[1]. Careddu, N., Di Capua, G., and Siotto, G. (2019). Dimension stone industry should meet the fundamental values of geoethics. Resources Policy, 63, 101468.
[2]. Mikaeil, R., Ozcelik, Y., Ataei, M., and Shaffiee Haghshenas, S. (2019). Application of harmony search algorithm to evaluate performance of diamond wire saw. Journal of Mining and Environment, 10 (1): 27-36.
[3]. Shamsi, R., Amini, M.S., Dehghani, H., Bascompta, M., Jodeiri Shokri, B., and Entezam, S. (2022). Prediction of Fly-rock using Gene Expression Programming and Teaching–learning-based Optimization Algorithm. Journal of Mining and Environment, 13 (2): 391-406.
[4]. Emami Meybodi, E., Hussain, S.K., Fatehi Marji, M., and Rasouli, V. (2022). Application of Machine Learning Models for Predicting Rock Fracture Toughness Mode-I and Mode-II. Journal of Mining and Environment, 13 (2): 465-480.
[5]. Shaffiee Haghshenas, S., Mikaeil, R., Esmaeilzadeh, A., Careddu, N., and Ataei, M. (2022). Statistical Study to Evaluate Performance of Cutting Machine in Dimension Stone Cutting Process. Journal of Mining and Environment, 13 (1): 53-67.
[6]. Alamdari, S., Basiri, M.H., Mousavi, A., and Soofastaei, A. (2022). Application of Machine Learning Techniques to Predict Haul Truck Fuel Consumption in Open-Pit Mines. Journal of Mining and Environment, 13(1): 69-85.
[7]. Mikaeil, R., Esmaeilzadeh, A., Shaffiee Haghshenas, S., Ataei, M., Hajizadehigdir, S., Jafarpour, A., and Geem, Z. W. (2022). Evaluation of Dimension Stone According to Resistance to Freeze–Thaw Cycling to Use in Cold Regions. Journal of Soft Computing in Civil Engineering, 6 (1): 88-109.
[8]. Mikaeil, R., Esmailzadeh, A., Aghaei, S., Haghshenas, S.S., Jafarpour, A., Mohammadi, J., and Ataei, M. (2021). Evaluating the sawability of rocks by chain-saw machines using the promethee technique. Rudarsko-geološko-naftni zbornik (The Mining-Geological-Petroleum Engineering Bulletin): 36 (1).
[9]. Khilman, T. (2004). Noise pollution in cities, Curitiba and Goteborg as examples. In proceeding of.
[10]. Gradl, C., Eustes, A.W., and Thonhauser, G. (2008, September). An analysis of noise characteristics of drill bits. In SPE Annual Technical Conference and Exhibition. OnePetro.
[11]. Karakurt, I., Aydın, G., and Aydıner, K. Experimental and Statistical Investigation on Noise Level of Diamond Sawblades in Granitic Rock Sawing.
[12]. Kumar, B.R., Vardhan, H., and Govindaraj, M. (2011). Sound level produced during rock drilling vis-à-vis rock properties. Engineering geology, 123 (4): 333-337.
[13]. Obert, L. (1941). Use of sub-audible noises for prediction of rock bursts (Vol. 3555). US Department of the Interior, Bureau of Mines.
[14]. Obert, Leonard, and Wilbur I. Duvall. Use of Sub-audible Noises for the Prediction of Rock Bursts: Part II. US Department of the Interior, Bureau of Mines, 1942.
[15]. Knill, J.L., Franklin, J.A., and Malone, A.W. (1968, January). A study of acoustic emission from stressed rock. In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts (Vol. 5, No. 1, pp. 87-88). Pergamon.
[16]. Hardy, H.R. (1972). Application of acoustic emission techniques to rock mechanics research. Acoustic emission.
[17]. Marceau, J. and Moji, Y. (1973). Application of fracture mechanics testing to process control for adhesive bonding. Document D6–41145, Boeing Commercial Airplane Company.
[18]. Byerlee, J., (1978). Friction of rocks In Rock friction and earthquake prediction. 615–626. Basel: Birkhäuser.
[19]. Zborovjan, M. (2002). Identification of minerals from sound during drilling. Semestral Project. TU-Kosice.
