Abstract
The mechanism of slope stability prediction is formulated based on its material, geometrical and environmental situation, and slope stability prediction has been accepted as a tool for analyzing and predicting future structure stability based on geotechnical properties and failure mechanisms. However, the study of slope instability is complex and usually difficult to explain by mathematical methods. The number of slope cases limits the accuracy of slope stability prediction, and the variability of soil or rock parameters of slopes poses new challenges for prediction using conventional algorithms. To improve the accuracy of slope stability state prediction, this paper proposes an efficient slope stability state prediction method with a highly robust convolutional neural network named the multiscale, multichannel, one-dimensional convolutional neural network (MSC-1DCNN) and substantial empirical data collected worldwide. The collected dataset is amplified. Additionally, the probability of failure is calculated considering the variability of soil or rock parameters. Compared with some state-of-the-art prediction methods, the MSC-1DCNN presents high prediction accuracy. The proposed method is applied to a slope, and the results indicate that this paper provides a reliable slope stability state prediction method for homogeneous slopes worldwide.
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References
Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley. Himalayas Int J Remote Sens 25:559–572. https://doi.org/10.1080/0143116031000156819
Balogun AL, Rezaie F, Pham QB, Gigović L, Drobnjak S, Aina YA, Panahi M, Yekeen ST, Lee S (2020) Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geosci Front 12(3):101104. https://doi.org/10.1016/j.gsf.2020.10.009
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Bishop AW (1955) The use of slip circle in the stability analysis of slopes. Geotechnique 5:7–17. https://doi.org/10.1680/geot.1955.5.1.7
Bishop CM (1995) Neural Networks for Pattern Recognition. Oxford University Press, Oxford. https://doi.org/10.7551/mitpress/5271.003.0005
Cai Z, Fan Q, Feris RS, Vasconcelos N (2016) A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision. Springer, Cham, pp 354–370. https://doi.org/10.1007/978-3-319-46493-0_22
Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833. https://doi.org/10.1016/j.catena.2020.104833
Cherubini C (2000) Probabilistic approach to the design of anchored sheet pile walls. Comput Geotech 26(3–4):309–330. https://doi.org/10.1016/S0266-352X(99)00044-0
Cho SE (2007) Effects of spatial variability of soil properties on slope stability. Eng Geol 92(3–4):97–109. https://doi.org/10.1016/j.enggeo.2007.03.006
Cho SE (2009) Probabilistic stability analyses of slopes using the ANN based response surface. Comput Geotech 36:787–797. https://doi.org/10.1016/j.compgeo.2009.01.003
Cho SE (2010) Probabilistic assessment of slope stability that considers the spatial variability of soil properties. J Geotech Geoenviron Eng 136(7):975–984. https://doi.org/10.1061/(asce)gt.1943-5606.0000309
Deng Z, Wang B, Xu Y, Xu T, Liu C, Zhu Z (2019) Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access 7:88058–88071. https://doi.org/10.1109/access.2019.2926137
Dyson AP, Tolooiyan A (2019) Prediction and classification for finite element slope stability analysis by random field comparison. Comput Geotech 109:117–129. https://doi.org/10.1016/j.compgeo.2019.01.026
El-Ramly H, Morgenstern NR, Cruden DM (2002) Probabilistic slope stability analysis for practice. Can Geotech J 39(3):665–683. https://doi.org/10.1139/t02-034
Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3):697–710. https://doi.org/10.1109/tnn.2002.1000134
Feng X, Li S, Yuan C, Zeng P, Sun Y (2018) Prediction of slope stability using naive Bayes classifier. KSCE J Civ Eng 22(3):941–950. https://doi.org/10.1007/s12205-018-1337-3
Friedrichs F, Igel C (2005) Evolutionary tuning of multiple SVM parameters. Neurocomputing 64:107–117. https://doi.org/10.1016/j.neucom.2004.11.022
Goh ATC, Zhang WG, Wong KS (2019) Deterministic and reliability analysis of basal heave stability for excavation in spatial variable soils. Comput Geotech 108:152–160. https://doi.org/10.1016/j.compgeo.2018.12.015
Gökceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44(1–4):147–161. https://doi.org/10.1016/s0013-7952(97)81260-4
Gong P (1996) Integrated analysis of spatial data from multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. Photogramm Eng Remote Sens 62:513–523
Griffiths DV, Fenton GA, Manoharan N (2002) Bearing capacity of rough rigid strip footing on cohesive soil: probabilistic study. J Geotech Geoenviron Eng 128(9):743–755. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(743)
