Abdolreza, Y.-C., & Siamak, H. Y. (2013). A new model to predict roadheader performance using rock mass properties. Journal of Coal Science Engineering, 19(1), 51-56.

Avunduk, E., Tumaç, D., & Atalay, A. (2014). Prediction of roadheader performance by artificial neural network. Tunnelling Underground Space Technology, 44, 3-9.

Bilgin, N., Kuzu, C., Eskikaya, S., & Özdemir, L. (1997). Cutting performance of jack hammers and roadheaders in Istanbul Metro drivages. World Tunnel Congress.

Ebrahimabadi, A., Azimipour, M., & Bahreini, A. (2015). Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM). Journal of Rock Mechanics Geotechnical Engineering, 7(5), 573-583.

Faradonbeh, R. S., Salimi, A., Monjezi, M., Ebrahimabadi, A., & Moormann, C. (2017). Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques. Environmental earth sciences, 76(16), 1-12.

Fattahi, H. (2016). Application of improved support vector regression model for prediction of deformation modulus of a rock mass [journal article]. Engineering with Computers, 32(4), 567-580. https://doi.org/10.1007/s00366-016-0433-6.

Fattahi, H. (2017). Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values. Analytical and numerical methods in mining engineering, 6, 11-24.

Fattahi, H., & Babanouri, N. (2017). Applying optimized support vector regression models for prediction of tunnel boring machine performance. Geotechnical and Geological Engineering, 35(5), 2205-2217.

Fattahi, H., & Babanouri, N. (2017). Predicting tensile strength of rocks from physical properties based on support vector regression optimized by cultural algorithm. Journal of Mining and Environment, 8(3), 467-474.

Fattahi, H., & Bazdar, H. (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunnelling and Underground Space Technology, 70, 114-124.

Fattahi, H., & Shirinzade, M. A. (2022). Applying different soft computing methods to predict mechanical properties of carbonate rocks based on petrographic and physical properties. Earth Science Informatics, 15(1), 351-368. https://doi.org/10.1007/s12145-021-00736-w.

Gao, B., Wang, R., Lin, C., Guo, X., Liu, B., & Zhang, W. (2021). TBM penetration rate prediction based on the long short-term memory neural network. Underground Space, 6(6), 718-731.

Ghasemi, E. (2017). Development of a site-specific regression model for assessment of road-header cutting performance of Tabas coal mine based on rock properties. Journal of Mining Environment, 8(4), 663-672.

Goktan, R., & Gunes, N. (2005). A comparative study of Schmidt hammer testing procedures with reference to rock cutting machine performance prediction. International journal of rock mechanics mining sciences, 42(3), 466-472.

Iphar, M. (2012). ANN and ANFIS performance prediction models for hydraulic impact hammers. Tunnelling Underground Space Technology, 27(1), 23-29.

Jonak, J., Kuric, I., DROŹDZIEL, P., Gajewski, J., & Saga, M. (2020). Prediction of load on the cutting tools in tunnel boring machines. Acta Montanistica Slovaca, 25(4).

Li, F., Li, Y., Yan, C., Ma, C., Liu, C., & Suo, Q. (2022). Swing speed control strategy of fuzzy PID roadheader based on PSO-BP Algorithm. 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC).

Mashrei, M. A. (2012). Neural network and adaptive neuro-fuzzy inference system applied to civil engineering problems. Fuzzy Inference System-Theory Applications.

Nicodemus, K. K. J. B. i. b. (2011). Letter to the editor: On the stability and ranking of predictors from random forest variable importance measures. 12(4), 369-373.

Rostami, J., Ozdemir, L., & Neil, D. M. (1995). Performance prediction: a key issue in mechanical hard rock mining. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts.

Salsani, A., Daneshian, J., Shariati, S., Yazdani-Chamzini, A., & Taheri, M. (2014). Predicting roadheader performance by using artificial neural network. Neural Computing Applications, 24(7), 1823-1831.

Seker, S. E., & Ocak, I. (2019). Performance prediction of roadheaders using ensemble machine learning techniques. Neural Computing Applications, 31(4), 1103.

Shahriar, K. (1988). Rock cuttability and geotechnical factors affecting the penetration rates of roadheaders Ph D Thesis, Istanbul Technical University.

Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.

Weisberg, S. (2005). Applied linear regression (Vol. 528). John Wiley & Sons.

Wu, X., & Kumar, V. (2009). The top ten algorithms in data mining. CRC press.

Yu, H., & Kim, S. (2012). SVM Tutorial-Classification, Regression and Ranking. Handbook of Natural computing, 1, 479-506.