[20]. Zborovjan, M., Lesso, I., and Dorcak, L. (2003). Acoustic identification of rocks during drilling process. Journal of Acta Montanistica Slovaca, 8 (4): 91-93.
[21]. Vardhan, H., Adhikari, G.R., and Raj, M.G. (2009). Estimating rock properties using sound levels produced during drilling. International Journal of Rock Mechanics and Mining Sciences, 46(3): 604-612.
[22]. Yilmaz, I. and Kaynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert systems with applications, 38 (5): 5958-5966.
[23]. Kumar, B.R., Vardhan, H., and Govindaraj, M. (2011). Prediction of uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling. Rock mechanics and rock engineering, 44 (5): 613-620.
[24]. Kumar, B. R., Vardhan, H., Govindaraj, M., and Vijay, G.S. (2013). Regression analysis and ANN models to predict rock properties from sound levels produced during drilling. International Journal of Rock Mechanics and Mining Sciences, 58, 61-72.
[25]. Kharaman, S., Delibalta, M.S., and Comakli, R. (2013). Noise level measurement test to predict the abrasion resistance of rock aggregates. Fluctuation and Noise Letters, 12(04): 1350021.
[26]. Masood. (2015). Estimation of Sound Level Produced During Drilling of Igneous Rock Samples using a Portable Drill set-up. Procedia earth and planetary science, 11, 456-482.
[27]. Delibalta, M.S., Kahraman, S.A.İ.R., and Comakli, R. (2015). The usability of noise level from rock cutting for the prediction of physico-mechanical properties of rocks. Fluctuation and Noise Letters, 14 (01): 1550006.
[28]. Kivade, S. B., Murthy, C.S.N., and Vardhan, H. (2015). ANN models for prediction of sound and penetration rate in percussive drilling. Journal of The Institution of Engineers (India): Series D, 96 (2): 93-103.
[29]. Kumar, C.V., Vardhan, H., Murthy, C.S., and Karmakar, N.C. (2019). Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations. Journal of Rock Mechanics and Geotechnical Engineering, 11(4): 850-859.
[30]. Yari, M. and Bagherpour, R. (2018). Investigating an innovative model for dimensional sedimentary rocks characterization using acoustic frequencies analysis during drilling. Rudarsko-geološko-naftni zbornik, 33(2): 17-25.
[31]. Yari, M. and Bagherpour, R. (2018). Implementing acoustic frequency analysis for development the novel model of determining geomechanical features of igneous rocks using rotary drilling device. Geotechnical and Geological Engineering, 36 (3): 1805-1816.
[32]. Yari, M., Bagherpour, R., and Khoshouei, M. (2019). Developing a novel model for predicting geomechanical features of carbonate rocks based on acoustic frequency processing during drilling. Bulletin of Engineering Geology and the Environment, 78 (3): 1747-1759.
[33]. Piri, M., Mikaeil, R., Hashemolhosseini, H., Baghbanan, A., and Ataei, M. (2021). Study of the effect of drill bits hardness, drilling machine operating parameters and rock mechanical parameters on noise level in hard rock drilling process. Measurement, 167, 108447.
[34]. Aryafar, A., Mikaeil, R., Doulati Ardejani, F., Shaffiee Haghshenas, S., and Jafarpour, A. (2019). Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. Journal of Mining and Environment, 10 (2): 327-337.
[35]. Mohammadi, J., Ataei, M., Kakaei, R.K., Mikaeil, R., and Haghshenas, S.S. (2018). Prediction of the production rate of chain saw machine using the multilayer perceptron (MLP) neural network. Civil Engineering Journal, 4(7): 1575-1583.
[36]. Behnood, A. and Golafshani, E.M. (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54-64.
[37]. Naderpour, H., Rafiean, A.H., and Fakharian, P. (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, 213-219.
[38]. Dormishi, A., Ataei, M., Mikaeil, R., Khalokakaei, R., and Haghshenas, S.S. (2019). Evaluation of gang saws’ performance in the carbonate rock cutting process using feasibility of intelligent approaches. Engineering Science and Technology, an International Journal, 22 (3): 990-1000.
