Artificial intelligence in underground development: a study of TBM performance

Author:

Benardos A.1

Affiliation:

1. National Technical University of Athens, School of Mining & Metallurgical Engineering, Greece

Publisher

WIT Press

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms;Engineering Applications of Artificial Intelligence;2024-10

2. Artificial Neural Networks as a Tool to Understand Complex Energy Poverty Relationships: The Case of Greece;Energies;2024-06-27

3. A novel long-short term memory network approach for stress model updating for excavations in high stress environments;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2023-01-02

4. Exploring Energy Poverty Indicators Through Artificial Neural Networks;Artificial Intelligence and Sustainable Computing;2022

5. Machine learning to inform tunnelling operations: recent advances and future trends;Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction;2020-12

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