Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources

Abstract

Soil liquefaction assessment remains a crucial and complex challenge in seismic geotechnical engineering due to various liquefaction records and limited information, which entails a more generalized off-the-shelf model that can achieve favourable performance on different data sources. In this work, a deep learning model is built and investigated on the soil liquefaction prediction and a modified transfer learning scheme between different data sources is presented. Various datasets, including shear wave velocity-based, CPT-based, SPT-based, and real cases, are collected and utilized to verify the effectiveness and accuracy of the proposed model. Because different data sources in soil liquefaction generally share several geotechnical and mechanical parameters, we work to combine model prior information, feature mapping and data reconstruction in transfer learning models to tackle multi-source domain adaption, which can be further applied to other predictive analysis and facilitate online learning models in geotechnical engineering. Also, the deep learning model is compared with several classical machine learning and ensemble learning models and the modified transfer learning model is formulated by comparing different feature transformation techniques integrated with various feature-based and instance-based transfer learning methods. The accuracy and effectiveness of the deep learning and modified transfer learning models have been validated in the numerical study.

Keywords

Liquefaction / Prediction / Deep learning / Ensemble methods / Transfer learning / Feature-based / Instance-based / Source data / Target data / Deep autoencoder / Variational autoencoder

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of competing interest

Timon Rabczuk is an editorial board member for Underground Space and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

[1] Ahmad, M., Tang, X.-W., Qiu, J.-N., & Ahmad, F. (2019). Evaluating seismic soil liquefaction potential using bayesian belief network and c4. 5 decision tree approaches. Applied Sciences, 9(20):4226.

[2] Alobaidi, M. H., Meguid, M. A., & Chebana, F. (2019). Predicting seismic-induced liquefaction through ensemble learning frameworks. Scientific Reports, 9(1), 1-12.

[3] An, M., Zhang, F., Min, K.-B., Elsworth, D., He, C., & Zhao, L. (2022). Frictional stability of metamorphic epidote in granitoid faults under hydrothermal conditions and implications for injection-induced seismicity. Journal of Geophysical Research: Solid Earth, 127(3), e2021JB023136.

[4] Andrus, R. D., & Stokoe, K. H. II, ( 2000). Liquefaction resistance of soils from shear-wave velocity. Journal of Geotechnical and Geoenvironmental Engineering, 126(11), 1015-1025.

[5] Apicella, A., Donnarumma, F., Isgrò F., & Prevete, R. (2021). A survey on modern trainable activation functions. Neural Networks.

[6] Bao, X., Jin, Z., Cui, H., Chen, X., & Xie, X. (2019). Soil liquefaction mitigation in geotechnical engineering: An overview of recently developed methods. Soil Dynamics and Earthquake Engineering, 120, 273-291.

[7] Bao, X., Xia, Z., Ye, G., Fu, Y., & Su, D. (2017). Numerical analysis on the seismic behavior of a large metro subway tunnel in liquefiable ground. Tunnelling and Underground Space Technology, 66, 91-106.

[8] Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 1-4). Springer.

[9] Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., & Varoquaux, G. (2013). API design for machine learning software:experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. (pp.108-122).

[10] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., et al. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1 (4), 1-4.

[11] Chen, Z., Li, H., Goh, A. T. C., Wu, C., & Zhang, W. (2020). Soil liquefaction assessment using soft computing approaches based on capacity energy concept. Geosciences, 10(9), 330.

[12] Chian, S. C., Tokimatsu, K., & Madabhushi, S. P. G. (2014). Soil liquefaction-induced uplift of underground structures: physical and numerical modeling. Journal of Geotechnical and Geoenvironmental Engineering, 140(10), 04014057.

[13] Chou, J.-S., & Thedja, J. P. P. (2016). Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Automation in Construction, 68, 65-80.

[14] de Mathelin, A., Deheeger, F., Richard, G., Mougeot, M., and Vayatis, N. (2021a). Adapt: Awesome domain adaptation python toolbox. arXiv preprint arXiv:2107.03049.

[15] de Mathelin, A., Richard, G., Deheeger, F., Mougeot, M., & Vayatis, N. (2021b). Adversarial weighting for domain adaptation in regression. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 49-56). IEEE.

[16] de Winter, J. C., Gosling, S. D., & Potter, J. (2016). Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods, 21(3), 273.

