al. Comprehensive monitoring of talus slope deformation and displacement back analysis of mechanical parameters based on back-propagation neural network. Landslides 18, 1889–1907 (2021). https://doi.org/10.1007/s10346-020-01613-1
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DOI: https://doi.org/10.1007/s10346-020-01613-1