Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geology,Geotechnical Engineering and Engineering Geology
Link
http://link.springer.com/content/pdf/10.1007/s10064-019-01626-8.pdf
Reference91 articles.
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3. Armaghani DJ, Hajihassani M, Sohaei H et al (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8:10937–10950. https://doi.org/10.1007/s12517-015-1984-3
4. Armaghani D, Mohamad E, Hajihassani M (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32:109–121
5. Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) 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
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