[39]. Hosseini, S.M., Ataei, M., Khalokakaei, R., Mikaeil, R., and Haghshenas, S.S. (2020). Study of the effect of the cooling and lubricant fluid on the cutting performance of dimension stone through artificial intelligence models. Engineering Science and Technology, an International Journal, 23 (1): 71-81.
[40]. Fiorini Morosini, A., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., and Geem, Z.W. (2020). Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach. Applied Sciences, 10 (9): 3029.
[41]. Amiri, M., Hasanipanah, M., and Amnieh, H.B. (2020). Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and item set mining. Neural Computing and Applications, 1-19.
[42]. Armaghani, D.J. and Asteris, P.G. (2021). A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Computing and Applications, 33 (9): 4501-4532.
[43]. Lee, S. K., Lee, H., Back, J., An, K., Yoon, Y., Yum, K., and Hwang, S.U. (2021). Prediction of tire pattern noise in early design stage based on convolutional neural network. Applied Acoustics, 172, 107617.
[44]. Dhiman, N.K., Singh, B., Saini, P.K., and Garg, N. (2021). Design of Optimal Noise Barrier for Metropolitan Cities using Artificial Neural Networks. In Optimization Methods in Engineering (pp. 359-375). Springer, Singapore.
[45]. Akbarzadeh, M., Shaffiee Haghshenas, S., Jalali, S. M.E., Zare, S., and Mikaeil, R. (2022). Developing the Rule of Thumb for Evaluating Penetration Rate of TBM using Binary Classification. Geotechnical and Geological Engineering, 1-19.
[46]. Er, M.J., Wu, S., Lu, J., and Toh, H.L. (2002). Face recognition with radial basis function (RBF) neural networks. IEEE transactions on neural networks, 13(3): 697-710.
[47]. Mahanty, R.N. and Gupta, P.D. (2004). Application of RBF neural network to fault classification and location in transmission lines. IEE Proceedings-Generation, Transmission and Distribution, 151(2): 201-212.
[48]. Seshagiri, S. and Khalil, H.K. (2000). Output feedback control of nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks, 11 (1): 69-79.
[49]. Mohammadi, J., Ataei, M., Kakaie, R., Mikaeil, R., and Haghshenas, S.S. (2019). Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models. Journal of Mining and Environment, 10(4): 1105-1119.
[50]. Hosseini, S.M., Ataei, M., Khalokakaei, R., Mikaeil, R., and Haghshenas, S.S. (2019). Investigating the Role of the Cooling and Lubricant Fluids on the Performance of Cutting Disks (Case Study: Hard Rocks). Rudarsko-geološko-naftni zbornik, 34 (2): 13-25.
[51]. Mikaeil, R., Shaffiee Haghshenas, S., Ozcelik, Y., and Shaffiee Haghshenas, S. (2017). Development of intelligent systems to predict diamond wire saw performance. Journal of Soft Computing in Civil Engineering, 1 (2): 52-69.
[52]. Aryafar, A., Mikaeil, R., Haghshenas, S.S., and Haghshenas, S.S. (2018). Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement, 124, 20-31.
[53]. Mikaeil, R., Haghshenas, S.S., and Hoseinie, S.H. (2018). Rock penetrability classification using artificial bee colony (ABC) algorithm and self-organizing map. Geotechnical and Geological Engineering, 36 (2): 1309-1318.
[54]. Salemi, A., Mikaeil, R., and Haghshenas, S.S. (2018). Integration of finite difference method and genetic algorithm to seismic analysis of circular shallow tunnels (Case study: Tabriz urban railway tunnels). KSCE Journal of Civil Engineering, 22 (5): 1978-1990.
[55]. Mikaeil, R., Bakhshinezhad, H., Haghshenas, S.S., and Ataei, M. (2019). Stability analysis of tunnel support systems using numerical and intelligent simulations (case study: Kouhin Tunnel of Qazvin-Rasht Railway). Rudarsko-geološko-naftni zbornik, 34(2): 1-10.
[56]. Haghshenas, S.S., Faradonbeh, R.S., Mikaeil, R., Haghshenas, S.S., Taheri, A., Saghatforoush, A., and Dormishi, A. (2019). A new conventional criterion for the performance evaluation of gang saw machines. Measurement, 146, 159-170.
