{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T06:46:52Z","timestamp":1773557212490,"version":"3.50.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T00:00:00Z","timestamp":1557446400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T00:00:00Z","timestamp":1557446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00366-019-00770-9","type":"journal-article","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T19:41:49Z","timestamp":1557517309000},"page":"1355-1370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production"],"prefix":"10.1007","volume":"36","author":[{"given":"Edy Tonnizam","family":"Mohamad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4181-5463","authenticated-orcid":false,"given":"Diyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhatawdekar Ramesh","family":"Murlidhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danial","family":"Jahed Armaghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khairul Anuar","family":"Kassim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Komoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,10]]},"reference":[{"key":"770_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s10064-009-0235-9","volume":"69","author":"G Tsiambaos","year":"2010","unstructured":"Tsiambaos G, Saroglou H (2010) Excavatability assessment of rock masses using the geological strength index (GSI). Bull Eng Geol Environ 69:13\u201327","journal-title":"Bull Eng Geol Environ"},{"key":"770_CR2","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.measurement.2017.07.035","volume":"111","author":"ET Mohamad","year":"2017","unstructured":"Mohamad ET, Armaghani DJ, Mahdyar A et al (2017) Utilizing regression models to find functions for determining ripping production based on laboratory tests. Measurement 111:216\u2013225","journal-title":"Measurement"},{"key":"770_CR3","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/S0022-4898(98)00018-4","volume":"35","author":"J Hadjigeorgiou","year":"1998","unstructured":"Hadjigeorgiou J, Poulin R (1998) Assessment of ease of excavation of surface mines. J Terramech 35:137\u2013153","journal-title":"J Terramech"},{"key":"770_CR4","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1007\/s10706-017-0254-4","volume":"35","author":"ET Mohamad","year":"2017","unstructured":"Mohamad ET, Armaghani DJ, Ghoroqi M et al (2017) Ripping production prediction in different weathering zones according to field data. Geotech Geol Eng 35:2381\u20132399. \nhttps:\/\/doi.org\/10.1007\/s10706-017-0254-4","journal-title":"Geotech Geol Eng"},{"key":"770_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.enggeo.2004.04.004","volume":"74","author":"H Basarir","year":"2004","unstructured":"Basarir H, Karpuz C (2004) A rippability classification system for marls in lignite mines. Eng Geol 74:303\u2013318","journal-title":"Eng Geol"},{"key":"770_CR6","unstructured":"Thuro K, Plinninger RJ (2003) Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. In: Proceedings of the 10th international congress ISRM, South African Institute on Mineral Metallurgy, pp 1\u20137"},{"key":"770_CR7","first-page":"817","volume":"107","author":"L Basarir","year":"2007","unstructured":"Basarir L (2007) A fuzzy logic based rippability classification system. J South Afr Inst Min Metall 107:817\u2013831","journal-title":"J South Afr Inst Min Metall"},{"key":"770_CR8","unstructured":"Thuro K, Plinninger RJ, Spaun G (2002) Drilling, blasting and cutting\u2014is it possible to quantify geological parameters relating to excavatability? In: Engineering geology for developing countries\u2014proceedings of 9th Congress of the International Association for Engineering Geology and the Environment, Durban, South Africa, pp 16\u201320"},{"key":"770_CR9","unstructured":"Fowell RJ, Johnson ST (1991) Cuttability assessment applied to drag tool tunnelling machines. In: 7th ISRM Congress. International Society for Rock Mechanics"},{"key":"770_CR10","unstructured":"Singh RN, Denby B, Egretli I (1987) Development of a new rippability index for coal measures excavations. In: the 28th US symposium on rock mechanics (USRMS). American Rock Mechanics Association"},{"key":"770_CR11","unstructured":"Singh RN, Elmherig AM, Sunu MZ (1986) Application of rock mass characterization to the stability