Abstract
Establishing a soil liquefaction prediction model with high accuracy is a critical way to evaluate the quality of in situ and prevent the loss caused by seismic. In this paper, considering the advantage of cone penetration test (CPT) over standard penetration test (SPT) and the suitability for dealing with the nonlinear problems of the extreme learning machine (ELM), the ELM was tried to train the prediction model. Firstly, seven prediction parameters were analyzed and determined; then 226 CPT samples were divided into the training set and test set; then the parameter of ELM model was assured by comparing the training accuracy and speed of model when setting the number of the neuron of the hidden layer from 5 to 16 and the activation function as \({\text{sig}}\), \({\text{sin}}\), \({\text{hardlim}}\). Finally, the performance of the established ELM model was tested through the test set. The results showed the accuracy of using function \({\text{sin}}\) was 81.43% and 87.50% for the training set and test set, respectively; at the same time, the operation was 1.5055 s which was not much different from other two functions. The prediction model based on CPT perform better than that of SPT and can obtain a highly accurate prediction of 100% for the liquefied case and overall accuracy of 87.5%. ELM was proved to be feasible to be used and developed into the in situ evaluation.
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References
Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Evaluating seismic soil liquefaction potential using Bayesian belief network and C4. 5 decision tree approaches. Appl Sci 9(20):4226
Das SK, Mohanty R, Mohanty M, Mahamaya M (2020) Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods. Nat Hazards. https://doi.org/10.1007/s11069-020-04089-3
Goh ATC, Goh SH (2007) Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34(5):410–421. https://doi.org/10.1016/j.compgeo.2007.06.001
Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186. https://doi.org/10.1016/j.enggeo.2017.01.016
Idriss IM, Boulanger RW (2004) Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dyn Earthq Eng 26(2–4):115–130. https://doi.org/10.1016/j.soildyn.2004.11.023
Juang CH, Chen CJ, Rosowsky DV, Tang WH (2000) CPT-based liquefaction analysis, Part 2: reliability for design. Geotechnique 50(5):593–599. https://doi.org/10.1680/geot.2000.50.5.593
Juang CH, Chen CH, Mayne PW (2008) CPTu simplified stress-based model for evaluating soil liquefaction potential. Soils Found 48(6):755–770. https://doi.org/10.3208/sandf.48.755
Juang CH, Yuan H, Lee DH, Lin PS (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng 129(1):66–80. https://doi.org/10.1061/(ASCE)1090-0241(2003)129:1(66)
Kohestani VR, Hassanlourad M, Ardakani A (2015) Evaluation of liquefaction potential based on CPT data using random forest. Nat Hazards 79(2):1079–1089. https://doi.org/10.1007/s11069-015-1893-5
Moss RES, Seed RB, Kayen RE, Stewart JP, Der Kiureghian A, Cetin KO (2006) CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential. J Geotech Geoenviron Eng 132(8):1032–1051. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:8(1032)
Muduli PK, Das SK (2015) First-order reliability method for probabilistic evaluation of liquefaction potential of soil using genetic programming. Int J Geomech 15(3):1–16. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000377
Nejad AS, Guler E, Ozturan M (2018). Evaluation of liquefaction potential using random forest method and shear wave velocity results. In: Proceedings: 2018 international conference on applied mathematics and computational science, ICAMCS.NET 2018, pp 23–26. https://doi.org/10.1109/ICAMCS.NET46018.2018.00012
Olsen RS (1997) Cyclic liquefaction based on the cone penetrometer test. In: NCEER Workshop on Evaluation of Liquefaction Resistance of Soils, (February), pp 225–276
Robertson P (2009) Performance based earthquake design using the CPT. In: Performance-based design in earthquake geotechnical engineering, (2009). https://doi.org/10.1201/noe0415556149.ch1
Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459. https://doi.org/10.1139/t98-017
Sadoghi Yazdi J, Kalantary F, Sadoghi Yazdi H (2012) Prediction of liquefaction potential based on CPT up-sampling. Comput Geosci 44:10–23. https://doi.org/10.1016/j.cageo.2012.03.025
Safa M, Sari PA, Shariati M, Suhatril M, Trung NT, Wakil K, Khorami M (2020) Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes. Physica A 550:124046. https://doi.org/10.1016/j.physa.2019.124046
Samui P (2007) Seismic liquefaction potential assessment by using relevance vector machine. Earthq Eng Eng Vibr 6(4):331–336. https://doi.org/10.1007/s11803-007-0766-7
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97(9):1249–1273
Shariati M, Mafipour MS, Ghahremani B, Azarhomayun F, Ahmadi M, Trung NT, Shariati A (2020a) A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng Comput. https://doi.org/10.1007/s00366-020-01081-0
Shariati M, Mafipour MS, Mehrabi P, Shariati A, Toghroli A, Trung NT, Salih MNA (2020b) A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Eng Comput. https://doi.org/10.1007/s00366-019-00930-x
Xue X, Yang X (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67(2):901–917. https://doi.org/10.1007/s11069-013-0615-0
Zhang J, Gu G (2005) Study of CPT for liquefaction estimation of sands with thin clay interlayer in Shanghai Area. Rock Soil Mech 26(10):1652–1656 (in Chinese)
Zhang YG, Tang J, He ZY, Tan J, Li C (2020) A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide. Nat Hazards. https://doi.org/10.1007/s11069-020-04337-6
Zhang YG, Tang J, Liao RP, Zhang MF, Zhang Y, Wang XM, Su ZY (2020) Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction. Stochastic Environ Res Risk Assess. https://doi.org/10.1007/s00477-020-01920-y
Zhang Y, Yang L (2020) A novel dynamic predictive method of water inrush from coal floor based on gated recurrent unit model. Nat Hazards. https://doi.org/10.1007/s11069-020-04388-9
Zhao X, Fourie A, Qi C (2019) An analytical solution for evaluating the safety of an exposed face in a paste backfill stope incorporating the arching phenomenon. Int J Miner Metall Mater 26(10):1206–1216. https://doi.org/10.1007/s12613-019-1885-7
Zhao X, Fourie A, Qi C (2020) Mechanics and safety issues in tailing-based backfill: a review. Int J Miner Metall Mater 27:1165–1178. https://doi.org/10.1007/s12613-020-2004-5
Zhou Y, Zhou N, Gong L, Jiang M (2020) Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 204:117894. https://doi.org/10.1016/j.energy.2020.117894
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All authors contributed to the study conception and design. Material preparation and data collection were performed by YZ; data analysis and model parameter determination were performed by JQ who should be considered as a co-first author; the first draft of the manuscript was written by YZ; YZ proposed the idea of this research and supervised the structure; YW reviewed this manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zhang, Yg., Qiu, J., Zhang, Y. et al. The adoption of ELM to the prediction of soil liquefaction based on CPT. Nat Hazards 107, 539–549 (2021). https://doi.org/10.1007/s11069-021-04594-z
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DOI: https://doi.org/10.1007/s11069-021-04594-z