Transfer component analysis-driven domain adaptation approach for estimating the life of tunnel boring machine disc cutters

Author:

Loy-Benitez JorgeORCID,Lee Hyun-Koo,Kyu Song Myung,Choi YoHyunORCID,Seungwon Lee Sean

Funder

Korea Agency for Infrastructure Technology Advancement

National Research Foundation of Korea

Publisher

Elsevier BV

Reference31 articles.

1. A review and case study of artificial intelligence and machine learning methods used for ground condition prediction ahead of tunnel boring machines;Ayawah;Tunn. Undergr. Sp. Technol.,2022

2. Transfer learning for reinforcement learning on a physical robot. proc. adapt. Learn. agents work;Barrett;ALA 2010 - conjunction with 9th int Conf. Auton. Agents Multiagent Syst. AAMAS,2010

3. Brochu, E., Cora, V.M., de Freitas, N., 2010. A Tutorial on Bayesian Optimization of Expensive Cost Functions. arXiv abs/1012.2.

4. Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with bayesian TPE optimization and SHAP feature analysis;Chen;Acta Geotech.,2023

5. Robotic replacement for disc cutters in tunnel boring machines;Du;Autom. Constr.,2022

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