Abstract
In soil mechanics, liquefaction is the phenomenon that occurs when saturated, cohesionless soils temporarily lose their strength and stiffness under cyclic loading shaking or earthquake. The present work introduces an optimal performance model by comparing two baselines, thirty tree-based, thirty support vector classifier-based, and fifteen neural network-based models in assessing the liquefaction potential. One hundred and seventy cone penetration test results (liquefied and non-liquefied) have been compiled from the literature for this aim. Earthquake magnitude, vertical-effective stress, mean grain size, cone tip resistance, and peak ground acceleration parameters have been used as input parameters to predict the soil liquefaction potential for the first time. Performance metrics, accuracy, an area under the curve (AUC), precision, recall, and F1 score have measured the training and testing performances. The comparison of performance metrics reveals that the model Runge–Kutta optimized extreme gradient boosting (RUN_XGB) has assessed the liquefaction potential with an overall accuracy of 99%, AUC of 0.99, precision of 0.99, recall value of 1, and F1 score of 1. Moreover, model RUN_XGB has a true negative rate of 0.98, negative predictive value of 1, Matthews correlation coefficient of 0.98, and average classification accuracy of 0.99, close to the ideal values and presents the robustness of the RUN_XGB model. Finally, the RUN_XGB model has been recognized as an optimal performance model for predicting the liquefaction potential. It has been noted that a low multicollinearity level affects the prediction accuracy of models based on conventional soft computing techniques, i.e., logistic regression. This research will help researchers choose suitable hybrid algorithms and enhance the accuracy of seismic soil liquefaction potential models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All data, models, and code generated or used during the study appear in the submitted article.
References
Abbaszadeh Shahri A, Maghsoudi Moud F (2020) Liquefaction potential analysis using hybrid multi-objective intelligence model. Environ Earth Sci 79(19):441. https://doi.org/10.1007/s12665-020-09173-2
Ahmad M, Tang XW, Qiu JN, Ahmad F, Gu WJ (2021) Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Front Struct Civ Eng 15:490–505. https://doi.org/10.1007/s11709-020-0669-5
Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079. https://doi.org/10.1016/j.eswa.2021.115079
Alizadeh Mansouri M, Dabiri R (2021) Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis. SN Appl Sci 3:1–31. https://doi.org/10.1007/s42452-021-04704-3
Ansari A, Zahoor F, Rao KS, Jain AK (2022) Deterministic approach for seismic hazard assessment of Jammu Region, Jammu and Kashmir. In Geo-Congress 2022 (pp. 590–598)
Baltzopoulos G, Baraschino R, Chioccarelli E, Cito P, Vitale A, Iervolino I (2023) Near-source ground motion in the M7.8 Gaziantep (Turkey) earthquake. Earthq Eng Struct Dyn. https://doi.org/10.1002/eqe.3939
Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
Cavaleri L, Chatzarakis GE, Di Trapani F, Douvika MG, Roinos K, Vaxevanidis NM, Asteris PG (2017) Modeling of surface roughness in electro-discharge machining using artificial neural networks. Adv Mater Res 6(2):169. https://doi.org/10.12989/amr.2017.6.2.169
Chan JYL, Leow SMH, Bea KT, Cheng WK, Phoong SW, Hong ZW, Chen YL (2022) Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics 10(8):1283. https://doi.org/10.3390/math10081283
Chen Z, Li H, Goh ATC, Wu C, Zhang W (2020) Soil liquefaction assessment using soft computing approaches based on capacity energy concept. Geosciences 10(9):330. https://doi.org/10.3390/geosciences10090330
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Christensen R (1996) Analysis of variance, design, and regression: applied statistical methods. CRC Press
Clerc M (2010) Particle swarm optimization (Vol. 93). John Wiley & Sons
Das BM, Luo Z (2016) Principles of soil dynamics. Cengage Learning
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 103:2371–2393. https://doi.org/10.1007/s11069-020-04089-3
Demir S, Şahin EK (2021) Assessment of feature selection for liquefaction prediction based on recursive feature elimination. Avrupa Bilim Ve Teknol Derg 28:290–294. https://doi.org/10.31590/ejosat.998033
Demir S, Sahin EK (2022) Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Soil Dyn Earthq Eng 154:107130. https://doi.org/10.1016/j.soildyn.2021.107130
Demir S, Sahin EK (2023) An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost. Neural Comput Appl 35(4):3173–3190. https://doi.org/10.1007/s00521-022-07856-4
Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol 74:103–116. https://doi.org/10.1007/s10064-014-0606-8
Garg A, Tai K (2012) Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem. In 2012 proceedings of international conference on modelling, identification and control (pp. 353–358). IEEE
Garini E, Gazetas G (2023) The 2 earthquakes of February 6th 2023 in Turkey & Syria – Second Preliminary Report. NTUA, Greece
Ge Y, Zhang Z, Zhang J, Huang H (2023) Develo** region-specific fragility function for predicting probability of liquefaction induced ground failure. Probab Eng Mech 71:103381. https://doi.org/10.1016/j.probengmech.2022.103381
Gelman A (2005) Analysis of variance—why it is more important than ever. Ann Stat 33(1):1–53. https://doi.org/10.1214/009053604000001048
Ghani S, Kumari S (2022) Prediction of liquefaction using reliability based regression analysis. In: Choudhary AK (ed) Advances in geo-science and geo-structures : select Proceedings of GSGS 2020. Springer Singapore, Singapore, pp 11–23
Ghani S, Kumari S, Ahmad S (2022) Prediction of the seismic effect on liquefaction behavior of fine-grained soils using artificial intelligence-based hybridized modeling. Arab J Sci Eng 47(4):5411–5441. https://doi.org/10.1007/s13369-022-06697-6
Ghorbani A, Eslami A (2021) Energy-based model for predicting liquefaction potential of sandy soils using evolutionary polynomial regression method. Comput Geotech 129:103867. https://doi.org/10.1016/j.compgeo.2020.103867
Ghorbani A, Jahanpour R, Hasanzadehshooiili H (2020) Evaluation of liquefaction potential of marine sandy soil with piles considering nonlinear seismic soil–pile interaction; A simple predictive model. Mar Georesour Geotechnol 38(1):1–22. https://doi.org/10.1080/1064119X.2018.1550543
Goh AT (1996) Neural-network modeling of CPT seismic liquefaction data. J Geotech Eng 122(1):70–73. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:1(70)
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press
Gunst RF, Webster JT (1975) Regression analysis and problems of multicollinearity. Commun Stat-Theory Methods 4(3):277–292. https://doi.org/10.1080/03610927308827246
Hair J Jr, Wolfnibarger MC, Ortinau DJ, Bush RP (2013) Essentials of marketing. McGraw Hill, New York, USA
Haldar A, Tang WH (1979) Probabilistic evaluation of liquefaction potential. J Geotech Eng Div 105(2):145–163. https://doi.org/10.1061/AJGEB6.0000765
Hastie T, Tibshirani R, Friedman J (2009) Boosting and additive trees. In: Hastie T, Tibshirani R, Friedman J (eds) The elements of statistical learning: data mining, inference, and prediction. Springer, NY, pp 337–387
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028
Hoang ND, Bui DT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Env 77:191–204. https://doi.org/10.1007/s10064-016-0924-0
Hsu SC, Yang MD, Chen MC, Lin, JY (2017) Artificial neural network of liquefaction evaluation for soils with high fines content. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 2643–2649). IEEE. https://doi.org/10.1109/IJCNN.2006.247143
Hu J (2021) A new approach for constructing two Bayesian network models for predicting the liquefaction of gravelly soil. Comput Geotech 137:104304. https://doi.org/10.1016/j.compgeo.2021.104304
Hu J, Liu H (2019) Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases. Eng Geol 254:76–88. https://doi.org/10.1016/j.enggeo.2019.04.003
Huang J, Asteris PG, Pasha SMK, Mohammed AS, Hasanipanah M (2020) A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Eng Comput. https://doi.org/10.1007/s00366-020-01207-4
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York
Jas K, Dodagoudar GR (2023a) Liquefaction potential assessment of soils using machine learning techniques: a state-of-the-art review from 1994–2021. Int J Geomech 23(7):03123002. https://doi.org/10.1061/IJGNAI.GMENG-7788
Jas K, Dodagoudar GR (2023b) Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP. Soil Dyn Earthq Eng 165:107662
Johari A, Khodaparast AR, Javadi AA (2019) An analytical approach to probabilistic modeling of liquefaction based on shear wave velocity. Iran J Sci Technol Trans Civ Eng 43:263–275. https://doi.org/10.1007/s40996-018-0163-7
Johnston R, Jones K, Manley D (2018) Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Qual Quant 52:1957–1976
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inform Process Syst 30
Khan S, Sasmal SK, Kumar GS, Behera RN (2021) Assessment of liquefaction potential based on SPT data by using machine learning approach. In: Sitharam TG (ed) Seismic Hazards and Risk Select Proceedings of 7th ICRAGEE 2020. Springer Singapore, Singapore, pp 145–156. https://doi.org/10.1007/978-981-15-9976-7_14
