{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:01:28Z","timestamp":1774879288910,"version":"3.50.1"},"reference-count":96,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05197-8","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T14:03:07Z","timestamp":1595944987000},"page":"3503-3519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model"],"prefix":"10.1007","volume":"33","author":[{"given":"Qiancheng","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-8314","authenticated-orcid":false,"given":"Hoang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan-Nam","family":"Bui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trung","family":"Nguyen-Thoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"5197_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09461-0","author":"X-N Bui","year":"2019","unstructured":"Bui X-N, Nguyen H, Le HA, Bui HB, Do NH (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-019-09461-0","journal-title":"Nat Resour Res"},{"issue":"2","key":"5197_CR2","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1007\/s11053-019-09603-4","volume":"29","author":"S Zhang","year":"2020","unstructured":"Zhang S, Bui X-N, Trung N-T, Nguyen H, Bui H-B (2020) Prediction of rock size distribution in mine bench blasting using a novel ant colony optimization-based boosted regression tree technique. Nat Resour Res 29(2):867\u2013886. https:\/\/doi.org\/10.1007\/s11053-019-09603-4","journal-title":"Nat Resour Res"},{"issue":"4","key":"5197_CR3","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s42452-019-0295-9","volume":"1","author":"H Nguyen","year":"2019","unstructured":"Nguyen H (2019) Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam. SN Appl Sci 1(4):283. https:\/\/doi.org\/10.1007\/s42452-019-0295-9","journal-title":"SN Appl Sci"},{"key":"5197_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3717-5","author":"H Nguyen","year":"2018","unstructured":"Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-3717-5","journal-title":"Neural Comput Appl"},{"issue":"2","key":"5197_CR5","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s11600-019-00268-4","volume":"67","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Bui X-N, Bui H-B, Cuong DT (2019) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67(2):477\u2013490. https:\/\/doi.org\/10.1007\/s11600-019-00268-4","journal-title":"Acta Geophys"},{"key":"5197_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-018-9424-1","author":"H Nguyen","year":"2018","unstructured":"Nguyen H, Bui X-N (2018) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-018-9424-1","journal-title":"Nat Resour Res"},{"key":"5197_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-019-00304-3","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Bui X-N, Moayedi H (2019) A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophys. https:\/\/doi.org\/10.1007\/s11600-019-00304-3","journal-title":"Acta Geophys"},{"issue":"12","key":"5197_CR8","doi-asserted-by":"crossref","first-page":"5383","DOI":"10.1007\/s12517-013-1174-0","volume":"7","author":"DJ Armaghani","year":"2014","unstructured":"Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani S (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383\u20135396","journal-title":"Arab J Geosci"},{"issue":"7","key":"5197_CR9","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1016\/j.apm.2014.10.006","volume":"39","author":"W-X Li","year":"2015","unstructured":"Li W-X, Qi D-L, Zheng S-F, Ren J-C, Li J-f, Yin X (2015) Fuzzy mathematics model and its numerical method of stability analysis on rock slope of opencast metal mine. Appl Math Model 39(7):1784\u20131793","journal-title":"Appl Math Model"},{"issue":"2","key":"5197_CR10","first-page":"41","volume":"61","author":"DV Binh","year":"2020","unstructured":"Binh DV, Ha TTK, Hai DT, Cuong DC, Anh DL, Nam H (2020) Calculation of the exploited flow water in the T2a\u0111g sediments at the well Kien Khe. Hanam. J Min Earth Sci 61(2):41\u201349","journal-title":"J Min Earth Sci"},{"issue":"2","key":"5197_CR11","first-page":"126","volume":"61","author":"NV Nguyen","year":"2020","unstructured":"Nguyen NV, Trinh HL (2020) Determination of water quality parameters in the Tan Rai exploiting area (Lam Dong province) using Sentinel-2 msi and Landsat 8 data. J Min Earth Sci 61(2):126\u2013134","journal-title":"J Min Earth