Abstract
This study goes into the complex area of predicting soil liquefaction potential, a crucial issue in geotechnical engineering. The work presents a novel viewpoint on forecasting soil liquefaction occurrences by combining traditional geotechnical concepts with cutting-edge machine-learning approaches. The research maintains authenticity and credibility in its conclusions by using real-world datasets from actual liquefaction incidents. While Pearson and Spearman correlation analyses clarify the complex interrelationships between the variables, a fundamental descriptive analysis offers a thorough statistical summary. Three machine learning models—logistic regression, random forest, and SVM—that make use of cutting-edge computational methods constitute the basis of the analytical efforts. Twenty different parameter sets were examined, and among them, sets 4, 12, 2, 8, 16, and 19 stood out as being particularly interesting. Set 4 achieved an accuracy of 86 percent. According to the study's definition of the "Gray zone", a zone of caution should be followed when calculated probabilities are close to the 0.5 decision threshold. The model's reliability and the significance of several geotechnical elements are further highlighted through sensitivity analysis. The research highlights complementary approaches' usefulness in merging traditional geotechnical evaluations with cutting-edge data analytics, as recommended by literature and regulations. To sum up, this study combines conventional geotechnical concepts with cutting-edge analytical methods, providing essential insights for upcoming geotechnical projects in the face of seismic difficulties.
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References
Ahmad M, Tang X-W, Qiu J-N, Ahmad F (2019a) Interpretive structural modeling and MICMAC analysis for identifying and benchmarking significant factors of seismic soil liquefaction. Appl Sci 9(2):233
Ahmad M, Tang X-W, Qiu J-N, Ahmad F (2019b) Evaluating seismic soil liquefaction potential using bayesian belief network and C4.5 decision tree approaches. Appl Sci 9(20):4226. https://doi.org/10.3390/app9204226
Ahmad M, Tang X-W, Qiu J-N, Wen-Jing G, Ahmad F (2020) Assessing the seismic soil liquefaction potential based on CPT through a hybrid approach utilizing Bayesian belief networks. J Cent South Univ 27(2):500–516. https://doi.org/10.1007/s11771-020-4312-3
Ahmad M, Tang X-W, Qiu J-N et al (2021) Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Front Struct Civ Eng 15(2):490–505. https://doi.org/10.1007/s11709-020-0669-5
Ahmad M, Amjad M, Al-Mansob RA, Kamiński P, Olczak P, Khan BJ, Alguno AC (2022) Forecasting liquefaction-induced lateral displacements through gaussian process regression. Appl Sci 12(4):1977. https://doi.org/10.3390/app12041977
Ali T, Lee J, Kim RE (2022). Machine learning tool to assess the earthquake structural safety of systems designed for wind: In application of noise barriers. Earthq Struct 23(3):315-328. https://doi.org/10.12989/eas.2022.23.3.315
Ambraseys N (1988) Engineering seismology: Part II. Earthq Eng Struct Dyn 17(1):1–105. https://doi.org/10.1002/eqe.4290170102
Azizpour M, Raoufi R, Kazeminezhad E. Investigation of the Robust H-Infinity Filter's Effectiveness on the Model Predictive Control and Linear Quadratic Regulator for Active Seismic Control of High-Rise Buildings. Iran J Sci Technol Trans Civ Eng (2023). https://doi.org/10.1007/s40996-023-01216-5
Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, Berlin, Heidelberg, pp 1–4. https://doi.org/10.1007/978-3-642-00296-0_5
Brown R, Miller S (2020) Comparative analysis of data split ratios in machine learning research. J Artif Intell Stud 25(2):87–106
Bureau of Indian Standards (2016) Criteria for earthquake resistant design of structures, part 1: general provisions and buildings (IS 1893), New Delhi
Cetin KO, Der Kiureghian A, Seed RB (2002) Probabilistic models for the initiation of seismic soil liquefaction. Struct Saf 26(3):197–211. https://doi.org/10.1016/S0167-4730(02)00036-X
Cetin KO, Seed RB, Cetin KO, Seed RB, Kayen RES, Moss RES, Bilge HT, Ilgac M, Chowdhury K (2018) Dataset on SPT-based seismic soil liquefaction. Data Brief 20:544–548. https://doi.org/10.1016/j.dib.2018.08.043
Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz TP, Daimlerchrysler C, Shearer R, Wirth (2020) CRISP-DM 1.0 Step-by-step Data Mining Guide
Cubrinovski M, Robinson K, Taylor M, Hughes M, Orense R (2012) Lateral spreading and its impacts in urban areas in the 2010–2011 Christchurch earthquakes. NZ J Geol Geophys 55(3):255–269. https://doi.org/10.1080/00288306.2012.699895
Fathi A, Haeri SM, Mazari M et al (2019) Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver. SN Appl Sci 1:703. https://doi.org/10.1007/s42452-019-0724-9
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Ghani S, Kumari S, Bardhan A (2021) A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models. Sādhanā 46:113. https://doi.org/10.1007/s12046-021-01640-1
Green M, Jackson L, Davis R (2018) Ensuring reproducibility in machine learning experiments: the role of random seed. In: Conference on neural information processing systems, pp 112–125
Guetterman TC (2019) Basics of statistics for primary care research. Fam Med Community Health 7(2):e000067. https://doi.org/10.1136/fmch-2018-000067
Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. Wiley. https://doi.org/10.1002/9781118548387
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
Hu J, Wang J (2023) Influence of data quality on the performance of supervised classification models for predicting gravelly soil liquefaction. Eng Geol 324:107254. https://doi.org/10.1016/j.enggeo.2023.107254
Hu J, Wang J, Zhang Z, Liu H (2022) Continuous-discrete hybrid Bayesian network models for predicting earthquake-induced liquefaction based on the Vs database. Comput Geosci 169:105231
Idriss IM, Boulanger RW (2008) Soil liquefaction during earthquake. EERI Publication, Monograph MNO-12, Earthquake Engineering Research Institute, Oakland. https://www.eeri.org/.
