PL EN
ESTIMATION OF AIR OVER-PRESSURE USING BAT ALGORITHM
 
Więcej
Ukryj
1
Hamedan University of Technology
 
2
Mining Engineering
 
 
Autor do korespondencji
Hesam Dehghani   

Hamedan University of Technology
 
 
Mining Science 2021;28:77-92
 
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
Air overpressure (AOp) is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to nearby structures and to cause injuries, to personnel or animals, AOp is one of the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for AOp estimation. The scope of this study is to predict AOp induced by blasting through a novel approach based on the bat algorithm. For this purpose, the parameters of 62 blasting operations were accurately recorded and AOp were measured for each operation. In the next stage, a new empirical predictor was developed to predict AOp. The results clearly showed the superiority of the proposed bat algorithm model in comparison with the empirical approaches.
REFERENCJE (31)
1.
ARMAGHANI D.J., MAHDIYAR A., HASANIPANAH M. 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, 1–11.
 
2.
BHANDARI S., 1997, Engineering rock blasting operations, A.A. Balkema, Netherlands.
 
3.
BUI X.N., NGUYEN H., LE H.A., BUI H.B., DO N.H., 2019, Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques, Natural Resources Research, https://doi.org/10.1007/s11053....
 
4.
CENGIZ K., 2008, The importance of site-specific characters in prediction models for blast-induced ground vibrations, Soil Dyn. Earthquake Eng., 28, 405–414.
 
5.
DEHGHANI H., ATAEE-POUR M., 2011, Development of a model to predict peak particle velocity in a blasting operation, International Journal of Rock Mechanics and Mining Sciences, 48(1), 51–58.
 
6.
DEHGHANI H., SHAFAGHI M., 2017, Prediction of blast-induced flyrock using differential evolution algorithm, Engineering with Computers, 33, 149–158.
 
7.
FENTON M., 2004, Bat natural history and echolocation, [in:] R. Brigham, K. Elisabeth, J.Gareth, P. Stuart, A. Herman (Eds.), Bat Echolocation Research Tools. Techniquesand Analysis, Bat Conservation International, pp. 2–6.
 
8.
GAO W., ALQAHTANI A.S., MUBARAKALI A., MAVALURU D., KHALAFI S., 2019, Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA, Engineering with Computers, https://doi.org/10.1007/s00366....
 
9.
HAJIHASSANI M., ARMAGHANI D.J., SOHAEI H., TONNIZAM MOHAMAD E., MARTO A., 2014, Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization, Applied Acoustics, 80, 57–67.
 
10.
JIMENO C.L., JIMENO E.L., CARCEDO F.J.A., 1995, Drilling and blasting of rocks, Balkema, Rotterdam.
 
11.
KONYA C.J., WALTER E.J., 2000, Surface blast design, Prentice Hall, p. 240–281.
 
12.
KUZU C., FISNE A., ERCELEBI S.G., 2009, Operational and geological parameters in the assessing blast induced air blast-overpressure in quarries, Applied Acoustics, 70, 404–411.
 
13.
LITTLE T.N., MURRAY C.E., 1996, The development and trialing a cement grout blasthole stemming, [in:] Proc. of the Int. Symp. on Rock Fragmentation by Blasting Fragblast, p. 331–41.
 
14.
LOOSE T., SAAL H., FREUND H.U., 2003, Sprengknallreduktion durch verdaemmte Materialien, [in:] Proc. of the Second World Conference on Explosive and Blasting Technique, p. 145–152.
 
15.
MAHDIYAR A., MARTO A., MIRHOSSEINEI S.A., 2018, Probabilistic air-overpressure simulation resulting from blasting operations, Environmental Earth Sciences, https://doi.org/10.1007/s12665....
 
16.
MCKENZINE C., 1990, Quarry blast monitoring: technical and environmental perspective, Quarry Manage., 17, 23–29.
 
17.
RAINA A.K., HALDAR A., CHAKRABORTY P.B., CHOUDHURY M., RAMULU M., 2004, Human response to blast induced vibration and air overpressure: an Indian scenario, Bull. Eng. Geol. Environ., 63, 209–214.
 
18.
RODRIGUEZ R., LOMBARDIA C., TORNO S., 2010, Prediction of the air wave due to blasting inside tunnels: approximation to a phonometric curve. Tunn. Undergr. Sp. Technol., 25, 483–489.
 
19.
RODRIGUEZ R., TORANO 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.
 
20.
ROSENTHAL M.F., MORLOCK G.L., 1987, Blasting guidance manual, office of surface mining reclamation and enforcement, US Department of the Interior.
 
21.
ROY P.P., 2005, Rock blasting effects and operations, Balkema, p. 223–240.
 
22.
SAGHATFOROUSH A., MONJEZI M., SHIRANI R., ARMAGHANI D.J., 2016, Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and backbreak induced by blasting, Eng. Comput., 32(2), 255–266.
 
23.
SEGARRA P., DOMINGO J.F., LOPEZ L.M., SANCHIDRIAN J.A., ORTEGA M.F., 2010, Prediction of near field overpressure from quarry blasting, Appl. Acoust., 1169–1176.
 
24.
SISKIND D.E., STACHURA V.J., STAGG M.S., KOOP J.W., 1980, [in:] D.E., Siskind (Ed.), Structure response and damage produced by airblast from surface mining, United States Bureau of Mines.
 
25.
WHARTON R.K., FORMBY S.A., MERRIFIELD R., 2000, Airblast TNT equivalence for a range of commercial blasting explosives, J. Hazard. Mater., 79, 31–39.
 
26.
WHITE T.J., FARNFIELD R.A., 1993, Computers and blasting, Trans. Inst. Min. Metall. Sec., 102.
 
27.
WISS J.F., LINEHAN P.W., 1978, Control of vibration and blast noise from surface coal mining, Contract 1025022, Research report for the U.S. Bureau of Mines.
 
28.
WU C., HAO H., 2005, Modelling of simultaneous ground shock and air blast pressure on nearby structures from surface explosions, Int. J. Impact Eng., 31, 699–717.
 
29.
YANG X.S., 2010, A new metaheuristic bat-inspired algorithm, [in:] J. Gonzlez, D. Pelta, C. Cruz, G. Terrazas, N. Krasnogor (Eds.), Nature Inspired Cooperative Strategiesfor Optimization of Studies in Computational Intelligence (NICSO 2010), Vol. 284, Springer, Berlin, Heidelberg, pp. 65–74.
 
30.
YILMAZ A.S., ÜKSILLE E.U.K., 2015, A new modification approach on bat algorithm for solving optimization problems. Applied Soft Computing, 28, 259–275.
 
31.
ZHOU J., NEKOUIE A., ARSLAN C.A., PHAM B.T., HASANIPANAH M., 2019, Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm, Engineering with Computers, https://doi.org/10.1007/s00366....
 
eISSN:2353-5423
ISSN:2300-9586
Journals System - logo
Scroll to top