This research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (STLSSVM),\nto accurately model the friction capacity of driven piles in cohesive soil. The hybrid approach uses LS-SVM as\na supervised-learning-based predictor to build an accurate input-output relationship of the dataset and SOS method to optimize\nthe ÃÆ? and c parameters of the LS-SVM. Evaluation and investigation of the ST-LSSVM were conducted on 45 training data and 20\ntesting data of driven pile load tests that were compiled from previous studies. The prediction accuracy of the ST-LSSVM was then\ncompared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by\nneural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error\n(RMSE). The comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and\nMAE. This comprehensive evaluation confirmed the capability of hybrid approach SOS and LS-SVM to modeling the accurate\nfriction capacity of driven piles in clay. It makes for a reliable and robust assistance tool in helping all geotechnical engineers\nestimate friction pile capacity.
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