Sci"},{"issue":"11","key":"5197_CR12","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1007\/s10661-015-4895-6","volume":"187","author":"DJ Armaghani","year":"2015","unstructured":"Armaghani DJ, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187(11):666","journal-title":"Environ Monit Assess"},{"key":"5197_CR13","doi-asserted-by":"publisher","first-page":"106292","DOI":"10.1016\/j.asoc.2020.106292","volume":"92","author":"H Nguyen","year":"2020","unstructured":"Nguyen H, Bui X-N (2020) Soft computing models for predicting blast-induced air over-pressure: a novel artificial intelligence approach. Appl Soft Comput 92:106292. https:\/\/doi.org\/10.1016\/j.asoc.2020.106292","journal-title":"Appl Soft Comput"},{"key":"5197_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-020-09710-7","author":"H Nguyen","year":"2020","unstructured":"Nguyen H, Bui X, Choi Y et al (2020) A novel combination of whale optimization algorithm and support vector machine with different kernel functions for prediction of blasting-induced fly-rock in quarry mines. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-020-09710-7","journal-title":"Nat Resour Res"},{"key":"5197_CR15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ijrmms.2012.06.005","volume":"55","author":"S Mahdevari","year":"2012","unstructured":"Mahdevari S, Torabi SR, Monjezi M (2012) Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon. Int J Rock Mech Min Sci 55:33\u201344","journal-title":"Int J Rock Mech Min Sci"},{"key":"5197_CR16","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ijrmms.2019.03.024","volume":"118","author":"M De Simone","year":"2019","unstructured":"De Simone M, Souza LM, Roehl D (2019) Estimating DEM microparameters for uniaxial compression simulation with genetic programming. Int J Rock Mech Min Sci 118:33\u201341","journal-title":"Int J Rock Mech Min Sci"},{"issue":"3","key":"5197_CR17","doi-asserted-by":"publisher","first-page":"19","DOI":"10.46326\/JMES.2020.61(3).03","volume":"61","author":"HV Nhu","year":"2020","unstructured":"Nhu HV, Duong BV, Vo TA, Pham KT (2020) Using numerical modeling method for design and constructive controlling of excavation wall in Madison Building, Ho Chi Minh city. J Min Earth Sci 61(3):19\u201327. https:\/\/doi.org\/10.46326\/JMES.2020.61(3).03","journal-title":"J Min Earth Sci"},{"issue":"7","key":"5197_CR18","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.ijrmms.2009.03.004","volume":"46","author":"M Khandelwal","year":"2009","unstructured":"Khandelwal M, Singh T (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214\u20131222","journal-title":"Int J Rock Mech Min Sci"},{"issue":"5","key":"5197_CR19","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.ijrmms.2011.04.016","volume":"48","author":"MT Mohamed","year":"2011","unstructured":"Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48(5):845","journal-title":"Int J Rock Mech Min Sci"},{"key":"5197_CR20","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ijrmms.2014.01.015","volume":"67","author":"M Yurdakul","year":"2014","unstructured":"Yurdakul M, Gopalakrishnan K, Akdas H (2014) Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. Int J Rock Mech Min Sci 67:127\u2013135","journal-title":"Int J Rock Mech Min Sci"},{"key":"5197_CR21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ijrmms.2017.02.012","volume":"94","author":"J Liu","year":"2017","unstructured":"Liu J, Zhao X-D, Xu Z-h (2017) Identification of rock discontinuity sets based on a modified affinity propagation algorithm. Int J Rock Mech Min Sci 94:32\u201342","journal-title":"Int J Rock Mech Min Sci"},{"key":"5197_CR22","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.asoc.2019.01.042","volume":"77","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376\u2013386. https:\/\/doi.org\/10.1016\/j.asoc.2019.01.042","journal-title":"Appl Soft Comput"},{"issue":"8","key":"5197_CR23","doi-asserted-by":"crossref","first-page":"4465","DOI":"10.1016\/j.asoc.2011.08.032","volume":"11","author":"A Daftaribesheli","year":"2011","unstructured":"Daftaribesheli A, Ataei M, Sereshki F (2011) Assessment of rock slope stability using the Fuzzy Slope Mass Rating (FSMR) system. Appl Soft Comput 11(8):4465\u20134473","journal-title":"Appl Soft Comput"},{"issue":"2","key":"5197_CR24","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1016\/j.asoc.2010.06.009","volume":"11","author":"M