Predicting and controlling the ground vibration using gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms

Springer Science and Business Media LLC - Tập 80 - Trang 1-15 - 2021
Hesam Dehghani1, Behshad Jodeiri Shokri1, Hoshiar Mohammadzadeh1, Reza Shamsi1, Nasrin Abbas Salimi1
1Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

Tóm tắt

Ground vibration is one of the most significant issues resulting from the blasting operation. This paper presents two empirical relationships based on gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms for predicting blast-induced peak particle velocity (PPV) at Galali Iron Mine, western Iran. For this purpose, data on 13 parameters were collected from 34 blasting blocks in the studied mine before having the data processed using statistical methods. Eventually, four parameters, including burden, mean hole depth, charge per delay ratio, and distance to monitoring station, were identified as the most effective factors. PPV was also considered as the output parameter of the model. Then, exploring the best curve-fitting relationships between input and output data, an empirical relationship was developed by applying the GEP algorithm. Afterward, the TLBO algorithm was utilized to optimize the developed relationship. A comparative analysis based on statistical parameters such as correlation coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) indicated the superior accuracy of TLBO algorithm compared to the GEP method. Finally, a blasting pattern was formulated to attenuate the PPV at the center of the Galali Village from 10 mm/s to 1 mm/s while increasing the mine production from 5500 tons to 17,500 tons per blasting block.

Tài liệu tham khảo

Armaghani DJ, Kumar D, Samui P, Hasanipanah M, Roy B (2020) A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine. Eng Comput. https://doi.org/10.1007/s00366-020-00997-x

Bureau of Indian Standards (1973) Criteria for safety and design of structures subjected to underground blast. India. ISI Bull IS-6922

Davies B, Farmer IW, Attewell PB (1964) Ground vibration from shallow sub-surface blasts. Engineer 217(5644):553–559

Duvall WI, Petkof B (1959) Spherical propagation of explosion-generated strain pulses in rock (No. 5481–5485). US Department of the Interior, Bureau of Mines

Ferreira C (2001) Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 13:87–129

Ghosh A, Daemen JJ (1983) A simple new blast vibration predictor (based on wave propagation laws). The 24th US symposium on rock mechanics (USRMS). American Rock Mechanics Association

Jodeiri Shokri B, Dehghani H, Shamsi R, Doulati Ardejani F (2020) Prediction of acid mine drainage generation potential of a copper mine tailings using gene expression programming-a case study. J Min Environ 11(4):1127–1140. https://doi.org/10.22044/jme.2020.10031.1938

Rostami Paydar G, Lotfi M, Ghaderi M, Amiri A, Vossoughi-Abedini M (2010) New results on mineralography and crystal chemistry of magnetite and pyrite at Baba-Ali and Galali iron deposits, West of Hamedan. Iran Sci Q J Geosci 20(77):121–130

Zhongya Z, Xiaoguang J (2018) Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. Math Prob Eng 20018:12. Article ID 8473547. https://doi.org/10.1155/2018/8473547