[1] Project, T.M.L., EPB TBM Cutter Head. 2019.
[2] Benardos, A., Artificial intelligence in underground development: a study of TBM performance. Underground Spaces: Design, Engineering and Environmental Aspects, 2008. 102: p. 121.
[3] Bazargan, S., et al., Analysis of the Performance of Cutting Tools of Tunnel Boring Machine (TBM) in Silty-Sand Soils Using Artificial Neural Network (ANN) – Case Study: Tabriz Metro Line 2 Project. Asian Journal of Water, Environment and Pollution, 2022. 19(2): p. 71-78.
[4] Darbor, M., H. Chakeri, and M. Asgharzadeh Dizaj, Investigation of the Effect of Different Parameters on the Penetration Rate of Earth Pressure Balance Boring Machine using Fuzzy and Neuro-Fuzzy Methods, and Metaheuristic Algorithms (A Case Study: Tabriz Metro Line 2). Journal of Analytical and Numerical Methods in Mining Engineering, 2020. 10(25): p. 43-60.
[5] Nickjou Tabrizi, A.H., et al., The effect of TBM operational parameters on the wear of cutting tools using a tunnel boring machine laboratory simulator. Journal of Analytical and Numerical Methods in Mining Engineering, 2022. 12(33): p. 55-63.
[6] Khodaee Ashestani, S., et al., Estimating Penetration Rate of Excavation Machine Using Geotechnical Parameters and Neural Networks in Tabriz Metro. Journal of Analytical and Numerical Methods in Mining Engineering, 2023. 13(37): p. 1-9.
[7] Khoshzaher, E., et al., The effects of water content and grain size on the clogging and abrasivity of fine-grained soils in mechanized excavation. Rudarsko-geološko-naftni zbornik, 2023. 38(2): p. 65-74.
[8] Darbor, M., H. Chakeri, and T. Ansari, The Influence of Soil Particle Size Distribution on the Abrasion of EPB Machine Cutting Tools. Ferdowsi Civil Engineering, 2023. 35(4): p. 17-34.
[9] Ansari, T., et al., Investigating Effect of Soil Grading Parameters on Tool Wear in Mechanized Tunneling using EPB-TBM Machine. Journal of Mining and Environment, 2024. 15(1): p. 301-321.
[10] Amoun, S. and H. Chakeri, Cutting Tools Wear in Soft Ground Tunneling: Field and Experimental Insights. Journal of Mining and Environment, 2024. 15(3): p. 1103-1129.
[11] Maleki, A., et al., Experimental study of the effects of mechanical properties of rocks on wear of cutting tools using a new small-scale linear cutting machine (LCM). Journal of Mining and Environment, 2025.
[12] Chakeri, H., et al., Experimental and numerical investigation of the TBM disc cutter wear using a new tunnel boring machine laboratory simulator. Heliyon, 2024. 10(17): p. e37148.
[13] Chakeri, H., et al., Laboratory and numerical investigation of cutting tool performance using a new small-scale linear cutting machine. Scientific Reports, 2025. 15(1): p. 22337.
[14] Yagiz, S., et al., Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence, 2009. 22(4): p. 808-814.
[15] Gholamnejad, J. and N. Tayarani, Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Mining Science and Technology (China), 2010. 20(5): p. 727-733.
[16] Yagiz, S. and H. Karahan, Prediction of hard rock TBM penetration rate using particle swarm optimization. International Journal of Rock Mechanics and Mining Sciences, 2011. 48(3): p. 427-433.
[17] Torabi, S.R., et al., Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS. Arabian Journal of Geosciences, 2013. 6(4): p. 1215-1227.
[18] Salimi, A., et al., Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunnelling and Underground Space Technology, 2016. 58: p. 236-246.
[19] Gao, L. and X.-b. Li, Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions. Journal of Central South University, 2015. 22(1): p. 290-295.
[20] Jamshidi, A., Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis. Modeling Earth Systems and Environment, 2018. 4(1): p. 383-394.
[21] Fatemi, S.A., M. Ahmadi, and J. Rostami, Evaluation of TBM performance prediction models and sensitivity analysis of input parameters. Bulletin of Engineering Geology and the Environment, 2018. 77(2): p. 501-513.
[22] Yang, X.-S. Firefly Algorithms for Multimodal Optimization. 2009. Berlin, Heidelberg: Springer Berlin Heidelberg.
[23] Sayadi, M., R. Ramezanian, and N. Ghaffari-Nasab, A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 2010. 1(1): p. 1-10.
[24] Łukasik, S. and S. Żak. Firefly Algorithm for Continuous Constrained Optimization Tasks. 2009. Berlin, Heidelberg: Springer Berlin Heidelberg.
[25] Yang, X.-S., Nature-inspired metaheuristic algorithms. 2010: Luniver press.
[26] Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proceedings of ICNN'95 - International Conference on Neural Networks. 1995.
[27] Shi, Y. and R. Eberhart. A modified particle swarm optimizer. in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). 1998.
[28] Xinchao, Z., A perturbed particle swarm algorithm for numerical optimization. Applied Soft Computing, 2010. 10(1): p. 119-124.
[29] Zhan, Z.H., et al., Adaptive Particle Swarm Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009. 39(6): p. 1362-1381.
[30] Draper, N., Applied regression analysis. 1998: McGraw-Hill. Inc.
[31] Slinker, B.K. and S.A. Glantz, Primer of applied regression and analysis of variance. 1990: McGraw-Hill.
[32] Steel, R. and J. Torrie, Principles and procedures of statistics. 1960: McGraw-Hill Book Company, Inc., New York, Toronto, London. xvi + 481 pp.
[33] Ghasemi, E., S. Yagiz, and M. Ataei, Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bulletin of Engineering Geology and the Environment, 2014. 73(1): p. 23-35.