Khatti J, Grover KS (2023a) Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, gaussian process regression, and gene expression programming. Multiscale Multidiscip Model Exp Des. https://doi.org/10.1007/s41939-022-00137-6
Khatti J, Grover KS (2023b) Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2022.12.034
Khatti J, Grover KS (2021) relationship between index properties and CBR of soil and prediction of CBR. In: Muthukkumaran K et al (eds) Indian Geotechnical Conference. Springer, Singapore, pp 171–185
Kim HY (2017) Statistical notes for clinical researchers: Chi-squared test and Fisher’s exact test. Restor Dent Endod 42(2):152–155. https://doi.org/10.5395/rde.2017.42.2.152
Kim HS, Kim M, Baise LG, Kim B (2021) Local and regional evaluation of liquefaction potential index and liquefaction severity number for liquefaction-induced sand boils in Pohang, South Korea. Soil Dyn Earthq Eng 141:106459. https://doi.org/10.1016/j.soildyn.2020.106459
Kingma D, Ba J (2015) Adam: A method for stochastic optimization in: Proceedings of the 3rd international conference for learning representations (iclr'15). San Diego, 500
Kumar D, Samui P, Kim D, Singh A (2021) A novel methodology to classify soil liquefaction using deep learning. Geotech Geol Eng 39:1049–1058. https://doi.org/10.1007/s10706-020-01544-7
Kumar DR, Samui P, Burman A (2022a) Determination of best criteria for evaluation of liquefaction potential of soil. Transp Infrastruct Geotechnol. https://doi.org/10.1007/s40515-022-00268-w
Kumar DR, Samui P, Burman A (2022b) Prediction of probability of liquefaction using hybrid ANN with optimization techniques. Arab J Geosci 15(20):1587. https://doi.org/10.1007/s12517-022-10855-3
Kumar DR, Samui P, Burman A (2022c) Prediction of probability of liquefaction using soft computing techniques. J Inst Eng (india) Series A 103(4):1195–1208. https://doi.org/10.1007/s40030-022-00683-9
Kumar DR, Samui P, Burman A, Wipulanusat W, Keawsawasvong S (2023a) Liquefaction susceptibility using machine learning based on SPT data. Intell Syst Appl 20:200281. https://doi.org/10.1016/j.iswa.2023.200281
Kumar DR, Samui P, Burman A, Kumar S (2023b) Seismically induced liquefaction potential assessment by different artificial intelligence procedures. Transp Infrastruct Geotechnol. https://doi.org/10.1007/s40515-023-00327-w
Kumar DR, Samui P, Burman A (2023c) Suitability assessment of the best liquefaction analysis procedure based on SPT data. Multiscale Multidiscip Model Exp Des 6(2):319–329. https://doi.org/10.1007/s41939-023-00148-x
Kurnaz TF, Kaya Y (2019a) A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction. Environmental Earth Sciences 78(11):339. https://doi.org/10.1007/s12665-019-8344-7
Kurnaz TF, Kaya Y (2019b) SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network. Arab J Geosci 12:1–14. https://doi.org/10.1007/s12517-019-4640-5
Kurnaz TF, Erden C, Kökçam AH, Dağdeviren U, Demir AS (2023) A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction. Eng Geol 319:107109. https://doi.org/10.1016/j.enggeo.2023.107109
Kutanaei SS, Choobbasti AJ (2019) Prediction of liquefaction potential of sandy soil around a submarine pipeline under earthquake loading. J Pipeline Syst Eng Pract 10(2):04019002. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000349
Larson MG (2008) Analysis of variance. Circulation 117(1):115–121. https://doi.org/10.1161/CIRCULATIONAHA.107.654335
Lawley DN (1938) A generalization of Fisher’s z test. Biometrika 30(1/2):180–187. https://doi.org/10.2307/2332232
Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055
Lin T, Stich SU, Patel KK, Jaggi M (2020) Don't Use Large Mini-Batches, Use Local SGD. s.l., ICLR 2020
Lin CC, Mudholkar GS (1980) A simple test for normality against asymmetric alternatives. Biometrika 67(2):455–461. https://doi.org/10.1093/biomet/67.2.455
Lu S, Koopialipoor M, Asteris PG, Bahri M, Armaghani DJ (2020) A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs. Materials 13(17):3902. https://doi.org/10.3390/ma13173902
Mahmood A, Tang XW, Qiu JN, Gu WJ, Feezan A (2020) A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks. J Cent South Univ 27(2):500–516. https://doi.org/10.1007/s11771-020-4312-3
Mansfield ER, Helms BP (1982) Detecting multicollinearity. Am Stat 36(3a):158–160. https://doi.org/10.1080/00031305.1982.10482818
Mase LZ, Agustina S, Hardiansyah MF, Supriani F, Tanapalungkorn W, Likitlersuang S (2023) Application of simplified energy concept for liquefaction prediction in Bengkulu City Indonesia. Geotech Geol Eng 41(3):1999–2021. https://doi.org/10.1007/s10706-023-02388-7413(1999),pp.10,2021.1007/s10706-023-02388-7
Menard S (2002) Applied logistic regression analysis (No. 106). Sage.
