Abstract
An increasing number of landslides are occurring during the construction of highways in mountainous areas all over the world. A recent highway landslide located in the mountainous region of Southwest China is taken as a representative example to carry out the initiation mechanism and deformation characteristics analysis, and study the intelligent partitioning method in this paper. The geological conditions of the landslide are obtained through detailed geological surveys and investigation. A three-dimensional (3D) monitoring network with a total of 72 multi-points, including 53 surface displacement monitoring points and 19 deep displacement monitoring holes, is established to examine the spatial deformation characteristics of the landslide for nearly 25 months. The monitoring results of the surface displacement show that the dynamic deformation of the landslide can be divided into three stages, namely initial deformation stage, accelerated deformation stage, and stabilization stage. Based on the strata distribution obtained by the borehole investigation and the results of deep displacement monitoring, the depth of the slip surface can be reliably determined. The smart contract method in blockchain technology combined with the multi-point monitoring dataset is novelty applied to realize the intelligent automatic partition of the highway landslide without any human involvement. This method can provide the basis for further independent analysis of different landslide zones. According to the investigation and analysis, the initiation mechanism for the highway landslides is attributed to both internal poor geological conditions and external intense precipitation and engineering excavation.
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References
Azaria A, Ekblaw A, Vieira T, Lippman A (2016) MedRec: Using Blockchain for Medical Data Access and Permission Management. International Conference on Open & Big Data. IEEE.
Benoit L, Briole P, Martin O, Thom C, Malet JP, Ulrich P (2015) Monitoring landslide displacements with the Geocube wireless network of low-cost GPS. Eng Geol 195:111–121. https://doi.org/10.1016/j.enggeo.2015.05.020
Chen X, Cui Y (2017) The formation of the Wulipo landslide and the resulting debris flow in Dujiangyan City, China. J Mt Sci 14(6):1100–1112. https://doi.org/10.1007/s11629-017-4392-1
Christidis K, Devetsikiotis M (2016) Blockchains and smart contracts for the internet of things. IEEE Access 4:2292–2303. https://doi.org/10.1109/ACCESS.2016.2566339
Corominas J, Westen C, Frattini P, Cascini L, Smith J (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Dowding C, Connor K (2016) Comparison of TRD and inclinometers for slope monitoring. Am Soc Civil Eng. https://doi.org/10.1061/40518(294)7
Gong W, Tang H, Juang CH, Wang L (2020) Optimization design of stabilizing piles in slopes considering spatial variability. Acta Geotech 15:3243–3259. https://doi.org/10.1007/s11440-020-00960-6
Hu J, Xu W (2015) The Ministry of Land and Resources has actively guided and assisted local governments in coping with the massive landslide disaster in Lishui, Zhejiang Province (in Chinese). China Emergency Management 2015(11):48
Huang H, Long J, Yi W, Yi Q, Zhang G, Lei B (2017) A method for using unmanned aerial vehicles for emergency investigation of single geo-hazards and sample applications of this method. Nat Hazard Earth Sys 17:1961–1979. https://doi.org/10.5194/nhess-17-1961-2017
Kang G, Song Y, Kim T (2009) Behavior and stability of a large-scale cut slope considering reinforcement stages. Landslides 6(3):263–272. https://doi.org/10.1007/s10346-009-0164-5
Li C, Tang H, Hu X, Li D, Hu B (2009) Landslide Prediction based on wavelet analysis and cusp catastrophe. J Earth Sci 20(6):971–977. https://doi.org/10.1007/s12583-009-0082-4
Li C, Wu J, Tang H, Hu X, Zhang Y (2016) Model testing of the response of stabilizing piles in landslides with upper hard and lower weak bedrock. Eng Geol 204:65–76. https://doi.org/10.1016/j.enggeo.2016.02.002
Li C, Yan J, Wu J, Lei C, Zhang Y (2017) Determination of the embedded length of stabilizing piles in colluvial landslides with upper hard and lower weak bedrock based on the deformation control principle. B Eng Geol Environ 8:1–20. https://doi.org/10.1007/s10064-017-1123-3
Li Q, Zeng C, Liu W, Wen Z, Li C (2019) Dynamic discrimination of main slip direction of a highway landslide based on dense multi-point deformation monitoring (in Chinese). Geological Science and Technology Information 38(4):231–239. https://doi.org/10.19509/j.cnki.dzkq.2019.0424
Li C, Criss RE, Fu Z, Long J, Tan Q (2021) Evolution characteristics and displacement forecasting model of landslides with stair-step sliding surface along the Xiangxi River, three Gorges Reservoir region, China. Eng Geol 283:105961. https://doi.org/10.1016/j.enggeo.2020.105961
Lin F, Wu L, Huang R, Zhang H (2018) Formation and characteristics of the Xiaoba landslide in Fuquan, Guizhou, China. Landslides 15(4):669–681. https://doi.org/10.1007/s10346-017-0897-5
Liu W (2019) Deformation mechanism and engineering prevention of the highway landslide based on spatial monitoring - a case study of YuKai Highway landslide in Guizhou Province (in Chinese). Doctoral dissertation.
