A case study of tunnel boring machines advance rate prediction using meta-heuristic techniques
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12517-024-11979-4.pdf
Reference69 articles.
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2. Afradi A, Ebrahimabadi A, Hallajian T (2022) Prediction of TBM penetration rate using fuzzy logic, particle swarm optimization and harmony search algorithm. Geotech Geol Eng 40:1513–1536. https://doi.org/10.1007/s10706-021-01982-x
3. Akbarzadeh M, Shaffiee Haghshenas S, Jalali SME et al (2022) Developing the rule of thumb for evaluating penetration rate of TBM, using binary classification. Geotech Geol Eng 40:4685–4703. https://doi.org/10.1007/s10706-022-02178-7
4. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12:783–789. https://doi.org/10.1016/S0893-6080(99)00032-5
5. Arbabsiar MH, Farsangi MAE, Mansouri H (2020) A new model for predicting the advance rate of a tunnel boring machine (TBM) in hard rock conditions. Rud Geol Naft Zb 35:57–74. https://doi.org/10.17794/rgn.2020.2.6
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