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Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine
Joint Authors
Prayogo, Doddy
Susanto, Yudas Tadeus Teddy
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil.
The hybrid approach uses LS-SVM as a supervised-learning-based predictor to build an accurate input-output relationship of the dataset and SOS method to optimize the σ and γ parameters of the LS-SVM.
Evaluation and investigation of the ST-LSSVM were conducted on 45 training data and 20 testing data of driven pile load tests that were compiled from previous studies.
The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE).
The comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and MAE.
This comprehensive evaluation confirmed the capability of hybrid approach SOS and LS-SVM to modeling the accurate friction capacity of driven piles in clay.
It makes for a reliable and robust assistance tool in helping all geotechnical engineers estimate friction pile capacity.
American Psychological Association (APA)
Prayogo, Doddy& Susanto, Yudas Tadeus Teddy. 2018. Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine. Advances in Civil Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1116412
Modern Language Association (MLA)
Prayogo, Doddy& Susanto, Yudas Tadeus Teddy. Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine. Advances in Civil Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1116412
American Medical Association (AMA)
Prayogo, Doddy& Susanto, Yudas Tadeus Teddy. Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine. Advances in Civil Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1116412
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1116412