A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine

Engineering with Computers - Tập 37 Số 4 - Trang 3221-3235 - 2021
Danial Jahed Armaghani1, Deepak Kumar2, Pijush Samui2, Mahdi Hasanipanah3, Bishwajit Roy4
1Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, India
3Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
4Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Raina AK, Murthy V, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ 73:1199–1209

Armaghani DJ, Hajihassani M, Mohamad ET et al (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396

Shirani Faradonbeh R, Monjezi M, Jahed Armaghani D (2016) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput. https://doi.org/10.1007/s00366-015-0404-3

Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2013.11.001

Trivedi R, Singh TN, Gupta N (2015) Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech Geol Eng 33:875–891

Han H, Armaghani DJ, Tarinejad R et al (2020) Random forest and bayesian network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites. Nat Resour Res. https://doi.org/10.1007/s11053-019-09611-4

Zhou J, Koopialipoor M, Murlidhar BR et al (2019) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Nat Resour Res. https://doi.org/10.1007/s11053-019-09519-z

Kumar R, Choudhury D, Bhargava K (2016) Determination of blast-induced ground vibration equations for rocks using mechanical and geological properties. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2015.10.009

Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2015.09.020

Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27:225–233

Armaghani DJ, 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. https://doi.org/10.1007/s12665-015-4305-y

Standard I (1973) Criteria for safety and design of structures subjected to under ground blast. ISI, IS-6922

Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56:97–107

Khandelwal M, Singh TN (2013) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222. https://doi.org/10.1016/j.ijrmms.2009.03.004

Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibrations. US Department of the Interior, Bureau of Mines

Roy P (1993) Putting ground vibration predictions into practice. Colliery Guard 241:63–67

Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. The Engineer, vol 217. London, pp 553–559

Langefors U, Kihlström B (1963) The modern technique of rock blasting. Wiley, New York

Hasanipanah M, Monjezi M, Shahnazar A et al (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2015.07.019

Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50. https://doi.org/10.1016/j.tust.2010.05.002

Hasanipanah M, Monjezi M, Shahnazar A et al (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

Khandelwal M, Singh TN (2009) Correlating static properties of coal measures rocks with P-wave velocity. Int J Coal Geol 79:55–60

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 22:1637–1643

Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M (2014) An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Int J Smart Struct Syst 14:785–809

Chahnasir ES, Zandi Y, Shariati M et al (2018) Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. SMART Struct Syst 22:413–424

Asteris PG, Armaghani DJ, Hatzigeorgiou GD et al (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24:469–488

Armaghani DJ, Hatzigeorgiou GD, Karamani C et al (2019) Soft computing-based techniques for concrete beams shear strength. Procedia Struct Integr 17:924–933

Hajihassani M, Abdullah SS, Asteris PG, Armaghani DJ (2019) A gene expression programming model for predicting tunnel convergence. Appl Sci 9:4650

Chen H, Asteris PG, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042

Xu H, Zhou J, Asteris GP et al (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9:3715

Huang L, Asteris PG, Koopialipoor M et al (2019) Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Appl Sci 9:5372

Sarir P, Chen J, Asteris PG et al (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https://doi.org/10.1007/s00366-019-00808-y

Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl. https://doi.org/10.1007/s00521-018-03965-1

Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424

Monjezi M, Khoshalan H, Razifard M (2012) A neuro-genetic network for predicting uniaxial compressive strength of rocks. Geotech Geol Eng 30:1053–1062

Mojtahedi SFF, Ebtehaj I, Hasanipanah M et al (2018) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 35(1):47–56

Koopialipoor M, Tootoonchi H, Jahed Armaghani D et al (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-019-01538-7

Zhou J, Aghili N, Ghaleini EN et al (2019) A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng Comput. https://doi.org/10.1007/s00366-019-00726-z

Xu C, Gordan B, Koopialipoor M et al (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700

Harandizadeh H, Armaghani DJ, Khari M (2019) A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Eng Comput. https://doi.org/10.1007/s00366-019-00849-3

Harandizadeh H, Toufigh MM, Toufigh V (2018) Application of improved ANFIS approaches to estimate bearing capacity of piles. Soft Comput. https://doi.org/10.1007/s00500-018-3517-y

Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22:3986–3997

Zhou J, Li E, Yang S et al (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

Wang M, Shi X, Zhou J (2018) Charge design scheme optimization for ring blasting based on the developed Scaled Heelan model. Int J Rock Mech Min Sci 110:199–209

Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79:291–316

Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45

Zhou J, Bejarbaneh BY, Armaghani DJ, Tahir MM (2019) Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-019-01626-8

Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04663-2

Asteris PG, Ashrafian A, Rezaie-Balf M (2019) Prediction of the compressive strength of self-compacting concrete using surrogate models. Comput Concr 24:137–150

Apostolopoulou M, Armaghani DJ, Bakolas A et al (2019) Compressive strength of natural hydraulic lime mortars using soft computing techniques. Procedia Struct Integr 17:914–923

Asteris PG, Moropoulou A, Skentou AD et al (2019) Stochastic vulnerability assessment of masonry structures: concepts, modeling and restoration aspects. Appl Sci 9:243

Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concr 24:329–345

Cavaleri L, Chatzarakis GE, Di TrapaniF et al (2017) Modeling of surface roughness in electro-discharge machining using artificial neural networks. Adv Mater Res 6:169–184

Cavaleri L, Asteris PG, Psyllaki PP et al (2019) Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. Appl Sci 9:2788

Psyllaki P, Stamatiou K, Iliadis I et al (2018) Surface treatment of tool steels against galling failure. In: MATEC web of conferences. EDP sciences, p 4024

Alavi Nezhad Khalil Abad SV, Yilmaz M, Jahed Armaghani D, Tugrul A (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2456-8

Momeni E, Armaghani DJ, Fatemi SA, Nazir R (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 34:319–327

Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43. https://doi.org/10.1016/j.tust.2016.12.009

Armaghani DJ, Mohamad ET, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48

Mohammadhassani M, Nezamabadi-Pour H, Suhatril M, Shariati M (2013) Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct Eng Mech 46:853–868

Mansouri I, Shariati M, Safa M et al (2019) 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 30:1247–1257

Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886. https://doi.org/10.1007/s10064-014-0657-x

Shahnazar A, Nikafshan Rad H, Hasanipanah M et al (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci. https://doi.org/10.1007/s12665-017-6864-6

Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (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

Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid MZ et al (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-016-0979-2

Sheykhi H, Bagherpour R, Ghasemi E, Kalhori H (2018) Forecasting ground vibration due to rock blasting: a hybrid intelligent approach using support vector regression and fuzzy C-means clustering. Eng Comput 34:357–365

Mirjalili S, Lewis A, Sadiq AS (2014) Autonomous particles groups for particle swarm optimization. Arab J Sci Eng 39:4683–4697

Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

Kennedy J, Eberhart RC (1995) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics, 1997. Computational cybernetics and simulation. IEEE, pp 4104–4108

Hajihassani M, Armaghani D, Sohaei H, Mohamad E (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. https://doi.org/10.1007/s00366-015-0400-7

Pal M, Deswal S (2014) Extreme learning machine based modeling of resilient modulus of subgrade soils. Geotech Geol Eng 32:287–296

Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B 42:513–529

Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

Huang G-B, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892

Cui D, Huang G-B, Liu T (2018) ELM based smile detection using distance vector. Pattern Recognit 79:356–369

Zhu H, Tsang ECC, Zhu J (2018) Training an extreme learning machine by localized generalization error model. Soft Comput 22:3477–3485

Mohapatra P, Chakravarty S, Dash PK (2015) An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 24:25–49

Satapathy P, Dhar S, Dash PK (2017) An evolutionary online sequential extreme learning machine for maximum power point tracking and control in multi-photovoltaic microgrid system. Renew Energy Focus 21:33–53

Li L-L, Sun J, Tseng M-L, Li Z-G (2019) Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation. Expert Syst Appl 127:58–67

Cao J, Lin Z, Huang G-B (2012) Self-adaptive evolutionary extreme learning machine. Neural Process Lett 36:285–305