Mola-Abasi H, Kordtabar B, Kordnaeij A (2018) Liquefaction prediction using CPT data by triangular chart identification. Int J Geotech Eng 12(4):377–382. https://doi.org/10.1080/19386362.2017.1282399
Nejad AS, Güler E, Özturan M (2018) Evaluation of liquefaction potential using random forest method and shear wave velocity results. In 2018 International Conference on Applied Mathematics & Computational Science (ICAMCS. NET) (pp. 23–233). IEEE. https://doi.org/10.1109/ICAMCS.NET46018.2018.00012
Njock PGA, Shen SL, Zhou A, Lyu HM (2020) Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model. Soil Dyn Earthq Eng 130:105988. https://doi.org/10.1016/j.soildyn.2019.105988
Nong Z, Park SS, Jeong SW, Lee DE (2020) Effect of cyclic loading frequency on liquefaction prediction of sand. Appl Sci 10(13):4502. https://doi.org/10.3390/app10134502
Obite CP, Olewuezi NP, Ugwuanyim GU, Bartholomew DC (2020) Multicollinearity effect in regression analysis: a feed forward artificial neural network approach. Asian J Probab Stat 6(1):22–33
Ozsagir M, Erden C, Bol E, Sert S, Özocak A (2022) Machine learning approaches for prediction of fine-grained soils liquefaction. Comput Geotech 152:105014. https://doi.org/10.1016/j.compgeo.2022.105014
Park SS, Ogun**mi PD, Woo SW, Lee DE (2020) A simple and sustainable prediction method of liquefaction-induced settlement at Pohang using an artificial neural network. Sustainability 12(10):4001. https://doi.org/10.3390/su12104001
Rahbarzare A, Azadi M (2019) Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine. Bull Eng Geol Env 78(7):4977–4987. https://doi.org/10.1007/s10064-018-01445-3
Robertson PK, Campanella RG (1985) Liquefaction potential of sands using the CPT. J Geotech Eng 111(3):384–403. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:3(384)
Rokach L (2019) Ensemble learning: pattern classification using ensemble methods. World Sci. https://doi.org/10.1142/9789811201967_0001
Rollins KM, Amoroso S, Milana G, Minarelli L, Vassallo M, Di Giulio G (2020) Gravel liquefaction assessment using the dynamic cone penetration test based on field performance from the 1976 Friuli earthquake. J Geotech Geoenviron Eng 146(6):04020038. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002252
Sabbar AS, Chegenizadeh A, Nikraz H (2019) Prediction of liquefaction susceptibility of clean sandy soils using artificial intelligence techniques. Indian Geotech J 49:58–69. https://doi.org/10.1007/s40098-017-0288-9
Sahin EK, Demir S (2023) Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential. Eng Appl Artif Intell 119:105732. https://doi.org/10.1016/j.engappai.2022.105732
Samui P, Hariharan R (2015) A unified classification model for modeling of seismic liquefaction potential of soil based on CPT. J Adv Res 6(4):587–592. https://doi.org/10.1016/j.jare.2014.02.002
Sawyer SF (2009) Analysis of variance: the fundamental concepts. J Man Manip Ther 17(2):27E-38E. https://doi.org/10.1179/jmt.2009.17.2.27E
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J the Soil Mech Found Div 97(9):1249–1273. https://doi.org/10.1061/JSFEAQ.0001662
Sharma M, Satyam N, Reddy KR (2021) State of the art review of emerging and biogeotechnical methods for liquefaction mitigation in sands. J Hazard Toxic Radioact Waste 25(1):03120002. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000557
Shrestha N (2020) Detecting multicollinearity in regression analysis. Am J Appl Math Stat 8(2):39–42
Sun P, Huang D, Du S (2023) Improving soil liquefaction prediction through an extensive database and innovative ground motion characterization: a case study of Port Island liquefied site. Soil Dyn Earthq Eng 165:107696. https://doi.org/10.1016/j.soildyn.2022.107696
Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300. https://doi.org/10.1023/A:1018628609742
Tang XW, Bai X, Hu JL, Qiu JN (2018) Assessment of liquefaction-induced hazards using Bayesian networks based on standard penetration test data. Nat Hazard 18(5):1451–1468