Liu G, Guo H, Perski Z, Fan J, Bai S, Yan S, Song R (2016) Monitoring the slope movement of the Shuping landslide in the Three Gorges Reservoir of China, using X-band time series SAR interferometry. Adv Space Res 57(12):2487–2495. https://doi.org/10.1016/j.asr.2016.03.043
Long J, Liu Y, Li C, Fu Z, Zhang H (2020) A novel model for regional susceptibility mapping of rainfall-reservoir induced landslides in Jurassic slide-prone strata of western Hubei Province. Three Gorges Reservoir area Stoch Env Res Risk A. https://doi.org/10.1007/s00477-020-01892-z
Mohammadi S, Taiebat H (2016) Finite element simulation of an excavation-triggered landslide using large deformation theory. Eng Geol 205:62–72. https://doi.org/10.1016/j.enggeo.2016.02.012
Nakamoto S (2009) Bitcoin: A Peer-to-Peer Electronic Cash System. Online. Available: https://bitcoin.org/bitcoin.pdf
Radziwill N (2018) Blockchain Revolution: how the technology behind Bitcoin is changing money, business, and the world. Qual Manag J 25(1):64–65. https://doi.org/10.1080/10686967.2018.1404373
Safaei M, Ghanbari S, Umarova Z, Iztayev Z (2020) A security model based on blockchain smart contracts for improve authentication on the Internet of Things. Azerbaijan. Journal of High-Performance Computing 3:3–14. https://doi.org/10.32010/26166127.2020.3.1.3.14
Segalini A, Chiapponi L, Pastarini B, Carani C (2014) Automated inclinometer monitoring based on micro electro-mechanical system technology: applications and verification. Landslide Science for a Safer Geo-environment. https://doi.org/10.1007/978-3-319-05050-8_92
Shentu N, Zhang H, Li Q, Zhou H, Tong R, Li X (2011) A theoretical model to predict both horizontal displacement and vertical displacement for electromagnetic induction-based deep displacement sensors. Sensors 12(12):233–259. https://doi.org/10.3390/s120100233
Simeoni L, Mongiovì L (2007) Inclinometer monitoring of the Castelrotto landslide in Italy. J Geotech Geoenviron Eng ASCE 133(6):653–666. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:6(653)
Stark T, Arellano W, Hillman R et al (2005) Effect of toe excavation on a deep bedrock landslide. J Perform Constr Facil 19(3):244–255. https://doi.org/10.1061/(ASCE)0887-3828(2005)19:3(244)
Stumpf A, Malet J, Delacourt C (2017) Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides. Remote Sens Environ 189:40–55. https://doi.org/10.1016/j.rse.2016.11.007
Tang H, Gong W, Li C, Wang L, Juang CH (2018) A new framework for characterizing landslide deformation: a case study of the Yu-Kai highway landslide in Guizhou, China. B Eng Geol Environ 78(6):4291–4309. https://doi.org/10.1007/s10064-018-1397-0
Wang J, Liang Y, Zhang H, Wu Y, Lin X (2013) A loess landslide induced by excavation and rainfall. Landslides 11(1):141–152. https://doi.org/10.1007/s10346-013-0418-0
Xia M, Ren G, Yang X (2020) Mechanism of a catastrophic landslide occurred on May 12, 2019, Qinghai Provinece, China. Landslides:1–14. https://doi.org/10.1007/s10346-020-01559-4
Xu G, Li W, Yu Z, Ma X, Yu Z (2015) The 2 September 2014 Shanshucao landslide, Three Gorges Reservoir, China. Landslides 12(6):1169–1178. https://doi.org/10.1007/s10346-015-0652-8
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. https://doi.org/10.1007/s10346-021-01657-x
Yan Y, Cui Y, Tian X, Hu S, Guo J, Wang Z, Yin S, Liao L (2020) Seismic signal recognition and interpretation of the 2019 “7.23” Shuicheng landslide by seismogram stations. Landslides. 17:1191–1206
Yao W, Li C, Zuo Q, Zhan H, Criss RE (2019) Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China. Geomorphology 343:34–47. https://doi.org/10.1016/j.geomorph.2019.06.024
Yin Y, Zheng W, Liu Y, Zhang J, Li X (2010) Integration of GPS with InSAR to monitoring of the Jiaju landslide in Sichuan, China. Landslides 7(3):391–391. https://doi.org/10.1007/s10346-010-0225-9
Yu H, Li C, Zhou J, Chen W, Long J, Wang X, Peng T (2020) Recent rainfall- and excavation-induced bedding rockslide occurring on 22 October 2018 along the Jian-En expressway, Hubei, China. Landslides 17:2619–2629. https://doi.org/10.1007/s10346-020-01468-6
Zhou J, Cui P, Hao M (2015) Comprehensive analyses of the initiation and entrainment processes of the 2000 yigong catastrophic landslide in Tibet, China. Landslides 13(1):1–16. https://doi.org/10.1007/s10346-014-0553-2
Zhu H, Shi B, Zhang C (2017) FBG-based monitoring of geohazards: current status and trends. Sensors 17(3):452. https://doi.org/10.3390/s17030452
Acknowledgements
Thanks to Prof. Wenping Gong from China University of Geosciences, Wuhan for his advices.
Funding
This work was supported by the National Key R&D Program of China (No. 2018YFC1507200), National Natural Science Foundation of China (Nos. 42090054, 41922055, 41931295 and 41772376), and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUGGC09).
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Li, C., Long, J., Liu, Y. et al. 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 (2021). https://doi.org/10.1007/s10346-021-01698-2
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DOI: https://doi.org/10.1007/s10346-021-01698-2