assessment and blast design in hard rock surface mining excavations. In: the 27th US symposium on rock mechanics (USRMS). American Rock Mechanics Association"},{"key":"770_CR12","unstructured":"Komoo I (1995) Geologi kejuruteraan perspektif rantau tropika lembab. Syarahan Perdana, Universiti Kebangs. Malaysia, Bangi, Selangor Malaysia, pp 1\u201362"},{"key":"770_CR13","unstructured":"Hudson JA (1999) Technical auditing of rock mechanics modeling and rock engineering design. In: 37th US symposium on rock mechanics, pp 183\u2013197"},{"key":"770_CR14","volume-title":"Caterpillar performance handbook","author":"TC Caterpillar","year":"2001","unstructured":"Caterpillar TC (2001) Caterpillar performance handbook. Caterpillar Inc, Preoria"},{"key":"770_CR15","unstructured":"Hardy MP, Goodrich RR, Brenner H (1992) Solution mining cavity stability: a site investigation and analytical assessment. In: rock characterization: ISRM Symposium, Eurock\u201992, Chester, UK, 14\u201317 Sept 1992. Thomas Telford Publishing, pp 293\u2013297"},{"key":"770_CR16","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.measurement.2015.03.009","volume":"68","author":"A Tripathy","year":"2015","unstructured":"Tripathy A, Singh TN, Kundu J (2015) Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement 68:302\u2013309","journal-title":"Measurement"},{"key":"770_CR17","unstructured":"Franklin JA, Broch E, Walton G (1971) Logging the mechanical character of rock. Transactions of the Institution of Mining and Metallurgy 80A, pp 1\u20139"},{"key":"770_CR18","first-page":"A101","volume":"80","author":"T Atkinson","year":"1971","unstructured":"Atkinson T (1971) Selection of open pit excavating and loading equipment. Trans Inst Min Met 80:A101\u2013A129","journal-title":"Trans Inst Min Met"},{"key":"770_CR19","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/S0167-9031(84)90349-9","volume":"1","author":"MJ Scoble","year":"1984","unstructured":"Scoble MJ, Muftuoglu YV (1984) Derivation of a diggability index for surface mine equipment selection. Min Sci Technol 1:305\u2013322","journal-title":"Min Sci Technol"},{"key":"770_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1144\/GSL.QJEGH.1994.027.P2.05","volume":"27","author":"GS Pettifer","year":"1994","unstructured":"Pettifer GS, Fookes PG (1994) A revision of the graphical method for assessing the excavatability of rock. Q J Eng Geol Hydrogeol 27:145\u2013164","journal-title":"Q J Eng Geol Hydrogeol"},{"key":"770_CR21","volume-title":"Geology for civil engineers","author":"C Gribble","year":"2003","unstructured":"Gribble C, McLean A (2003) Geology for civil engineers. CRC Press, Boca Raton"},{"key":"770_CR22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/0167-9031(90)90303-A","volume":"11","author":"C Karpuz","year":"1990","unstructured":"Karpuz C (1990) A classification system for excavation of surface coal measures. Min Sci Technol 11:157\u2013163","journal-title":"Min Sci Technol"},{"key":"770_CR23","first-page":"1024","volume-title":"Excavation handbook","author":"HK Church","year":"1981","unstructured":"Church HK (1981) Excavation handbook. McGraw-Hill, New York, NY, p 1024"},{"key":"770_CR24","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.measurement.2014.08.007","volume":"57","author":"E Momeni","year":"2014","unstructured":"Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Meas J Int Meas Confed 57:122\u2013131. \nhttps:\/\/doi.org\/10.1016\/j.measurement.2014.08.007","journal-title":"Meas J Int Meas Confed"},{"key":"770_CR25","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1007\/s13762-016-0979-2","volume":"13","author":"R Shirani Faradonbeh","year":"2016","unstructured":"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 13:1453\u20131464. \nhttps:\/\/doi.org\/10.1007\/s13762-016-0979-2","journal-title":"Int J Environ Sci Technol"},{"key":"770_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-018-3253-3","volume":"2018","author":"M Koopialipoor","year":"2018","unstructured":"Koopialipoor M, Armaghani DJ, Hedayat A et al (2018) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput 2018:1\u201317. \nhttps:\/\/doi.org\/10.1007\/s00500-018-3253-3","journal-title":"Soft Comput"},{"key":"770_CR27","first-page":"413","volume":"22","author":"ES