Asadi","year":"2011","unstructured":"Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11(2):1932\u20131937","journal-title":"Appl Soft Comput"},{"key":"5197_CR25","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.asoc.2017.07.011","volume":"60","author":"F Kang","year":"2017","unstructured":"Kang F, Xu B, Li J, Zhao S (2017) Slope stability evaluation using Gaussian processes with various covariance functions. Appl Soft Comput 60:387\u2013396","journal-title":"Appl Soft Comput"},{"key":"5197_CR26","doi-asserted-by":"crossref","first-page":"101604","DOI":"10.1016\/j.resourpol.2020.101604","volume":"66","author":"H Zhang","year":"2020","unstructured":"Zhang H, Nguyen H, Bui X-N, Nguyen-Thoi T, Bui T-T, Nguyen N, Vu D-A, Mahesh V, Moayedi H (2020) Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm. Resour Policy 66:101604","journal-title":"Resour Policy"},{"issue":"3","key":"5197_CR27","first-page":"12","volume":"61","author":"HV Nguyen","year":"2020","unstructured":"Nguyen HV, Bui ST, Phung HH, Pham HNT (2020) The initial research on the compressive strength of mortar when using bottom ash from thermal power plants to replace natural sand in construction. J Min Earth Sci 61(3):12\u201318","journal-title":"J Min Earth Sci"},{"issue":"3","key":"5197_CR28","first-page":"51","volume":"61","author":"TM Tran","year":"2020","unstructured":"Tran TM, Do TN, Dinh HTT, Vu HX, Ferrier E (2020) A 2-D numerical model of the mechanical behavior of the textile-reinforced concrete composite material: effect of textile reinforcement ratio. J Min Earth Sci 61(3):51\u201359","journal-title":"J Min Earth Sci"},{"issue":"2","key":"5197_CR29","first-page":"86","volume":"61","author":"LQ Nguyen","year":"2020","unstructured":"Nguyen LQ (2020) A novel approach of determining the parameters of Asadi profiling function for predicting ground subsidence due to inclined coal seam mining at Quang Ninh coal basin. J Min Earth Sci 61(2):86\u201395","journal-title":"J Min Earth Sci"},{"issue":"2","key":"5197_CR30","first-page":"106","volume":"61","author":"NTT Nguyen","year":"2020","unstructured":"Nguyen NTT, Tong HT (2020) Predicting land use change base on GIS and remote sensing. J Min Earth Sci 61(2):106\u2013115","journal-title":"J Min Earth Sci"},{"issue":"2","key":"5197_CR31","first-page":"134","volume":"61","author":"LT Pham","year":"2020","unstructured":"Pham LT, Nguyen SP, Nguyen NV, Dao HV, Doan LD, Vo NHT, Nguyen TTT, Tran HV (2020) Establishment of land cover map using object-oriented classification method for VNREDSat-1 data. J Min Earth Sci 61(2):134\u2013144","journal-title":"J Min Earth Sci"},{"issue":"1","key":"5197_CR32","first-page":"1","volume":"61","author":"NV Nghia","year":"2020","unstructured":"Nghia NV (2020) Building DEM for deep open-pit coal mines using DJI Inspire 2. J Min Earth Sci 61(1):1\u201310","journal-title":"J Min Earth Sci"},{"issue":"1","key":"5197_CR33","first-page":"21","volume":"61","author":"LV Canh","year":"2020","unstructured":"Canh LV, Cuong CX, Tien D (2020) Volume computation of quarries in Vietnam based on Unmanned Aerial Vehicle (UAV) data. J Min Earth Sci 61(1):21\u201330","journal-title":"J Min Earth Sci"},{"issue":"1","key":"5197_CR34","first-page":"41","volume":"61","author":"VT Sinh","year":"2020","unstructured":"Sinh VT, Quyen VT, Huan LN, Huong TT (2020) Design information orientation supporting system for user. J Min Earth Sci 61(1):41\u201351","journal-title":"J Min Earth Sci"},{"issue":"1","key":"5197_CR35","first-page":"132","volume":"61","author":"DV Nam","year":"2020","unstructured":"Nam DV, Oanh NT, Hoai NX, Manh NV, Hien NT (2020) Detect and process outliers for temperature data at 3h monitoring stations in Vietnam. J Min Earth Sci 61(1):132\u2013146","journal-title":"J Min Earth Sci"},{"issue":"3","key":"5197_CR36","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s00366-015-0425-y","volume":"32","author":"M Hasanipanah","year":"2016","unstructured":"Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441\u2013455","journal-title":"Eng Comput"},{"issue":"1","key":"5197_CR37","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s00521-016-2434-1","volume":"28","author":"M Hasanipanah","year":"2017","unstructured":"Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043\u20131050","journal-title":"Neural Comput Appl"},{"issue":"9","key":"5197_CR38","doi-asserted-by":"crossref","first-page":"6881","DOI":"10.1007\/s12517-014-1677-3","volume":"8","author":"M