Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725 Monjezi M, Ahmadi M, Sheikhan A, Bahrami M, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dyn Earthq Eng 30:1233–1236 Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-015-0720-2 Jahed Armaghani D, Hasanipanah M, Mohamad ET (2015) A combination of the ICA-ANN model to predict air overpressure resulting from blasting. Eng Comput. doi:10.1007/s00366-015-0408-z Saghatforoush A, Monjezi M, Faradonbeh RS, Jahed Armaghani D (2015) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput. doi:10.1007/s00366-015-0415-0 Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geolog Eng 23:249–262 Ozer U, Kahriman A, Aksoy M, Adiguzel D, Karadogan A (2008) The analysis of ground vibrations induced by bench blasting at Akyol quarry and practical blasting charts. Environ Geol 54:737–743 Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27:225–233 Faramarzi F, Ebrahimi Farsangi MA, Mansouri H (2014) Simultaneous investigation of blast induced ground vibration and airblast effects on safety level of structures and human in surface blasting. Int J Min Sci Technol 24(5):663–669 Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25(6):1011–1015 Bureau of Indian Standard (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull IS-6922 Kahriman A (2002) Analysis of ground vibrations caused by bench blasting at can open-pit lignite mine in Turkey. Environ Earth Sci 41:653–661 Singh TN (2004) Artificial neural network approach for prediction and control of ground vibrations in mines. Min Technol 113(4):251–256 Sawmliana C, Roy PP, Singh RK, Singh TN (2007) Blast induced air overpressure and its prediction using artificial neural network. Min Technol 116(2):41–48 Duvall WI, Petkof B (1959) Spherical propagation of explosion of generated strain pulses in rocks. USBM, RI-5483 Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New York Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineer 217:553–559 Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses: rock mechanics in engineering practices. Wiley, London Roy PP (1993) Putting ground vibration predictors into practice. Colliery Guard 241:63–67 Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22(7–8):1685–1693 Hudaverdi T (2012) Application of multivariate analysis for prediction of blast-induced ground vibrations. Soil Dyn Earthq Eng 43:300–308 Khandelwal M, Singh TN (2009) Prediction of blasting induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222 Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886 Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851 Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453 Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50 Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neurofuzzy inference system. Environ Geol 56:97–107 Jahed Armaghani D, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860 Fisne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174:461–470 Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770 Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghani D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297 Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J South Afr Inst Min Metall 115(7):637–643 Hajihassani M, Jahed Armaghani D, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74:2799–2817 Liang M, Mohamad ET, Faradonbeh RS, Armaghani DJ, Ghoraba S (2016) Rock strength assessment based on regression tree technique. Eng Comput. doi:10.1007/s00366-015-0429-7 Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99:51–60 Tiryaki B (2009) Estimating rock cuttability using regression trees and artificial neural networks. Rock Mech Rock Eng 42:939–946 Henderson BL, Bui EN, Moran CJ, Simon DAP (2005) Australia-wide predictions of soil properties using decision trees. Geoderma 124(3):383–398 Gandomi AH, Fridline MM, Roke DA (2013) Decision tree approach for soil liquefaction assessment. Sci World J 2013:346285 Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemom 18:275–285 Chou JS (2012) Comparison of multilabel classification models to forecast project dispute resolutions. Expert Syst 39:10202–10211 Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping. Comput Geosci 51:350–365 Michael JA, Gordon SL (1997) Data mining technique: for marketing, sales and customer support. Wiley, New York Loh WY, Shih YS (1997) Split selection methods for classification trees. Stat Sin 7:815–840 Breiman L, Friedman J, Olshen R, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont Quinlan JR (1986) Introduction of decision trees. Mach Learn 1:81–106 Biggs D, Ville BD, Suen E (1991) A method of choosing multiway partitions for classification and decision trees. J Appl Stat 18:49–62 Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8(12):10819–10832 Jahed Armaghani D, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2015) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput. doi:10.1007/s00366-015-0410-5 Jahed Armaghani D, Mohamad ET, Momeni E, Monjezi M, Narayanasamy MS (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci. doi:10.1007/s12517-015-2057-3 Inc SPSS (2007) SPSS for windows (Version 160). SPSS Inc, Chicago