Chahnasir","year":"2018","unstructured":"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\u2013424","journal-title":"SMART Struct Syst"},{"key":"770_CR28","doi-asserted-by":"publisher","first-page":"679","DOI":"10.12989\/scs.2016.21.3.679","volume":"21","author":"M Safa","year":"2016","unstructured":"Safa M, Shariati M, Ibrahim Z et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam\u2019s shear strength. Steel Compos Struct 21:679\u2013688","journal-title":"Steel Compos Struct"},{"key":"770_CR29","first-page":"243","volume":"29","author":"M Shariat","year":"2018","unstructured":"Shariat M, Shariati M, Madadi A, Wakil K (2018) Computational Lagrangian Multiplier Method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams. Steel Compos Struct 29:243\u2013256","journal-title":"Steel Compos Struct"},{"key":"770_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-019-00711-6","volume":"2019","author":"X Liao","year":"2019","unstructured":"Liao X, Khandelwal M, Yang H et al (2019) Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng Comput 2019:1\u201312. \nhttps:\/\/doi.org\/10.1007\/s00366-019-00711-6","journal-title":"Eng Comput"},{"key":"770_CR31","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s12665-019-8163-x","volume":"78","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor M, Ghaleini EN, Tootoonchi H et al (2019) Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ Earth Sci 78:165. \nhttps:\/\/doi.org\/10.1007\/s12665-019-8163-x","journal-title":"Environ Earth Sci"},{"key":"770_CR32","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1080\/0305215X.2018.1439943","volume":"50","author":"M Wang","year":"2018","unstructured":"Wang M, Shi X, Zhou J, Qiu X (2018) Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects. Eng Optim 50:2177\u20132191","journal-title":"Eng Optim"},{"key":"770_CR33","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1016\/S1003-6326(11)61195-3","volume":"22","author":"X Shi","year":"2012","unstructured":"Shi X, Jian Z, Wu B et al (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432\u2013441","journal-title":"Trans Nonferrous Met Soc China"},{"key":"770_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-019-00726-z","volume":"2019","author":"J Zhou","year":"2019","unstructured":"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 2019:1\u201311. \nhttps:\/\/doi.org\/10.1007\/s00366-019-00726-z","journal-title":"Eng Comput"},{"key":"770_CR35","doi-asserted-by":"publisher","first-page":"4016003","DOI":"10.1061\/(ASCE)CP.1943-5487.0000553","volume":"30","author":"J Zhou","year":"2016","unstructured":"Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003","journal-title":"J Comput Civ Eng"},{"key":"770_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2598-8","author":"DJ Armaghani","year":"2016","unstructured":"Armaghani DJ, Hasanipanah M, Mahdiyar A et al (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2598-8","journal-title":"Neural Comput Appl"},{"key":"770_CR37","unstructured":"Khorami M, Khorami M, Motahar H, et al (2017) Evaluation of the seismic performance of special moment frames using incremental nonlinear dynamic analysis"},{"issue":"6","key":"770_CR38","doi-asserted-by":"publisher","first-page":"853","DOI":"10.12989\/sem.2013.46.6.853","volume":"46","author":"M Mohammadhassani","year":"2013","unstructured":"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(6):853\u2013868","journal-title":"Struct Eng Mech"},{"key":"770_CR39","unstructured":"Toghroli A, Suhatril M, Ibrahim Z, et al (2016) Potential of soft computing approach for evaluating the factors affecting the capacity of steel\u2013concrete composite beam. J Intell Manuf 1\u20139"},{"issue":"5","key":"770_CR40","doi-asserted-by":"publisher","first-page":"457","DOI":"10.12989\/scs.2019.30.5.457","volume":"30","author":"C Chen","year":"2019","unstructured":"Chen C, Shi L, Shariati M et al (2019) Behavior of steel storage pallet racking connection-A review. Steel Compos Struct 30(5):457\u2013469. \nhttps:\/\/doi.org\/10.12989\/scs.2019.30.5.457","journal-title":"Steel Compos Struct"},{"issue":"5","key":"770_CR41","doi-asserted-by":"publisher","first-page":"785","DOI":"10.12989\/sss.2014.14.5.785","volume":"14","author":"M Mohammadhassani","year":"2014","unstructured":"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. Smart