No abstract

is 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.e 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 c 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. e 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). e comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and MAE. is 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.

Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior.

Pondasi adalah bagian dari struktur bangunan yang berfungsi meneruskan beban struktur atas ke lapisan tanah dengan aman. Sementara pondasi dangkal digunakan apabila lapisan tanah keras terletak dekat dengan permukaan tanah. Untuk mendapatkan hasil desain pondasi dangkal yang optimal, terdapat tiga kriteria penting yang harus diperhatikan yaitu Ultimate Limit State (ULS), Serviceability Limit State (SLS), dan ekonomis. Sehingga, penggunaan metode optimasi yang baik akan membantu menghasilkan dimensi pondasi yang optimal dan ekonomis namun tetap memenuhi syarat aman. Penelitian-penelitian sebelumnya mengindikasikan bahwa metode metaheuristik dapat digunakan sebagai alternatif yang mampu menyelesaikan permasalahan optimasi yang ada. Oleh karena itu, penelitian ini menggunakan metode metaheuristik Particle Swarm Optimization (PSO) dan Symbiotic Organisms Search (SOS) untuk menyelesaikan permasalahan optimasi pondasi dangkal. Pada penelitian ini, optimasi pondasi dangkal dilakukan terhadap pondasi setempat untuk studi kasus bangunan dua lantai. PSO dan SOS bekerja untuk menemukan solusi dimensi pondasi setempat yang diharapkan dapat memiliki biaya konstruksi terendah dan dibatasi oleh constraint dari SNI 8460:2017, SNI 2847:2013, dan bearing capacity theory. Hasil penelitian menunjukkan bahwa metode metaheuristik mampu menemukan dimensi pondasi dangkal yang optimal untuk masing-masing studi kasus. Selain itu, dapat dilihat apabila algoritma SOS memiliki performa yang lebih baik dari PSO.