Jain K, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323. https://doi.org/10.1145/331499.331504
James G et al (2013) An introduction to statistical learning, 112. Springer. https://doi.org/10.1007/978-1-4614-7138-7
Jones P, Wang Q (2019) Optimal split ratios in training and test sets for machine learning models. In: Proceedings of the International Conference on artificial intelligence, pp 45–52
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)
Kayen R, Moss RES, Thompson EM, Seed RB, Cetin KO, Kiureghian AD, Tanaka Y, Tokimatsu K (2013) Shear-wave velocity–based probabilistic and deterministic assessment of seismic soil liquefaction potential. J Geotech Geoenviron Eng 139(3):407–419. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000743
Kayen R, Mitchell J, Seed RB, Lodge A, Nishio S, Coutinho R (1992) Evaluation of SPT-, CPT-, and shear wave-based methods for liquefaction potential assessment using Loma Prieta data. In: Proceedings of the Fourth US-Japan Workshop on Earthquake Resistant Design of Lifeline Facilities and Countermeasures for Soil Liquefaction, 1, 111–130
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on artificial intelligence
Kramer SL (1996) Geotechnical earthquake engineering. Prentice Hall
Li L, Zhao X (2019) Application of machine learning in optimized distribution of dampers for structural vibration control. Earthq Struct 16(6):679. https://doi.org/10.12989/eas.2019.16.6.679
Menard S (2002) Applied logistic regression analysis, 2nd edn. Sage. https://doi.org/10.4135/9781412983433
Moeindarbari H, Taghikhany T (2018) Seismic reliability assessment of base-isolated structures using artificial neural network: operation failure of sensitive equipment. Earthq Struct 14(5):425. https://doi.org/10.12989/eas.2018.14.5.425
Murtagh F, Legendre P (2014) Ward’s hierarchical agglomerative clustering method: Which algorithms implement Ward’s criterion? J Classif 31(3):274–295. https://doi.org/10.1007/s00357-014-9161-z
Pirhadi N, Tang X, Yang Q, Kang F (2019) A new equation to evaluate liquefaction triggering using the response surface method and parametric sensitivity analysis. Sustainability 11(1):112. https://doi.org/10.3390/su11010112
Saltelli A, Tarantola S, Campolongo F, Ratto M (2008) Sensitivity analysis practice: a guide to assessing scientific models. Wiley
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 97(9):1249–1273. https://doi.org/10.1061/JSFEAQ.000166
Seed HB, Tokimatsu K, Harder LF, Chung RM (1985) Influence of SPT procedures in soil liquefaction resistance evaluations. J Geotech Eng 111(12):1425–1445. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:12(1425)
Shafiei P, Azadi M, Razzaghi MS (2022) A novel liquefaction prediction framework for seismically excited tunnel lining. Earthq Struct 22(4):401. https://doi.org/10.12989/eas.2022.22.4.401
Smith A, Johnson B, White C (2017) Effective data partitioning for improved model performance in neural networks. J Mach Learn Res 18(3):123–145
Tukey JW (1977) Exploratory data analysis. Addison-Wesley
Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian JT, Dobry R, Finn WDL, Harder LF, Hynes ME, Ishihara K, Koester JP, Liao SSC, Marcuson WF, Martin GR, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stokoe KH (2001) Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Environ Eng 127(10):817–833. https://doi.org/10.1061/(ASCE)1090-0241(2001)127:10(817)
Zarrin M, Asgarian B, Fulad R (2017) A review of factors affecting seismic pile response analysis: a parametric study. Numer Methods CivEng 2(2):35–51. https://doi.org/10.29252/nmce.2.2.35
Zhao J, Wu J, Ding X, Wang M (2017) Elevation extraction and deformation monitoring by multitemporal InSAR of Lupu Bridge in Shanghai. Remote Sens 9(9):897. https://doi.org/10.3390/rs9090897
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 33:04019024
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Abbasimaedeh, P. Soil liquefaction in seismic events: pioneering predictive models using machine learning and advanced regression techniques. Environ Earth Sci 83, 189 (2024). https://doi.org/10.1007/s12665-024-11480-x
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DOI: https://doi.org/10.1007/s12665-024-11480-x