Struct Syst, Int J 14(5):785\u2013809","journal-title":"Smart Struct Syst, Int J"},{"key":"770_CR42","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.1177\/1077546314568172","volume":"22","author":"J Zhou","year":"2016","unstructured":"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\u20133997","journal-title":"J Vib Control"},{"key":"770_CR43","doi-asserted-by":"publisher","first-page":"4019024","DOI":"10.1061\/(ASCE)CF.1943-5509.0001292","volume":"33","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Wang M et al (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33:4019024","journal-title":"J Perform Constr Facil"},{"key":"770_CR44","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.3390\/app9061042","volume":"9","author":"H Chen","year":"2019","unstructured":"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","journal-title":"Appl Sci"},{"key":"770_CR45","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.3390\/app9081621","volume":"9","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Wei H et al (2019) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Appl Sci 9:1621","journal-title":"Appl Sci"},{"key":"770_CR46","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.tust.2016.12.009","volume":"63","author":"DJ Armaghani","year":"2017","unstructured":"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\u201343. \nhttps:\/\/doi.org\/10.1016\/j.tust.2016.12.009","journal-title":"Tunn Undergr Space Technol"},{"key":"770_CR47","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1007\/s10845-016-1217-y","volume":"29","author":"A Toghroli","year":"2018","unstructured":"Toghroli A, Suhatril M, Ibrahim Z et al (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel\u2013concrete composite beam. J Intell Manuf 29:1793\u20131801","journal-title":"J Intell Manuf"},{"key":"770_CR48","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/s12517-015-2057-3","volume":"9","author":"DJ Armaghani","year":"2016","unstructured":"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","journal-title":"Arab J Geosci"},{"key":"770_CR49","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1007\/s00521-016-2434-1","volume":"28","author":"M Hasanipanah","year":"2016","unstructured":"Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28:1043\u20131050. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2434-1","journal-title":"Neural Comput Appl"},{"key":"770_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-019-00701-8","volume":"2019","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor M, Fahimifar A, Ghaleini EN et al (2019) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng Comput 2019:1\u201313. \nhttps:\/\/doi.org\/10.1007\/s00366-019-00701-8","journal-title":"Eng Comput"},{"key":"770_CR51","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s00366-018-0625-3","volume":"35","author":"EN Ghaleini","year":"2018","unstructured":"Ghaleini EN, Koopialipoor M, Momenzadeh M et al (2018) A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls. Eng Comput 35:647. \nhttps:\/\/doi.org\/10.1007\/s00366-018-0625-3","journal-title":"Eng Comput"},{"key":"770_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-018-00700-1","volume":"2019","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor M, Murlidhar BR, Hedayat A et al (2019) The use of new intelligent techniques in designing retaining walls. Eng Comput 2019:1\u201312. \nhttps:\/\/doi.org\/10.1007\/s00366-018-00700-1","journal-title":"Eng Comput"},{"key":"770_CR53","doi-asserted-by":"crossref","unstructured":"Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Neural Networks, 1991, IJCNN-91-Seattle international joint conference on IEEE, pp 765\u2013770","DOI":"10.1109\/IJCNN.1991.155275"},{"key":"770_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-016-2728-3","volume":"1","author":"ET Mohamad","year":"2016","unstructured":"Mohamad ET, Armaghani DJ, Momeni E et al (2016) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 1:1. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2728-3","journal-title":"Neural Comput Appl"},{"key":"770_CR55","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.measurement.2014.09.075","volume":"60","author":"E Momeni","year":"2015","unstructured":"Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Meas J Int Meas Confed 60:50\u201363. \nhttps:\/\/doi.org\/10.1016\/j.measurement.2014.09.075","journal-title":"Meas J Int Meas Confed"},{"key":"770_CR56","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/72.97934","volume":"2","author":"DF Specht","year":"1991","unstructured":"Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568\u2013576","journal-title":"IEEE Trans Neural Netw"},{"key":"770_CR57","unstructured":"Simpson PK (1990) Artificial neural systems: foundations, paradigms, applications, and implementations. Pergamon"},{"key":"770_CR58","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.measurement.2014.06.001","volume":"55","author":"DJ Armaghani","year":"2014","unstructured":"Armaghani DJ, Hajihassani M, Bejarbaneh BY, Marto A, Mohamad ET (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487\u2013498. \nhttps:\/\/doi.org\/10.1016\/j.measurement.2014.06.001","journal-title":"Measurement"},{"key":"770_CR59","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3\u201331","journal-title":"J Microbiol Methods"},{"key":"770_CR60","volume-title":"Neural networks: methodology and applications","author":"G Dreyfus","year":"2005","unstructured":"Dreyfus G (2005) Neural networks: methodology and applications. Springer, Berlin, Heidelberg"},{"key":"770_CR61","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007. CEC 2007, IEEE Congress on IEEE, pp 4661\u20134667","DOI":"10.1109\/CEC.2007.4425083"},{"key":"770_CR62","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10661-015-4895-6","volume":"187","author":"DJ Armaghani","year":"2015","unstructured":"Armaghani DJ, Hajihassani M, Marto A et al (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187:11. \nhttps:\/\/doi.org\/10.1007\/s10661-015-4895-6","journal-title":"Environ Monit Assess"},{"key":"770_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/643715","volume":"1","author":"A Marto","year":"2014","unstructured":"Marto A, Hajihassani M, Jahed Armaghani D et al (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 1:1. \nhttps:\/\/doi.org\/10.1155\/2014\/643715","journal-title":"Sci World J"},{"key":"770_CR64","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s12665-017-6726-2","volume":"76","author":"M Khandelwal","year":"2017","unstructured":"Khandelwal M, Mahdiyar A, Armaghani DJ et al (2017) An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Environ Earth Sci 76:399. \nhttps:\/\/doi.org\/10.1007\/s12665-017-6726-2","journal-title":"Environ Earth Sci"},{"key":"770_CR65","unstructured":"Kennedy J, Eberhart RC (1995) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation, 1997 IEEE International Conference on IEEE, pp 4104\u20134108"},{"key":"770_CR66","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1007\/s12517-013-1174-0","volume":"7","author":"DJ Armaghani","year":"2014","unstructured":"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\u20135396","journal-title":"Arab J Geosci"},{"key":"770_CR67","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.apacoust.2014.01.005","volume":"80","author":"M Hajihassani","year":"2014","unstructured":"Hajihassani M, Jahed Armaghani D, Sohaei H et al (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57\u201367. \nhttps:\/\/doi.org\/10.1016\/j.apacoust.2014.01.005","journal-title":"Appl Acoust"},{"key":"770_CR68","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s00366-016-0447-0","volume":"32","author":"M Hasanipanah","year":"2016","unstructured":"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 32:705\u2013715. \nhttps:\/\/doi.org\/10.1007\/s00366-016-0447-0","journal-title":"Eng Comput"},{"key":"770_CR69","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-tr06, Erciyes university, engineering faculty, computer engineering department"},{"key":"770_CR70","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.petrol.2011.05.006","volume":"78","author":"R Irani","year":"2011","unstructured":"Irani R, Nasimi R (2011) Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling. J Pet Sci Eng 78:6\u201312","journal-title":"J Pet Sci Eng"},{"key":"770_CR71","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.petlm.2015.11.004","volume":"2","author":"B Nozohour-leilabady","year":"2016","unstructured":"Nozohour-leilabady B, Fazelabdolabadi B (2016) On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum 2:79\u201389","journal-title":"Petroleum"},{"key":"770_CR72","unstructured":"de Oliveira IMS, Schirru R, de Medeiros J (2009) On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a pwr nuclear power plant. In: 2009 international nuclear Atlantic conference (INAC 2009)"},{"key":"770_CR73","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2456-8","author":"SVANK Abad","year":"2016","unstructured":"Abad SVANK, Yilmaz M, Jahed Armaghani D, Tugrul A (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-016-2456-8","journal-title":"Neural Comput Appl"},{"key":"770_CR74","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-017-0356-z","author":"M Hajihassani","year":"2017","unstructured":"Hajihassani M, Jahed Armaghani D, Kalatehjari R (2017) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotech Geol Eng. \nhttps:\/\/doi.org\/10.1007\/s10706-017-0356-z","journal-title":"Geotech Geol Eng"},{"key":"770_CR75","first-page":"1","volume":"2017","author":"M Khandelwal","year":"2017","unstructured":"Khandelwal M, Marto A, Fatemi SA et al (2017) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 2017:1\u201311","journal-title":"Eng Comput"},{"key":"770_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10064-016-0983-2","volume":"1","author":"BY Bejarbaneh","year":"2016","unstructured":"Bejarbaneh BY, Bejarbaneh EY, Amin MFM et al (2016) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ 1:1. \nhttps:\/\/doi.org\/10.1007\/s10064-016-0983-2","journal-title":"Bull Eng Geol Environ"},{"key":"770_CR77","first-page":"742","volume":"15","author":"S-W Liou","year":"2009","unstructured":"Liou S-W, Wang C-M, Huang Y-F (2009) Integrative discovery of multifaceted sequence patterns by frame-relayed search and hybrid PSO-ANN. J UCS 15:742\u2013764","journal-title":"J UCS"},{"key":"770_CR78","unstructured":"Ulusay R, Hudson JA ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974\u20132006. In: Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey, p 628"},{"key":"770_CR79","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.ijrmms.2005.06.007","volume":"43","author":"H Sonmez","year":"2006","unstructured":"Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224\u2013235","journal-title":"Int J Rock Mech Min Sci"},{"key":"770_CR80","first-page":"53","volume":"3","author":"M Caudill","year":"1988","unstructured":"Caudill M (1988) Neural networks primer, part III. AI Expert 3:53\u201359","journal-title":"AI Expert"},{"key":"770_CR81","volume-title":"Applying neural networks: a practical guide","author":"K Swingler","year":"1996","unstructured":"Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York"},{"key":"770_CR82","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1016\/j.asoc.2012.10.009","volume":"13","author":"MA Ahmadi","year":"2013","unstructured":"Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085\u20131098","journal-title":"Appl Soft Comput"},{"key":"770_CR83","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s10064-014-0657-x","volume":"74","author":"M Hajihassani","year":"2014","unstructured":"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\u2013886. \nhttps:\/\/doi.org\/10.1007\/s10064-014-0657-x","journal-title":"Bull Eng Geol Environ"},{"key":"770_CR84","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1007\/s12665-015-4274-1","volume":"74","author":"M Hajihassani","year":"2015","unstructured":"Hajihassani M, Jahed Armaghani D, Monjezi M et al (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74:2799\u20132817. \nhttps:\/\/doi.org\/10.1007\/s12665-015-4274-1","journal-title":"Environ Earth Sci"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-019-00770-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00366-019-00770-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-019-00770-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T14:12:59Z","timestamp":1601302379000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00366-019-00770-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,10]]},"references-count":84,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["770"],"URL":"https:\/\/doi.org\/10.1007\/s00366-019-00770-9","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,10]]},"assertion":[{"value":"22 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}