[17] Demir, S., & Sahin, E. K. (2023). An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using adaboost, gradient boosting, and xgboost. Neural Computing and Applications, 35(4), 3173-3190.

[18] Dorogush, A.V., Ershov, V., and Gulin, A. (2018). Catboost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.

[19] Fang, Y., Jairi, I., & Pirhadi, N. (2023). Neural transfer learning for soil liquefaction tests. Computers & Geosciences, 171, 105282.

[20] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domainadversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096-2030.

[21] Goh, A. T., & Goh, S. (2007). Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics, 34(5), 410-421.

[22] Goharzay, M., Noorzad, A., Ardakani, A. M., & Jalal, M. (2017). A worldwide spt-based soil liquefaction triggering analysis utilizing gene expression programming and bayesian probabilistic method. Journal of Rock Mechanics and Geotechnical Engineering, 9(4), 683-693.

[23] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

[24] Guo, H., Rabczuk, T., Zhu, Y., Cui, H., Su, C., & Zhuang, X. (2022). Soil liquefaction assessment by using hierarchical gaussian process model with integrated feature and instance based domain adaption for multiple data sources. AI in Civil Engineering, 1(1), 5.

[25] Hanna, A. M., Ural, D., & Saygili, G. (2007). Neural network model for liquefaction potential in soil deposits using turkey and taiwan earthquake data. Soil Dynamics and Earthquake Engineering, 27(6), 521-540.

[26] Hoang, N.-D., & Bui, D. T. (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. Bulletin of Engineering Geology and the Environment, 77(1), 191-204.

[27] Hsein Juang, C., & Chen, C. J. (2000). A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements. International Journal for numerical and analytical methods in geomechanics, 24(1), 1-27.

[28] Huang, J., Gretton, A., Borgwardt, K., Schölkopf, B., & Smola, A. (2006). Correcting sample selection bias by unlabeled data. In Advances in neural information processing systems (pp.19).

[29] Jas, K., & Dodagoudar, G. (2023). Explainable machine learning model for liquefaction potential assessment of soils using xgboost-shap. Soil Dynamics and Earthquake Engineering, 165, 107662.

[30] Jas, K., Mangalathu, S., & Dodagoudar, G. (2024). Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model. Computers and Geotechnics, 167, 106051.

[31] Javdanian, H. (2019a). Evaluation of soil liquefaction potential using energy approach: experimental and statistical investigation. Bulletin of Engineering Geology and the Environment, 78(3), 1697-1708.

[32] Javdanian, H. (2019b). Field data-based modeling of lateral ground surface deformations due to earthquake-induced liquefaction. The European Physical Journal Plus, 134(6), 297.

[33] Juang, C. H., Yuan, H., Lee, D.-H., & Lin, P.-S. (2003). Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. Journal of geotechnical and geoenvironmental engineering, 129(1), 66-80.

[34] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146-3154.

[35] Kingma, D.P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.

[36] Kingma, D.P. and Welling, M. (2019). An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691.

[37] Ku, C.-S., Juang, C. H., Chang, C.-W., & Ching, J. (2012). Probabilistic version of the robertson and wride method for liquefaction evaluation: development and application. Canadian Geotechnical Journal, 49(1), 27-44.

[38] Kurnaz, T. F., & 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.

[39] Kurnaz, T. F., & Kaya, Y. (2019b). Spt-based liquefaction assessment with a novel ensemble model based on gmdh-type neural network. Arabian Journal of Geosciences, 12(15), 456.

[40] Kutanaei, S. S., & Choobbasti, A. J. (2019). Prediction of liquefaction potential of sandy soil around a submarine pipeline under earthquake loading. Journal of Pipeline Systems Engineering and Practice, 10(2), 04019002.

[41] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521 (7553), 436-444.

[42] Liu, B., Vu-Bac, N., Zhuang, X., Fu, X., & Rabczuk, T. (2022a). Stochastic full-range multiscale modeling of thermal conductivity of polymeric carbon nanotubes composites: A machine learning approach. Composite Structures, 289, 115393.

[43] Liu, B., Vu-Bac, N., Zhuang, X., Fu, X., & Rabczuk, T. (2022b). Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology, 224, 109425.

