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Shear deformation calculation of landslide using distributed strain sensing technology considering the coupling effect

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Abstract

Deformation monitoring is of great significance in understanding the evolution process of landslides and evaluating their stability conditions. Distributed strain sensing (DSS) technology, with obvious advantages of long measuring distance and excellent long-term performance, has become a widely accepted method in landslide monitoring. Nonetheless, when monitoring the subsurface shear deformation of a landslide based on DSS technology, two significant challenges remain unresolved. First, the coupling behavior between the borehole-installed DSS cable and the surrounding soil is unclear. Second, how to convert the strains exerted on the DSS cable into shear displacements remains elusive. To address these issues, this study investigates the coupling deformation mechanism between the DSS cable and surrounding soil under both low and high confining pressures, and develops corresponding soil-cable coupling criteria. Subsequently, a novel strain–displacement conversion model, namely the accumulative integral method (AIM), is proposed based on the geometric configuration of the DSS cable during soil shearing. The proposed method is verified against laboratory shear test results and field monitoring data, yielding results that not only confirm the reliability of the soil-cable coupling criteria, but also demonstrate the superiority of the proposed AIM over other methods in terms of accuracy .

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References

  • Bergado DT, Chai JC, Miura N (1996) Prediction of pullout resistance and pullout force-displacement relationship for inextensible grid reinforcements. Soils Found 36(4):11–22

    Google Scholar 

  • Bernini R, Minardo A, Zeni L (2002) Reconstruction technique for stimulated Brillouin scattering distributed fiber-optic sensors. Opt Eng 41(9):2186–2194

    Google Scholar 

  • Carlà T, Tofani V, Lombardi L, Raspini F, Bianchini S, Bertolo D, Thuegaz P, Casagli N (2019) Combination of GNSS, satellite InSAR, and GBInSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment. Geomorphology 335:62–75

    Google Scholar 

  • Das BM (2010) Principles of geotechnical engineering. Stamford, CT, USA: Cengage Learning (7th edition)

  • Gu K, Xiang FL, Liu C, Shi B, Zheng X (2022) Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method. Eng Geol 312:106948

    Google Scholar 

  • Guo T, Li A, Song Y, Zhang B, Liu Y, Yu N (2009) Experimental study on strain and deformation monitoring of reinforced concrete structures using PPP-BOTDA. Sci China Ser e: Technol Sci 52(10):2859–2868

    Google Scholar 

  • Han HM, Shi B, Zhang L, Chen Q, Wang C, Ding L, Wang R (2021) Deep displacement monitoring and foundation base boundary reconstruction analysis of diaphragm wall based on ultra-weak FBG. Tunn Undergr Space Technol 117:104158

    Google Scholar 

  • Hauswirth D, Iten M, Richli R, Puzrin AM (2010). Fibre optic cable and micro-anchor pullout tests in sand. Proceedings of the 7th International Conference on Physical Modelling in Geotechnics, Zurich, Switzerland

  • Horpibulsuk S, Niramitkornburee A (2010) Pullout resistance of bearing reinforcement embedded in sand. Soils Found 50(2):215–226

    Google Scholar 

  • Hu X, Bürgmann R, Lu Z, Handwerger AL, Wang T, Miao R (2019) Mobility, thickness, and hydraulic diffusivity of the slow-moving Monroe landslide in California revealed by L-band satellite radar interferometry. J Geophys Res: Solid Earth 124(7):7504–7518

    Google Scholar 

  • Hu X, Bürgmann R, Schulz WH, Fielding EJ (2020) Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing. Nat Commun 11(1):1–9

    Google Scholar 

  • Iten M, Puzrin AM, Schmid A (2008) Landslide monitoring using a road-embedded optical fiber sensor, In: W. Ecke, K.J. Peters, N.G. Meyendorf, (Eds.), Proc. SPIE 6933, Smart Sensor Phenomena, Technology, Networks, and Systems. SPIE, San Diego, California, USA, 693315–693319

  • Iten M (2011) Novel applications of distributed fiber-optic sensing in geotechnical engineering. Swiss Federal Institute of Technology in Zurich

  • Komac M, Holley R, Mahapatra P, Van der marel H, Bavec M (2015) Coupling of GPS/GNSS and radar interferometric data for a 3D surface displacement monitoring of landslides. Landslides 12(2):241–257

    Google Scholar 

  • Lacroix P, Handwerger AL, Bièvre G (2020) Life and death of slow-moving landslides. Nature Rev Earth Environ 1(8):404–419

    Google Scholar 

  • Li B, Zhang D, Wang J, Liu S, Shi B (2015) Calculation method for soil shear deformation based on strain distribution of sensing fiber. J Eng Geol 23:767–772 (in Chinese with English abstract)

    Google Scholar 

  • Li CD, Long J, Liu Y, Li Q, Liu WQ, Feng PF, Li BC, Xian J (2021a) Mechanism analysis and partition characteristics of a recent highway landslide in Southwest China based on a 3D multi-point deformation monitoring system. Landslides 18:2895–2906

