PENGGUNAAN ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI LOAD-SETTLEMENT CURVE PADA FONDASI TIANG

Raden Harya Dananjaya, Sutrisno Sutrisno, Damian Paska Santyo Brahman

Abstract

Dalam beberapa tahun terakhir Artificial Neural Network telah diterapkan pada banyak masalah geoteknik. Berhubungan dengan desain fondasi tiang, prediksi penurunan tiang yang akurat diperlukan untuk memastikan kinerja struktural yang sesuai. Pada penelitian ini bertujuan untuk membangun model ANN untuk memprediksi penurunan tiang berdasarkan data uji CPT. Data yang diperoleh dari literatur, digunakan untuk mengembangkan model. Selain itu dalam penelitian ini membahas parameter terbaik untuk mendapatkan model yang optimal. Akhirnya pada penelitian ini membandingkan prediksi yang diperoleh ANN dengan static load test di lapangan dengan metode validasi menggunakan k-fold cross validation. Setelah melakukan training dan testing didapatkan hasil uji koefisien determinasi sebesar 0,77 dan RMSE 174 kN. Hasil cross validation koefisien determinasi adalah 0,68.

References

Aljarah, I., Faris, H. and Mirjalili, S., 2018. Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22, pp.1-15.

Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31. https://doi.org/10.1016/S0167-7012(00)00201-3

Bradshaw, A. (2006). Design and Construction of Driven Pile Foundations-Lessons Learned on the Central Artery/Tunnel Project.

Chen, W., Sarir, P., Bui, X.N., Nguyen, H., Tahir, M.M. and Jahed Armaghani, D., 2020. Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Engineering with Computers, 36, pp.1101-1115.

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623

Eslami, A., Moshfeghi, S., MolaAbasi, H., & Eslami, M. M. (2020). CPT equipment, performance, and records. In Piezocone and Cone Penetration Test (CPTu and CPT) Applications in Foundation Engineering (pp. 55–80). Elsevier. https://doi.org/10.1016/B978-0-08-102766-0.00003-1

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. https://doi.org/10.1016/0893-6080(89)90020-8

Jong, S.C., Ong, D.E.L. and Oh, E., 2021. State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction. Tunnelling and Underground Space Technology, 113, p.103946.

Luthfiani, F., Nurhuda, I., & Atmanto, I. D. (2017). ANALISIS PENURUNAN BANGUNAN PONDASI TIANG PANCANG DAN RAKIT PADA PROYEK PEMBANGUNAN APARTEMEN SURABAYA CENTRAL BUSINESS DISTRICT. JURNAL KARYA TEKNIK SIPIL, 6(2), 166–179. http://ejournal-s1.undip.ac.id/index.php/jkts

Murlidhar, B.R., Sinha, R.K., Mohamad, E.T., Sonkar, R. and Khorami, M., 2020. The effects of particle swarm optimisation and genetic algorithm on ANN results in predicting pile bearing capacity. International Journal of Hydromechatronics, 3(1), pp.69-87.

Pham, T.A., Tran, V.Q., Vu, H.L.T. and Ly, H.B., 2020. Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLoS One, 15(12), p.e0243030.

Pooya Nejad, F., Jaksa, M. B., Kakhi, M., & McCabe, B. A. (2009). Prediction of pile settlement using artificial neural networks based on standard penetration test data. Computers and Geotechnics, 36(7), 1125–1133. https://doi.org/10.1016/j.compgeo.2009.04.003

Raissi, M., Perdikaris, P. and Karniadakis, G.E., 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378, pp.686-707.

Shahin, M. A. (2016). State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers, 7(1), 33–44. https://doi.org/10.1016/j.gsf.2014.10.002

Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y. and Ding, X., 2021. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, pp.1-41.

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