Earthquake-Induced Liquefaction Manifestation Multiclass Prediction Utilizing Random Forests for the Canterbury Earthquake Sequence
Publication: Geo-Congress 2024
ABSTRACT
The abundance of post-earthquake data from the Canterbury, New Zealand (NZ), area can be leveraged for exploring machine learning (ML) opportunities for geotechnical earthquake engineering. Herein, random forest (RF) is chosen as the ML model to be utilized as it is a powerful non-parametric classification model that can also calculate global feature importance post-model building. The results and procedure are presented of building a multiclass liquefaction manifestation classification RF model with features engineered to preserve special relationships. The RF model hyperparameters are optimized with a two-step fivefold cross-validation grid search to avoid overfitting. The overall model accuracy is 96% over six ordinal categories predicting over the Canterbury earthquake sequence measurements from 2010, 2011, and 2016. The resultant RF model can serve as a blueprint for incorporation of other sources of physical data such as geological maps to widen the bounds of model usability.
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REFERENCES
Bassal, P. C., Boulanger, R. W., and DeJong, J. T. (2022). System Response of an Interlayered Deposit with Spatially Distributed Ground Deformations in the Chi-Chi Earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 148(10), 05022004.
Bengfort, B., et al. (2018). Yellowbrick (0.9.1).
Beyzaei, C. Z., Bray, J. D., van Ballegooy, S., Cubrinovski, M., and Bastin, S. (2018). Depositional environment effects on observed liquefaction performance in silt swamps during the Canterbury earthquake sequence. Soil Dynamics and Earthquake Engineering, 107, 303–321.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Cubrinovski, M., and Robinson, K. (2016). Lateral spreading: Evidence and interpretation from the 2010–2011 Christchurch earthquakes. Soil Dynamics and Earthquake Engineering, 91, 187–201.
Cubrinovski, M., Bray, J. D., Taylor, M., Giorgini, S., Bradley, B., Wotherspoon, L., and Zupan, J. (2011). Soil Liquefaction Effects in the Central Business District during the February 2011 Christchurch Earthquake. Seismological Research Letters, 82(6), 893–904.
Cubrinovski, M., Rhodes, A., Ntritsos, N., and Van Ballegooy, S. (2019). System response of liquefiable deposits. Soil Dynamics and Earthquake Engineering, 124, 212–229.
Durante, M. G., and Rathje, E. M. (2021). An exploration of the use of machine learning to predict lateral spreading. Earthquake Spectra, 875529302110046.
Geyin, M., Maurer, B. W., Bradley, B. A., Green, R. A., and van Ballegooy, S. (2021). CPT-based liquefaction case histories compiled from three earthquakes in Canterbury, New Zealand. Earthquake Spectra, 37(4), 2920–2945.
Geyin, M., Maurer, B. W., and Christofferson, K. (2022). An AI driven, mechanistically grounded geospatial liquefaction model for rapid response and scenario planning. Soil Dynamics and Earthquake Engineering, 159, 107348.
Harris, C. R., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357–362.
Maurer, B. W., Green, R. A., Cubrinovski, M., and Bradley, B. A. (2015). Assessment of CPT-based methods for liquefaction evaluation in a liquefaction potential index framework. Géotechnique, 65(5), 328–336.
McKinney, W. (2010). Data Structures for Statistical Computing in Python. In S. van der Walt and J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 56–61).
Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Robertson, P. K. (2010). Soil behaviour type from the CPT: An update. 2nd International Symposium on Cone Penetration Testing, (pp. 56).
Van Ballegooy, S., Malan, P., Lacrosse, V., Jacka, M. E., Cubrinovski, M., Bray, J. D., O’Rourke, T. D., Crawford, S. A., and Cowan, H. (2014). Assessment of Liquefaction-Induced Land Damage for Residential Christchurch. Earthquake Spectra, 30(1), 31–55.
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Geo-Congress 2024
Pages: 222 - 231
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Published online: Feb 22, 2024
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