A hybridized intelligence model to improve the predictability level of strength index parameters of rocks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-020-05223-9.pdf
Reference57 articles.
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2. Abbaszadeh Shahri A, Maghsoudi Moud F, Mirfallah Lialestani SP (2020) A hybrid computing model to predict rock strength index properties using support vector regression. Eng Comput. https://doi.org/10.1007/s00366-020-01078-9
3. Abbaszadeh Shahri A, Asheghi R (2018) Optimized developed artificial neural network-based models to predict the blast-induced ground vibration. Innov Infrastruct Solut. https://doi.org/10.1007/s41062-018-0137-4
4. Abbaszadeh Shahri A, Larsson S, Johansson F (2016) Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test. Innov Infrastruct Solut 1:17. https://doi.org/10.1007/s41062-016-0016-9
5. Abdechiri M, Faez K, Bahrami H (2010) Neural network learning based on chaotic imperialist competitive algorithm. In: IEEE proceedings of the 2nd international workshop on intelligent systems and applications (ISA), pp 1–5, https://doi.org/10.1109/iwisa.2010.5473247
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