[57]. Mikaeil, R., Beigmohammadi, M., Bakhtavar, E., and Haghshenas, S.S. (2019). Assessment of risks of tunneling project in Iran using artificial bee colony algorithm. SN Applied Sciences, 1 (12): 1-9.
[58]. Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Gallelli, V., and Astarita, V. (2020). Development of a Binary Classification Model to Assess Safety in Transportation Systems using GMDH-Type Neural Network Algorithm. Sustainability, 12 (17): 6735.
[59]. Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Astarita, V., and Haghshenas, A.S. (2020). Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy. Sustainability, 12 (18): 7541.
[60]. Noori, A.M., Mikaeil, R., Mokhtarian, M., Haghshenas, S.S., and Foroughi, M. (2020). Feasibility of intelligent models for prediction of utilization factor of TBM. Geotechnical and Geological Engineering, 38(3): 3125-3143.
[61]. Naderpour, H. and Mirrashid, M. (2020). Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex & Intelligent Systems, 6 (1): 97-107.
[62]. Fiorini Morosini, A., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., Choi, D.Y., and Geem, Z.W. (2021). Sensitivity Analysis for Performance Evaluation of a Real Water Distribution System by a Pressure-driven Analysis Approach and Artificial Intelligence Method. Water, 13 (8): 1116.
[63]. Golafshani, E.M. and Behnood, A. (2021). Predicting the mechanical properties of sustainable concrete containing waste foundry sand using multi-objective ANN approach. Construction and Building Materials, 291, 123314.
[64]. Haghshenas, S.S., Haghshenas, S.S., Geem, Z.W., Kim, T.H., Mikaeil, R., Pugliese, L., and Troncone, A. (2021). Application of harmony search algorithm to slope stability analysis. Land, 10 (11): 1250.
[65]. Singh, D., Upadhyay, R., Pannu, H. S., and Leray, D. (2021). Development of an adaptive neuro fuzzy inference system based vehicular traffic noise prediction model. Journal of Ambient Intelligence and Humanized Computing, 12 (2): 2685-2701.
[66]. Senthilselvi, A., Duela, J. S., Prabavathi, R., and Sara, D. (2021). Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise. Journal of Ambient Intelligence and Humanized Computing, 1-6.
[67]. Naderpour, H. and Mirrashid, M. (2020). Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex & Intelligent Systems, 6 (1): 97-107.
[68]. Armaghani, D.J., Harandizadeh, H., and Momeni, E. (2021). Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm. Engineering with Computers, 1-23.
[69]. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23 (3): 665-685.
[70]. Jang, J.S.R., Sun, C.T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10): 1482-1484.
[71]. Dormishi, A.R., Ataei, M., Khaloo Kakaie, R., Mikaeil, R., and Shaffiee Haghshenas, S. (2019). Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. Journal of Mining and Environment, 10(2): 543-557.
[72]. Mikaeil, R., Haghshenas, S.S., Ozcelik, Y., and Gharehgheshlagh, H.H. (2018). Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotechnical and Geological Engineering, 36 (6): 3779-3791.
[73]. Mikaeil, R., Haghshenas, S.S., Haghshenas, S.S., and Ataei, M. (2018). Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Computing and Applications, 29 (6): 283-292.
[74]. Faradonbeh, R.S., Haghshenas, S.S., Taheri, A., and Mikaeil, R. (2020). Application of self-organizing map and fuzzy c-mean techniques for rock-burst clustering in deep underground projects. Neural Computing and Applications, 32 (12): 8545-8559.
[75]. Mikaeil, R., Haghshenas, S.S., and Sedaghati, Z. (2019). Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: The second part of Emamzade Hashem tunnel). Natural Hazards, 97 (3): 1099-1113.
[76]. Fattahi, H. (2017). Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods. Journal of Mining and Environment, 8 (2): 163-177.
[77]. Asgari, M., Kheyroddin, A., and Naderpour, H. (2017). A proposal model for estimation of project success in terms of radial based neural networks: a case study in Iran. Civil Engineering Journal, 3(10): 904-919.
[78]. Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H.A., and Acikalin, S.J.E.G. (2008). Prediction of uniaxial compressive strength of sandstones using petrography-based models. Engineering Geology, 96 (3-4): 141-158.
[79]. Fattahi, H. (2016). Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm, a technique for estimation of TBM penetration rate. Iran University of Science & Technology, 6 (2): 159-171.