A novel gradient boosting regression tree technique optimized by improved sparrow search algorithm for predicting TBM penetration rate
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s12517-022-09665-4.pdf
Reference58 articles.
1. Abdulhammed OY (2021) Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. J Supercomput. https://doi.org/10.1007/s11227-021-03989-w
2. Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11:779–789. https://doi.org/10.1016/j.jrmge.2019.01.002
3. Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017a) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009
4. Armaghani DJ, Shoib RSNSBR, Faizi K, Rashid ASA (2017b) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405. https://doi.org/10.1007/s00521-015-2072-z
5. Barton N (1999) TBM performance estimation in rock using Q(TBM). Tunn Tunn Int 31:30–34
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