Abstract
Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise model for the prediction of AOp would be much beneficial to control the AOp. To this end, the present study proposes a new hybrid of group method of data handling (GMDH) and genetic algorithm (GA). In the other words, the GA is used to optimize the GMDH. The proposed GMDH–GA model was constructed, trained, and tested based on a collection of 84 actual datasets collected from the Shur river dam region. In the modeling, four input parameters were considered: maximum charge per delay, distance between the blasting point and monitoring station, powder factor and rock mass rating. The coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF), as the statistical performance indices, were used to evaluate the accuracy of the proposed GMDH–GA model. Consequently, the results indicate that the predicted values using the GMDH–GA model are in excellent agreement with the actual data (with the R2 of 0.988), which demonstrate the reliability of the GMDH–GA model.
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References
Hasanipanah M, Shahnazar A, Arab H, Golzar SB, Amiri M (2017) Developing a new hybrid-AI model to predict blast induced backbreak. Eng Comput 33(3):349–359
Hasanipanah M et al (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci 76(1):27
Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808
Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297
Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643
Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34(2):339–345
Hasanipanah M, Jahed Armaghani D, Amnieh HB, Koopialipoor M, Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng 36:2247–2260
Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717
Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast induced airblast using a modified conjugate FR method. Measurement 131:35–41
Koopialipoor M et al (2019) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 35:243–256
Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Control Worldw 36:7–16
Armaghani DJ, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Mon Assess 187:1–13
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
Kuzu C, Fisne A, Ercelebi SG (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70:404–411
Rodríguez R, Toraño J, Menéndez M (2007) Prediction of the airblast wave effects near a tunnel advanced by drilling and blasting. Tunn Undergr Sp Technol 22:241–251
Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31
Siskind DE, Stachura VJ, Stagg MS, Koop JW (1980) Structure response and damage produced by airblast from surface mining. United States Bureau of Mines, Washington, D.C.
Konya CJ, Walter EJ (1990) Surface blast design. Prentice Hall, Englewood Cliffs
Hajihassani M, Jahed Armaghani D, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67
Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32:631–644. https://doi.org/10.1007/s00366-016-0442-5
Faradonbeh RS, Hasanipanah M, Amnieh HB, Jahed Armaghani D, Monjezi M (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190(6):351
Jahed Armaghani D, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171
Hasanipanah M, Jahed Armaghani D, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455
Wiss JF, Linehan PW (1978) Control of vibration and blast noise from surface coal mining. Wiss, Janney, Elstner and Associates Inc, Northbrook
Rosenthal MF, Morlock GL (1987) Blasting guidance manual, office of surface mining reclamation and enforcement. US Department of the Interior
Segarra P, Domingo JF, López LM, Sanchidrián JA, Ortega MF (2010) Prediction of near field overpressure from quarry blasting. Appl Acoust 71:1169–1176
Hasanipanah M, Jahed Armaghani D, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050
Koopialipoor M et al (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Env. https://doi.org/10.1007/s10064-018-1349-8
Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700
Toghroli A et al (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17(5):623–639
Toghroli A et al (2016) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf. https://doi.org/10.1007/s10845-016-1217-y
Safa M et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21(3):679–688
Koopialipoor M, Jahed Armaghani D, Haghighi M, Noroozi Ghaleini E (2018) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1116-2
Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208–219
Moayedi H, Jahed Armaghani D (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34(2):347–356
Mosallanezhad M, Moayedi H (2017) Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci 10(22):479–489
Mansouri I, Shariati M, Safa M, Ibrahim Z, Tahir MM, Petković D (2017) Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf. https://doi.org/10.1007/s10845-017-1306-6
Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33(4):951–959
Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Mohamad ET (2016) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2577-0
Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024
Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715
Gordan B et al (2018) Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Eng Comput. https://doi.org/10.1007/s00366-018-0642-2
Koopialipoor M et al (2018) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput. https://doi.org/10.1007/s00500-018-3253-3
Koopialipoor M et al (2018) Overbreak prediction and optimization in tunnel using neural network and bee colony techniques. Eng Comput. https://doi.org/10.1007/s00366-018-0658-7
Noroozi Ghaleini E et al (2018) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Eng Comput. https://doi.org/10.1007/s00366-018-0625-3
Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4:427–433
Mohamad ET, Hajihassani M, Armaghani DJ, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Modell Simul 5:2501–2506
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
Nariman-Zadeh N, Darvizeh A, Ahmad-Zadeh GR (2003) Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc Inst Mech Eng Part B J Eng Manuf 217(6):779–790
Khalkhali A, Safikhani H (2012) Pareto based multi-objective optimization of a cyclone vortex finder using CFD, GMDH type neural networks and genetic algorithms. Eng Optim 44(1):105–118
Zjavka L (2012) Recognition of generalized patterns by a differential polynomial neural network. Eng Technol Appl Sci Res 2(1):167–172
Mokfi T, Shahnazar A, Bakhshayeshi I, Derakhsh AM, Tabrizi O (2018) Proposing of a new soft computing-based model to predict peak particle velocity induced by blasting. Eng Comput 34(4):881–888
Rad HN, Jalali Z (2018) Modification of rock mass rating system using soft computing techniques. Eng Comput. https://doi.org/10.1007/s00366-018-0667-6
Armaghani DJ, Hasanipanah M, Mahdiyar A, Majid MZA, Amnieh HB, Tahir MM (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29(9):619–629
Tonnizam Mohamad E et al (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75:174
Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22(7–8):1637–1643
Behzadafshar K, Sarafraz ME, Hasanipanah M, Mojtahedi SFF, Tahir MM (2017) Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results. Bull Eng Geol Env. https://doi.org/10.1007/s10064-017-1210-5
Hasanipanah M, Shirani Faradonbeh R, Bakhshandeh Amnieh H, Jahed Armaghani D, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307–316
Hasanipanah M et al (2016) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-017-1395-y
Hasanipanah M, Naderi R, Kashir J, Noorani SA, Qaleh AZA (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179. https://doi.org/10.1007/s00366-016-0462-1
Jahed Armaghani D et al (2016) Risk assessment and prediction of flyrock distance by combined multiple regression analysis and monte carlo simulation of quarry blasting. Rock Mech Rock Eng 49(9):3631–3641
Mahdiyar A et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 33(4):807–817
Shahnazar A et al (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci 76(15):527
Mojtahedi SFF, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2018) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput. https://doi.org/10.1007/s00366-018-0582-x
Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK (2019) Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manag 183(3):137–148
Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2019) Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng Comput. https://doi.org/10.1007/s00366-018-00694-w
Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput. https://doi.org/10.1007/s00366-018-0644-0
Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2990-z
Asadi A, Huat BB, Moayedi H, Shariatmadari N, Parsaie A (2011) Electro-osmotic permeability coefficient of peat with different degree of humification. Int J Electrochem Sci 6:4481–4492
Asadi A, Moayedi H, Huat BB, Boroujeni FZ, Parsaie A, Sojoudi S (2011) Prediction of zeta potential for tropical peat in the presence of different cations using artificial neural networks. Int J Electrochem Sci 6:1146–1158
Asadi A, Moayedi H, Huat BBK, Parsaie A, Taha MR (2011) Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci 6:1135–1145
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Gao, W., Alqahtani, A.S., Mubarakali, A. et al. Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Engineering with Computers 36, 647–654 (2020). https://doi.org/10.1007/s00366-019-00720-5
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DOI: https://doi.org/10.1007/s00366-019-00720-5