    Google Scholar 

  • Li HJ, Zhu HH, Li YH, Hu W, Shi B (2021b) Fiber Bragg grating–based flume test to study the initiation of landslide-debris flows induced by concentrated runoff. Geotech Test J 44(4):20190290

    Google Scholar 

  • Li HJ, Zhu HH, Li Y, Zhang C, Shi B (2022) Experimental study on uplift mechanism of pipeline buried in sand using high-resolution fiber optic strain sensing nerves. J Rock Mech Geotech Eng 14(4):1304–1318

    Google Scholar 

  • Liu YR, Tang HM (2008) Rock Mechanics. Chemical Industry Press, Beijing (in Chinese)

    Google Scholar 

  • Ma J, Tang H, Hu X, Bobet A, Zhang M, Zhu T, Song Y, Ez Eldin MAM (2017) Identification of causal factors for the Majiagou landslide using modern data mining methods. Landslides 14:311–322

    Google Scholar 

  • Ma P, Cui Y, Wang W, Lin H, Zhang Y (2021) Coupling InSAR and numerical modeling for characterizing landslide movements under complex loads in urbanized hillslopes. Landslides 18(5):1611–1623

    Google Scholar 

  • Olivares L, Damiano E, Greco R, Zeni L, Picarelli L, Minardo A, Guida A, Bernini R (2009) An instrumented flume to investigate the mechanics of rainfall-induced landslides in unsaturated granular soils. Geotech Testing J 32(2):108–118

  • Panizzo A, Girolamo PD, Risio MD, Maistri A, Petaccia A (2005) Great landslide events in Italian artificial reservoirs. Nat Hazards Earth Syst Sci 5(5):733–740

    Google Scholar 

  • Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930

    Google Scholar 

  • Picarelli L, Damiano E, Greco R, Minardo A, Olivares L, Zeni L (2015) Performance of slope behavior indicators in unsaturated pyroclastic soils. J Mt Sci 12(6):1434–1447

    Google Scholar 

  • Sang HW, Zhang D, Gao Y, Zhang L, Wang G, Shi B, Zheng BN, Liu Y (2019) Strain distribution based geometric models for characterizing the deformation of a sliding zone. Eng Geol 263:105300

    Google Scholar 

  • Schenato L, Palmieri L, Camporese M, Bersan S, Cola S, Pasuto A, Galtarossa A, Salandin P, Simonini P (2017) Distributed optical fibre sensing for early detection of shallow landslides triggering. Sci Rep 7:14686

    Google Scholar 

  • Schlögel R, Doubre C, Malet JP, Masson F (2015) Landslide deformation monitoring with ALOS/PALSAR imagery: a D-InSAR geomorphological interpretation method. Geomorphology 231:314–330

    Google Scholar 

  • Song Z, Shi B, Juang H, Shen M, Zhu H (2017) Soil strain-field and stability analysis of cut slope based on optical fiber measurement. Bull Eng Geol Env 76:937–946

    Google Scholar 

  • Sukmak K, Sukmak P, Horpibulsuk S et al (2015) Effect of fine content on the pullout resistance mechanism of bearing reinforcement embedded in cohesive–frictional soils. Geotext Geomembr 43(2):107–117

    Google Scholar 

  • Sun YJ, Zhang D, Shi B, Tong HJ, Wei GQ, Wang X (2014) Distributed acquisition, characterization and process analysis of multi-field information in slopes. Eng Geol 182:49–62

    Google Scholar 

  • Sun YJ, Cao SQ, Xu HZ, Zhou XX (2020) Application of distributed fiber optic sensing technique to monitor stability of a geogrid reinforced model slope. Int J Geosynth Ground Eng 6:29

    Google Scholar 

  • Tang HM, Wasowski J, Juang CH (2019) Geohazards in the three Gorges Reservoir Area, China–lessons learned from decades of research. Eng Geol 261:105267

    Google Scholar 

  • Tofani V, Dapporto S, Vannocci P, Casagli N (2006) Infiltration, seepage and slope instability mechanisms during the 20–21 November 2000 rainstorm in Tuscany, central Italy. Nat Hazard 6(6):1025–1033

    Google Scholar 

  • Wang BJ, Li K, Shi B, Wei GQ (2009) Test on application of distributed fiber optic sensing technique into soil slope monitoring. Landslides 6(1):61–68

    Google Scholar 

  • Wang DY, Zhu HH, Wang J, Sun YJ, Schenato L, Pasuto A, Shi B (2023) Characterization of sliding surface deformation and stability evaluation of landslides with fiber–optic strain sensing nerves. Eng Geol 314:107011

    Google Scholar 

  • Wang Z, Zhang W, Gao X et al (2020) Stability analysis of soil slopes based on strain information. Acta Geotech 15:3121–3134

    Google Scholar 

  • White DJ, Take WA, Bolton MD (2001) Measuring soil deformation in geotechnical models using digital images and PIV analysis, in: Proc. 10th International Conference on Computer Methods and Advances in Geomechanics, Tucson, Arizona, 997–1002

