This study presents a support vector machine (SVM)-based approach for predicting earthquake liquefaction. The SVM model database includes five indexes: earthquake magnitude, total overburden pressure, effective overburden pressure, qc values from cone penetration tests (CPT), and peak ground acceleration. The proposed model was trained and tested on a dataset comprising 466 field liquefaction performance records and CPT measurements. A grid search method with k-fold cross-validation was also used to verify the feasibility. Compared with an artificial neural network (ANN)–based method, the SVM-based method has the advantage of increased accuracy and simpler operation. Experimental results show that the proposed SVM approach can increase the classification accuracy rate to a standard of 98.71%.
Lee, Ching-Yinn and Chern, Shuh-Gi
"APPLICATION OF A SUPPORT VECTOR MACHINE FOR LIQUEFACTION ASSESSMENT,"
Journal of Marine Science and Technology: Vol. 21:
3, Article 10.
Available at: https://jmstt.ntou.edu.tw/journal/vol21/iss3/10