DOI: 10.1155/2018/6490169. Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

10.1155/2018/6490169

Crossref journal-article

Wiley

Advances in Civil Engineering (311)

Abstract

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.

Bibliography

Prayogo, D., & Susanto, Y. T. T. (2018). Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel SelfâTuning Least Squares Support Vector Machine. Advances in Civil Engineering, 2018(1). Portico.

Authors 2

Doddy Prayogo (first)
Yudas Tadeus Teddy Susanto (additional)

References 38 Referenced 33

{'key': 'e_1_2_8_1_2', 'article-title': 'Jack-in pile design–Malaysian experience and design approach to EC7', 'volume': '7', 'author': 'Chow C.', 'year': '2009', 'journal-title': 'IEM Course on Eurocode'} / IEM Course on Eurocode / Jack-in pile design–Malaysian experience and design approach to EC7 by Chow C. (2009)
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{'key': 'e_1_2_8_27_2', 'first-page': '293', 'article-title': 'SOS optimization model for bridge life cycle risk evaluation and maintenance strategies', 'volume': '26', 'author': 'Cheng M.-Y.', 'year': '2014', 'journal-title': 'Journal of the Chinese Institute of Civil and Hydraulic Engineering'} / Journal of the Chinese Institute of Civil and Hydraulic Engineering / SOS optimization model for bridge life cycle risk evaluation and maintenance strategies by Cheng M.-Y. (2014)
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VijayvergiyaV. A new way to predict capacity of piles in clay 2 Proceedings of 4th Annual Offshore Technology Conference May 1972 Houston TX USA 865–871.
FlaateK.andSelnesP. Side friction of piles in clay Proceedings of the 9th International Conference on Soil Mechanics and Foundation Engineering July 1977 Tokyo Japan 517–522.
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Dates

Type	When
Created	7 years, 5 months ago (March 20, 2018, 7:33 p.m.)
Deposited	1 year ago (Aug. 6, 2024, 9:26 a.m.)
Indexed	1 month ago (July 30, 2025, 9:16 a.m.)
Issued	7 years, 8 months ago (Jan. 1, 2018)
Published	7 years, 8 months ago (Jan. 1, 2018)
Published Online	7 years, 5 months ago (March 20, 2018)
Published Print	7 years, 8 months ago (Jan. 1, 2018)

Funders 0

None

BibTeX

@article{Prayogo_2018, title={Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine}, volume={2018}, ISSN={1687-8094}, url={http://dx.doi.org/10.1155/2018/6490169}, DOI={10.1155/2018/6490169}, number={1}, journal={Advances in Civil Engineering}, publisher={Wiley}, author={Prayogo, Doddy and Susanto, Yudas Tadeus Teddy}, editor={Benedetto, Andrea}, year={2018}, month=jan }

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