  • Wolter A, Stead D, Clague JJ (2014) A morphologic characterisation of the 1963 Vajont Slide, Italy, using long-range terrestrial photogrammetry. Geomorphology 206:147–164

    Google Scholar 

  • Wu H, Zhu HH, Zhang CC, Zhou GY, Zhu B, Zhang W, Azarafza M (2020) Strain integration-based soil shear displacement measurement using high-resolution strain sensing technology. Measurement 166:108210

    Google Scholar 

  • Wu JH, Shi B, Cao DF, Jiang HT, Wang XF, Gu K (2017) Model test of soil deformation response to draining-recharging conditions based on DFOS. Eng Geol 226:107–121

    Google Scholar 

  • Wu JH, Shi B, Gu K, Liu S, Wei G (2021) Evaluation of land subsidence potential by linking subsurface deformation to microstructure characteristics in Suzhou, China. Bull Eng Geol Env 80:2587–2600

    Google Scholar 

  • Yan Y, Cui Y, Liu D, Tang H, Li Y, Tian X, Zhang L, Hu S (2021) Seismic signal characteristics and interpretation of the 2020 “6.17” Danba landslide dam failure hazard chain process. Landslides 18(6):2175–2192

  • Ye X, Zhu HH, Wang J, Zhang Q, Shi B, Schenato L, Pasuto A (2022) Subsurface multi-physical monitoring of a reservoir landslide with the fiber-optic nerve system. Geophys Res Lett 49(11):e2022GL098211

  • Zeni L, Picarelli L, Avolio B, Coscetta A, Papa R, Zeni G, Miao CD, Vassallo R, Minardo A (2015) Brillouin optical time-domain analysis for geotechnical monitoring. J Rock Mech Geotech Eng 7(4):458–462

    Google Scholar 

  • Zhang CC, Zhu HH, Liu SP, Shi B, Zhang D (2018) A kinematic method for calculating shear displacements of landslides using distributed fiber optic strain measurements. Eng Geol 234:83–96

    Google Scholar 

  • Zhang CC, Zhu HH, Chen DD, Xu XY, Shi B, Chen XP (2019) Feasibility study of anchored fiber-optic strain-sensing arrays for monitoring soil deformation beneath model foundation. Geotech Test J 42(4):966–984

    Google Scholar 

  • Zhang CC, Zhu HH, Liu SP, Shi B, Cheng G (2020a) Quantifying progressive failure of micro-anchored fiber optic cable–sand interface via high-resolution distributed strain sensing. Can Geotech J 57(6):871–881

    Google Scholar 

  • Zhang CC, Shi B, Zhang S, Gu K, Liu SP, Gong XL, Wei GQ (2021a) Microanchored borehole fiber optics allows strain profiling of the shallow subsurface. Sci Rep 11:9173

    Google Scholar 

  • Zhang L, Shi B, Zhang D, Sun YJ, Inyang HI (2020) Kinematics, triggers and mechanism of Majiagou landslide based on FBG real-time monitoring. Environ Earth Sci 79:200

    Google Scholar 

  • Zhang L, Cheng G, Wu J, Minardo A, Song Z (2021b) Study on slope failure evolution under surcharge loading and toe cutting with BOTDA technology. Opt Fiber Technol 66:102644

    Google Scholar 

  • Zhang L, Shi B, Zhu H, Yu XB, Han H, Fan X (2021c) PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect. Landslides 18(1):179–193

    Google Scholar 

  • Zhu HH, Garg A, Yu X, Zhou HW (2022) Editorial for Internet of Things (IoT) and artificial intelligence (AI) in geotechnical engineering. J Rock Mech Geotech Eng 14(4):1025–1027

    Google Scholar 

  • Zhu HH, Ho ANL, Yin JH, Sun HW, Pei HF, Hong CY (2012) An optical fibre monitoring system for evaluating the performance of a soil nailed slope. Smart Struct Syst 9:393–410

    Google Scholar 

  • Zhu HH, She JK, Zhang CC, Shi B (2015) Experimental study on pullout performance of sensing optical fibers in compacted sand. Measurement 73:284–294

    Google Scholar 

  • Zhu HH, Shi B, Zhang J, Yan JF, Zhang CC (2014) Distributed fiber optic monitoring and stability analysis of a model slope under surcharge loading. J Mt Sci 11(4):979–989

    Google Scholar 

  • Zou Z, Yan J, Tang H, Wang S, Hu X (2020) A shear constitutive model for describing the full process of the deformation and failure of slip zone soil. Eng Geol 105766

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Funding

This study is financially supported provided by the National Natural Science Foundation of China (Grant Nos. 42120104002, 42225702, and 42077235), the State Key Laboratory of Hydroscience and Hydraulic Engineering (No. 2021-KY-04), and the fellowship of China Postdoctoral Science Foundation (Grant No. 2021M701841).

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Correspondence to Honghu Zhu.

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Zhang, L., Cui, Y., Zhu, H. et al. Shear deformation calculation of landslide using distributed strain sensing technology considering the coupling effect. Landslides 20, 1583–1597 (2023). https://doi.org/10.1007/s10346-023-02051-5

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