Griffiths DV, Huang J, Fenton GA (2009) Influence of spatial variability on slope reliability using 2-D random fields. J Geotech Geoenviron Eng 135(10):1367–1378. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000099
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint http://arxiv.org/abs/1207.0580
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Ji J, Low BK (2012) Stratified response surfaces for system probabilistic evaluation of slopes. J Geotech Geoenviron Eng 138(11):1398–1406. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000711
Jiang S, Liu Y, Zhang H (2020) Quantitatively evaluating the effects of prior probability distribution and likelihood function models on slope reliability assessment. Rock Soil Mech 41(9):3087–3097
Juang CH, Luo Z, Atamturktur S, Huang H (2013) Bayesian updating of soil parameters for braced excavations using field observations. J Geotech Geoenviron Eng 139(3):395–406. https://doi.org/10.1061/(asce)gt.1943-5606.0000782
Khan A, Sohail A, Zahoora U, Aqsa SQ (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Li J, Wang F (2010) Study on the forecasting models of slope stability under data mining. In: Earth and space 2010: engineering, science, construction, and operations in challenging environments, pp 765–776
Li PF, Guo T, Liu Z (2012) Finite element analysis of parameters sensitivity analysis of landslide stability. Appl Mech Mater 170:903–906
Li DQ, Qi XH, 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. https://doi.org/10.1016/j.strusafe.2013.08.005
Liang RY, Nusier OK, Malkawi AH (1990) A reliability based approach for evaluating the slope stability of embankment dams. Eng Geol 54(3–4):271–285. https://doi.org/10.1016/S0013-7952(99)00017-4
Lin HM, Chang SK, Wu JH, Juang C (2009) Neural network-based model for assessing failure potential of highway slopes in the Alishan. Taiwan Area: pre- and post-earthquake investigation. Eng Geol 104:280–289. https://doi.org/10.1016/j.enggeo.2008.11.007
Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint http://arxiv.org/abs/1312.4400
Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804. https://doi.org/10.1007/s11069-014-1106-7
Lu P, Rosenbaum MS (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30(3):383–398. https://doi.org/10.1023/b:nhaz.0000007168.00673.27
Ma W, Kong L (2009) Genetic-least square support vector machine estimation of slope stability. Rock Soil Mech 30(12):3876–3880. https://doi.org/10.16285/j.rsm.2009.12.056
Meng Y, Xu W, Liu Z, Liu D, Cai D (2010) Analysis of 3D visualization of safety monitoring for complicated high rock slope engineering. Chin J Rock Mech Eng 29(12):2500–2509
Misra D (2019) Mish: a self regularized non-monotonic neural activation function. arXiv preprint http://arxiv.org/abs/1908.08681 4(2):10-48550
Montufar GF, Pascanu R, Cho K, Bengio Y (2014) On the number of linear regions of deep neural networks. In: Advances in neural information processing systems, pp 2924–2932
Ngo PT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12(2):505–519. https://doi.org/10.1016/j.gsf.2020.06.013
Nguyen Q, Mukkamala M, Hein M (2018) Neural networks should be wide enough to learn disconnected decision regions. Preprint http://arxiv.org/abs/1803.00094
Oka Y, Wu TH (1990) System reliability of slope stability. J Geotech Eng 116(8):1185–1189
Park D, Rilett LR (1999) Forecasting freeway link ravel times with a multi-layer feed forward neural network. Comput Aided Civ Inf 4:358–367. https://doi.org/10.1111/0885-9507.00154
Peethambaran B, Anbalagan R, Kanungo DP, Goswami A, Shihabudheen KV (2020) A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas. CATENA 195:104751. https://doi.org/10.1016/j.catena.2020.104751
Peng M, Li XY, Li DQ, Jiang SH, Zhang LM (2014) Slope safety evaluation by integrating multi-source monitoring information. Struct Saf 49:65–74. https://doi.org/10.1016/j.strusafe.2013.08.007
Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Van Le H, Prakash I (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 195:104805. https://doi.org/10.1016/j.catena.2020.104805
Qin Z, Qin P (2010) Evaluation coupling model for high slope stability based on fuzzy analytical hierarchy process—set pair analysis method. Chin J Geotech Eng 32(5):706–711
Rethati L (1988) Probabilistic solutions in geotechnics. Elsevier, New York. https://doi.org/10.1016/c2009-0-09654-8
Sah NK, Sheorey PR, Upadhyaya LN (1994) Maximum likelihood estimation of slope stability. Int J Rock Mech Min Sci Geomech Abstr 31(1):47–53. https://doi.org/10.1016/0148-9062(94)91226-2
Samui P (2013) Support vector classifier analysis of slope. Geomat Nat Hazards Risk 4(1):1–12. https://doi.org/10.1080/19475705.2012.684725
Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica 18(1):53–58. https://doi.org/10.1016/j.scient.2011.03.007
Spencer EE (1967) A method of the analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 17:11–26. https://doi.org/10.1680/geot.1967.17.1.11
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–9. https://doi.org/10.1109/cvpr.2015.7298594
Szegedy C, Iofe S, Vanhoucke V (2016a) Inception-v4, Inception-ResNet and the impact of residual connections on learning. Preprint http://arxiv.org/abs/1602.07261v2 131:262–263. https://doi.org/10.1609/aaai.v31i1.11231
Szegedy C, Vanhoucke V, Iofe S, Shlens J, Wojna Z (2016b) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2818–2826. https://doi.org/10.1109/cvpr.2016.308
Tang Y, Zhang BD, Wu J, Hu T, Zhou J, Liu F (2013) Parallel architecture and optimization for discrete-event simulation of spike neural networks. Sci China Tech Sci 56:509–517. https://doi.org/10.1007/s11431-012-5084-2
Vani S, Rao TM (2019) An experimental approach towards the performance assessment of various optimizers on convolutional neural network. In 2019 3rd international conference on trends in electronics and informatics (ICOEI). IEEE, pp 331–336
Vanmarcke EH (1997) Reliability of earth slopes. J Geotech Eng Div 103(11):1247–1265. https://doi.org/10.1061/ajgeb6.0000518
Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4757-3264-1
Verma D, Kainthola A, Thareja R, Singh TN (2013) Stability analysis of an open cut slope in Wardha valley coal field. J Geol Soc India 81(6):804–812. https://doi.org/10.1007/s12594-013-0105-8
Wang SC (2003) Artificial neural network. Interdisciplinary computing in java programming. Springer, Boston, pp 81–100. https://doi.org/10.1007/springerreference_61728
Wang G, Cui H, Li Q (2009) Investigation of method for determining factors weights in evaluating slope stability based on rough set theory. Rock Soil Mech 30(8):2418–2422. https://doi.org/10.16285/j.rsm.2009.08.058
Wei X, Zhang L, Yang HQ, Zhang L, Yao YP (2021) Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks. Geosci Front 12(1):453–467. https://doi.org/10.1016/j.gsf.2020.04.011
Xue X (2017) Prediction of slope stability based on hybrid PSO and LSSVM. J Comput Civ Eng 31(1):04016041. https://doi.org/10.1061/(asce)cp.1943-5487.0000607
Xue X, Zhang W, Liu H (2008) Evaluation of slope stability based on SOFM neural network. Rock Soil Mech 29(8):2236–2240. https://doi.org/10.16285/j.rsm.2008.08.047
Yan X, Li X (2011) Bayes discriminant analysis method for predicting the stability of open pit slope. In: International conference on electric technology and civil engineering, pp 147–150. https://doi.org/10.1109/icetce.2011.5776304
Zhang LL, Zhang J, Zhang LM, Tang WH (2010) Back analysis of slope failure with Markov chain Monte Carlo simulation. Comput Geotech 37(7–8):905–912. https://doi.org/10.1016/j.compgeo.2010.07.009
Zhang Y, Miyamori Y, Mikami T, Saito T (2019) Vibration-based structural state identification by a 1-dimensional convolutional neural network. Comput Aided Civ Infrastruct Eng 34(9):822–839. https://doi.org/10.1111/mice.12447
Zhao H, Feng X (2003) Application of support vector machines function fitting in slope stability evaluation. Chin J Rock Mech Eng 22(2):241–245
Zhou K, Chen Z (2009) Stability prediction of tailing dam slope based on neural network pattern recognition. In: Second international conference on environmental and computer science, pp 380–383. https://doi.org/10.1109/icecs.2009.55
Zhu H, Fang Q, He H, Hu J, Jiang D, Xu K (2019a) Automatic prediction of meningioma grade image based on data amplification and improved convolutional neural network. Comput Math Methods Med. https://doi.org/10.1155/2019/7289273
Zhu J, Pei J, Zhao Y (2019b) Research on convolution kernel initialization method in convolutional neural network (CNN) training. Signal Process 35(4):641–648
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This work was supported by the National Natural Science Foundation of China (Grant Numbers [51979188] and [52109163]); Visiting Researcher Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science (Grant Numbers [2021SGG03]); and the science and technology projects from Huaneng Power Corporation (Grant Number [HNKJ21-H33]).
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by HJ, SZ, XW, ZM, and XT. The first draft of the manuscript was written by HJ and CW, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Jia, H., Zhang, S., Wang, C. et al. MSC-1DCNN-based homogeneous slope stability state prediction method integrated with empirical data. Nat Hazards 118, 729–753 (2023). https://doi.org/10.1007/s11069-023-06026-6
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DOI: https://doi.org/10.1007/s11069-023-06026-6