Author:
Flor A.,Sassi F.,La Morgia M.,Cernera F.,Amadini F.,Mei A.,Danzi A.
Subject
Geotechnical Engineering and Engineering Geology,Building and Construction
Reference38 articles.
1. Neuro-fuzzy modelling of tbm performance with emphasis on the penetration rate;Alvarez Grima;Memoirs Centre Eng. Geol.,1988
2. Alvarez Grima, M., Bruines, P., Verhoef, P., 2000. Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling and Underground Space Technology 15, 259–269. URL: https://www.scienced irect.com/science/article/pii/S0886779800000559, doi:https: //doi.org/10.1016/S0886-7798(00)00055-9.
3. BBT, Lot Mules 2–3. Application of machine learning on TBM parameters for risk prediction tools;Amadini,2023
4. A comparison of the box-cox maximum likelihood estimator and the non-linear two-stage least squares estimator;Amemiya;J. Econ.,1981
5. Development of hybrid intelligent models for predicting tbm penetration rate in hard rock condition;Armaghani;Tunn. Undergr. Space Technol.,2017
Cited by
7 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