Toprak S, Holzer TL, Bennett MJ, Tinsley III JC (1999) CPT-and SPT-based probabilistic assessment of liquefaction. In Proc., 7th US–Japan Workshop on Earthquake Resistant Design of Lifeline Facilities and Countermeasures against Liquefaction (pp. 69–86). Buffalo, NY: Multidisciplinary Center for Earthquake Engineering Research
Vittinghoff E, Glidden DV, Shiboski SC, McCulloch,CE (2006) Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. Springer New York, NY. https://doi.org/10.1007/978-1-4614-1353-0
Witten D, James G (2013) An introduction to statistical learning with applications in R. springer publication
**ng DU, Yongfu SU, Yupeng SO, Binghui SO, **aolong ZH, Shasha SO, Yue WA (2020) Multilayer perception neural network for assessment and prediction of earthquake-induced sand liquefaction. 工程地质学报 28(6):1425–1432. https://doi.org/10.13544/j.cnki.jeg.2019-321
Xue X, Yang X (2016) Seismic liquefaction potential assessed by support vector machines approaches. Bull Eng Geol Environ 75:153–162. https://doi.org/10.1007/s10064-015-0741-x
Youd TL (2018) Application of MLR procedure for prediction of liquefaction-induced lateral spread displacement. J Geotech Geoenviron Eng 144(6):04018033. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001860
Youd TL, Noble SK (1997) Liquefaction criteria based on statistical and probabilistic analyses. In Proc., NCEER Workshop on Evaluation of Liquefaction Resistance of Soils. State Univ. of New York, Buffalo, NY, pp. 201–205
Yu H, Miao YU, Subhamoy B (2013) Review on liquefaction-induced damages of soils and foundations during 2011 of the Pacific Coast of Tohoku Earthquake (Japan). Chin J Geotech Eng 35(5):834–840
Zhang W, Goh AT (2016) Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression. Geomech Eng 10(3):269–284. https://doi.org/10.12989/gae.2016.10.3.269
Zhang J, **ao S, Huang H, Zhou J (2020) Calibrating a standard penetration test based method for region-specific liquefaction potential assessment. Bull Eng Geol Env 79:5185–5204. https://doi.org/10.1007/s10064-020-01815-w
Zhang Y, Qiu J, Zhang Y, **e Y (2021a) The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction. Environ Earth Sci 80:1–9. https://doi.org/10.1007/s12665-021-09648-w
Zhang Y, **e Y, Zhang Y, Qiu J, Wu S (2021b) The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity. Bull Eng Geol Env 80:5053–5060. https://doi.org/10.1007/s10064-021-02250-1
Zhang YG, Qiu J, Zhang Y, Wei Y (2021c) The adoption of ELM to the prediction of soil liquefaction based on CPT. Nat Hazards 107(1):539–549. https://doi.org/10.1007/s11069-021-04594-z
Zhang X, He B, Sabri MMS, Al-Bahrani M, Ulrikh DV (2022) Soil liquefaction prediction based on Bayesian optimization and support vector machines. Sustainability 14(19):11944. https://doi.org/10.3390/su141911944
Zheng G, Zhang W, Zhang W, Zhou H, Yang P (2021) Neural network and support vector machine models for the prediction of the liquefaction-induced uplift displacement of tunnels. Undergr Space 6(2):126–133. https://doi.org/10.1016/j.undsp.2019.12.002
Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001292
Zhou J, Huang S, Wang M, Qiu Y (2021a) Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Eng Comput. https://doi.org/10.1007/s00366-021-01418-3
Zhou J, Huang S, Zhou T, Armaghani DJ, Qiu Y (2022) Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential. Artif Intell Rev 55(7):5673–5705. https://doi.org/10.1007/s10462-022-10140-5
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
Jitendra Khatti and Yewuhalashet Fissha: Main author, conceptualization, literature review, manuscript preparation, methodological development, statistical analysis, detailing, and overall analysis; Kamaldeep Singh Grover, Hajime Ikeda, Hisatoshi Toriya, Tsuyoshi Adachi, and Youhei Kawamura: Main author, detailing, overall analysis; conceptualization, comprehensive analysis, manuscript finalization, detailed review, and editing.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Khatti, J., Fissha, Y., Grover, K.S. et al. Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00447-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41939-024-00447-x