Chen S, Shang Y, Wu M (2016) Application of PSO-ELM in electronic system fault diagnosis. In: 2016 IEEE international conference on prognostics and health management (ICPHM). IEEE, pp 1–5

Marshall AW, Olkin I (1960) Multivariate chebyshev inequalities. Ann Math Stat. https://doi.org/10.1214/aoms/1177705673

Bertsimas D, Popescu I (2005) Optimal inequalities in probability theory: a convex optimization approach. SIAM J Optim. https://doi.org/10.1137/S1052623401399903

Lanckriet G, Ghaoui L, Bhattacharyya C (2002) Minimax probability machine. In: Advances in neural information processing systems, papers.nips.cc

Strohmann T, Grudic G (2003) A formulation for minimax probability machine regression. In: Advances in neural information processing systems, papers.nips.cc

Vapnik V (1995) The nature of statistical learning theory. Springer, New York

Zhang L, Rao K, Wang R (2015) T-QoS-aware based parallel ant colony algorithm for services composition. J Syst Eng Electron 26:1100–1106

Zhu C, Huo Y, Leung VCM, Yang LT (2016) Sensor-cloud and power line communication: recent developments and integration. In: Proceedings—2016 IEEE 14th international conference on dependable, autonomic and secure computing (DASC 2016), 2016 IEEE 14th international conference on pervasive intelligence and computing (PICom 2016), 2016 IEEE 2nd international conference on big data

Rasmussen CE (2004) Gaussian processes in machine learning. Springer, Berlin, pp 63–71

Matérn B (1960) Spatial variation, volume 36 of lecture notes in statistics, 2nd edn. Springer, New York

Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015:Article ID 931256. http://dx.doi.org/10.1155/2015

Cai X, Cui Y, Tan Y (2009) Predicted modified PSO with time-varying accelerator coefficients. Cognition 1:3

Cai X, Cui Z, Zeng J, Tan Y (2008) Dispersed particle swarm optimization. Inf Process Lett 105:231–235

Bao GQ, Mao KF (2009) Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 2134–2139

Yu Z, Shi X, Zhou J et al (2019) Prediction of blast-induced rock movement during bench blasting: use of gray wolf optimizer and support vector regression. Nat Resour Res. https://doi.org/10.1007/s11053-019-09593-3

Khari M, Armaghani DJ, Dehghanbanadaki A (2019) Prediction of lateral deflection of small-scale piles using hybrid PSO–ANN model. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04134-9

Asteris PG, Argyropoulos I, Cavaleri L et al (2018) Masonry compressive strength prediction using artificial neural networks. In: International conference on transdisciplinary multispectral modeling and cooperation for the preservation of cultural heritage. Springer, Berlin, pp 200–224

Asteris P, Roussis P, Douvika M (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17:1344

Asteris PG, Tsaris AK, Cavaleri L et al (2016) Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Comput Intell Neurosci 2016:20

Apostolopoulour M, Douvika MG, Kanellopoulos IN et al (2018) Prediction of compressive strength of mortars using artificial neural networks. In: Proceedings of the 1st international conference TMM_CH, transdisciplinary multispectral modelling and cooperation for the preservation of cultural heritage, Athens, Greece, pp 10–13

Zhou J, Guo H, Koopialipoor M et al (2020) Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Eng Comput. https://doi.org/10.1007/s00366-019-00908-9

Mahdiyar A, Jahed Armaghani D, Koopialipoor M et al (2020) Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and Monte Carlo simulation techniques. Appl Sci 10:472

Yong W, Zhou J, Armaghani DJ et al (2020) A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles. Eng Comput. https://doi.org/10.1007/s00366-019-00932-9

Duvall W, Petkof B (1958) Spherical propagation of explosion-generated strain pulses in rock. Bur Mines

Edwards A, Northwood T (1960) Experimental studies of the effects of blasting on structures. Division of Building Research, National Research Council

Lemon J, Bolker B, Oom S, Klein E, Rowlingson B, Wickham H, Tyagi A, Eterradossi O, Grothendieck GTM (2009) Plotrix: various plotting functions. R package version 2.7-2. R Project for Statistical Computing, Vienna

Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719

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 19:755–770