[44] Moss, R., Seed, R. B., Kayen, R. E., Stewart, J. P., Der Kiureghian, A., & Cetin, K. O. (2006). Cpt-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential. Journal of Geotechnical and Geoenvironmental Engineering, 132(8), 1032-1051.

[45] Myers, J. L., Well, A. D., & Lorch, R. F. Jr, (2013). Research design and statistical analysis. Routledge.

[46] Njock, P. G. A., Shen, S.-L., Zhou, A., & Lyu, H.-M. (2020). Evaluation of soil liquefaction using ai technology incorporating a coupled enn/tsne model. Soil Dynamics and Earthquake Engineering, 130, 105988.

[47] Pal, M. (2006). Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10), 983-996.

[48] Pardoe, D., & Stone, P. (2010). Boosting for regression transfer. In Proceedings of the 27th International Conference on Machine Learning (ICML).

[49] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

[50] Samui, P., Sitharam, T., & Contadakis, M. (2011). Machine learning modelling for predicting soil liquefaction susceptibility. Natural Hazards & Earth System Sciences, 11(1).

[51] San Martin, G., López Droguett, E., Meruane, V., & das Chagas Moura, M. (2019). Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis. Structural Health Monitoring, 18(4), 1092-1128.

[52] Shen, Y., El Naggar, M. H., Zhang, D., Huang, Z., & Du, X. (2024). Optimal intensity measure for seismic performance assessment of shield tunnels in liquefiable and non-liquefiable soils. Underground Space.

[53] Sladen, J., D'hollander, R., & Krahn, J. (1985). The liquefaction of sands, a collapse surface approach. Canadian Geotechnical Journal, 22(4), 564-578.

[54] Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P., and Kawanabe, M. (2007). Direct importance estimation with model selection and its application to covariate shift adaptation. Advances in neural information processing systems, 20.

[55] Sun, B., & Saenko, K. (2016). Deep coral:Correlation alignment for deep domain adaptation. In European conference on computer vision. Springer (pp.443-450). Springer.

[56] Székely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007). Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35(6), 2769-2794.

[57] Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.7167-7176).

[58] Unutmaz, B. (2014). 3d liquefaction assessment of soils surrounding circular tunnels. Tunnelling and Underground Space Technology, 40, 85-94.

[59] Wang, Y., Yao, H., & Zhao, S. (2016). Auto-encoder based dimensionality reduction. Neurocomputing, 184, 232-242.

[60] Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big data, 3(1), 1-40.

[61] Xue, X., & Yang, X. (2013). Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Natural Hazards, 67(2), 901-917.

[62] Zhang, G., Robertson, P. K., & Brachman, R. (2004). Estimating liquefaction-induced lateral displacements using the standard penetration test or cone penetration test. Journal of Geotechnical and Geoenvironmental Engineering, 130(8), 861-871.

[63] Zhang, J., & Wang, Y. (2021). An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study. Neural Computing and Applications, 33(5), 1533-1546.

[64] Zhang, W., & Goh, A. T. (2016). Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression. Geomechanics Engineering, 10(3), 269-284.

[65] Zhang, W., & Goh, A. T. (2018). Assessment of soil liquefaction based on capacity energy concept and back-propagation neural networks. In In Integrating Disaster Science and Management (pp.41-51). Elsevier.

[66] Zhang, W., Goh, A. T., Zhang, Y., Chen, Y., & Xiao, Y. (2015). Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines. Engineering Geology, 188, 29-37.

[67] Zhang, Y., Liu, T., Long, M., & Jordan, M. (2019). Bridging theory and algorithm for domain adaptation. In In International Conference on Machine Learning (pp. 7404-7413), PMLR.

[68] 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. Underground Space, 6(2), 126-133.

[69] 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. Journal of Performance of Constructed Facilities, 33(3), 04019024.

[70] Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76.

[71] Zhuang, H., Chen, G., Hu, Z., & Qi, C. (2016). Influence of soil liquefaction on the seismic response of a subway station in model tests. Bulletin of Engineering Geology and the Environment, 75, 1169-1182.

[72] Zhuang, H., Hu, Z., Wang, X., & Chen, G. (2015). Seismic responses of a large underground structure in liquefied soils by fem numerical modelling. Bulletin of Earthquake Engineering, 13, 3645-3668.