Esmaeili","year":"2015","unstructured":"Esmaeili M, Salimi A, Drebenstedt C, Abbaszadeh M, Bazzazi AA (2015) Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci 8(9):6881\u20136893","journal-title":"Arab J Geosci"},{"issue":"1","key":"5197_CR39","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s10064-015-0720-2","volume":"75","author":"E Ebrahimi","year":"2016","unstructured":"Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27\u201336","journal-title":"Bull Eng Geol Environ"},{"issue":"4","key":"5197_CR40","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s00521-016-2746-1","volume":"30","author":"M Hasanipanah","year":"2018","unstructured":"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\u20131024","journal-title":"Neural Comput Appl"},{"issue":"2","key":"5197_CR41","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s00366-017-0544-8","volume":"34","author":"W Gao","year":"2018","unstructured":"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\u2013345. https:\/\/doi.org\/10.1007\/s00366-017-0544-8","journal-title":"Eng Comput"},{"issue":"2","key":"5197_CR42","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s00366-017-0535-9","volume":"34","author":"PF Asl","year":"2018","unstructured":"Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241\u2013251","journal-title":"Eng Comput"},{"issue":"1","key":"5197_CR43","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00366-018-0582-x","volume":"35","author":"SFF Mojtahedi","year":"2019","unstructured":"Mojtahedi SFF, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2019) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 35(1):47\u201356","journal-title":"Eng Comput"},{"issue":"12","key":"5197_CR44","doi-asserted-by":"crossref","first-page":"10819","DOI":"10.1007\/s12517-015-1952-y","volume":"8","author":"S Shams","year":"2015","unstructured":"Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8(12):10819\u201310832","journal-title":"Arab J Geosci"},{"issue":"9","key":"5197_CR45","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1007\/s12665-016-5503-y","volume":"75","author":"M Hasanipanah","year":"2016","unstructured":"Hasanipanah M, Armaghani DJ, 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","journal-title":"Environ Earth Sci"},{"issue":"2","key":"5197_CR46","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s12517-012-0770-8","volume":"7","author":"M Monjezi","year":"2014","unstructured":"Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7(2):505\u2013511","journal-title":"Arab J Geosci"},{"key":"5197_CR47","volume-title":"Generalized boosted additive models","author":"S Amodio","year":"2011","unstructured":"Amodio S (2011) Generalized boosted additive models. University of Naples Federico II, Napoli"},{"issue":"13","key":"5197_CR48","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.3390\/app9132714","volume":"9","author":"LT Le","year":"2019","unstructured":"Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci 9(13):2714","journal-title":"Appl Sci"},{"issue":"13","key":"5197_CR49","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.3390\/app9132630","volume":"9","author":"LT Le","year":"2019","unstructured":"Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings\u2019 energy efficiency for smart city planning. Appl Sci 9(13):2630","journal-title":"Appl Sci"},{"key":"5197_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09503-7","author":"Y Shang","year":"2019","unstructured":"Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-019-09503-7","journal-title":"Nat Resour Res"},{"key":"5197_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00733-0","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Moayedi H, Foong LK, Al Najjar HAH, Jusoh WAW, Rashid ASA, Jamali J (2019) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00733-0","journal-title":"Eng Comput"},{"key":"5197_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09470-z","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Drebenstedt C, Bui X-N, Bui DT (2019) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-019-09470-z","journal-title":"Nat Resour Res"},{"key":"5197_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-018-00694-w","author":"H Moayedi","year":"2019","unstructured":"Moayedi H, Raftari M, Sharifi A, Jusoh WAW, Rashid ASA (2019) Optimization of ANFIS with GA and PSO estimating \u03b1 ratio in driven piles. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-018-00694-w","journal-title":"Eng Comput"},{"issue":"4","key":"5197_CR54","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s00366-016-0438-1","volume":"32","author":"E Ghasemi","year":"2016","unstructured":"Ghasemi E, Kalhori H, Bagherpour R (2016) A new hybrid ANFIS-PSO model for prediction of peak particle velocity due to bench blasting. Eng Comput 32(4):607\u2013614. https:\/\/doi.org\/10.1007\/s00366-016-0438-1","journal-title":"Eng Comput"},{"key":"5197_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-018-0659-6","author":"W Jiang","year":"2018","unstructured":"Jiang W, Arslan CA, Soltani Tehrani M, Khorami M, Hasanipanah M (2018) Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-018-0659-6","journal-title":"Eng Comput"},{"issue":"21","key":"5197_CR56","doi-asserted-by":"crossref","first-page":"6438","DOI":"10.1016\/j.apm.2015.01.059","volume":"39","author":"Y-T Xu","year":"2015","unstructured":"Xu Y-T, Zhang Y, Wang S-G (2015) A modified tunneling function method for non-smooth global optimization and its application in artificial neural network. Appl Math Model 39(21):6438\u20136450","journal-title":"Appl Math Model"},{"issue":"11\u201312","key":"5197_CR57","doi-asserted-by":"crossref","first-page":"2800","DOI":"10.1016\/j.apm.2013.10.073","volume":"38","author":"U Aich","year":"2014","unstructured":"Aich U, Banerjee S (2014) Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38(11\u201312):2800\u20132818","journal-title":"Appl Math Model"},{"key":"5197_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-019-00396-x","author":"H Nguyen","year":"2020","unstructured":"Nguyen H, Bui X-N, Tran Q-H, Van Hoa P, Nguyen D-A, Hoa LTT, Le Q-T, Do N-H, Bao TD, Bui H-B, Moayedi H (2020) A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophys. https:\/\/doi.org\/10.1007\/s11600-019-00396-x","journal-title":"Acta Geophys"},{"issue":"1","key":"5197_CR59","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3390\/s20010132","volume":"20","author":"H Nguyen","year":"2020","unstructured":"Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2020) Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20(1):132","journal-title":"Sensors"},{"key":"5197_CR60","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01003-0","author":"J Duan","year":"2020","unstructured":"Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01003-0","journal-title":"Eng Comput"},{"key":"5197_CR61","doi-asserted-by":"crossref","first-page":"103517","DOI":"10.1016\/j.tust.2020.103517","volume":"103","author":"X Zhang","year":"2020","unstructured":"Zhang X, Nguyen H, Bui X-N, Le HA, Nguyen-Thoi T, Moayedi H, Mahesh V (2020) Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunn Undergr Space Technol 103:103517","journal-title":"Tunn Undergr Space Technol"},{"issue":"2","key":"5197_CR62","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1214\/aos\/1176349548","volume":"13","author":"CJ Stone","year":"1985","unstructured":"Stone CJ (1985) Additive regression and other nonparametric models. Ann Stat 13(2):689\u2013705","journal-title":"Ann Stat"},{"key":"5197_CR63","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1201\/9780203738535-7","volume-title":"Statistical models in S","author":"TJ Hastie","year":"2017","unstructured":"Hastie TJ (2017) Generalized additive models. In: Hastie TJ, Tibshirani RJ (eds) Statistical models in S. Routledge, Abingdon, pp 249\u2013307"},{"key":"5197_CR64","volume-title":"Generalized additive models","author":"T Hastie","year":"1990","unstructured":"Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall Inc, London"},{"issue":"2","key":"5197_CR65","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0167-9473(98)00033-4","volume":"28","author":"BD Marx","year":"1998","unstructured":"Marx BD, Eilers PH (1998) Direct generalized additive modeling with penalized likelihood. Comput Stat Data Anal 28(2):193\u2013209","journal-title":"Comput Stat Data Anal"},{"issue":"4","key":"5197_CR66","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s001800000047","volume":"15","author":"MP Wand","year":"2000","unstructured":"Wand MP (2000) A comparison of regression spline smoothing procedures. Comput Stat 15(4):443\u2013462","journal-title":"Comput Stat"},{"issue":"2","key":"5197_CR67","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1111\/1467-9876.00229","volume":"50","author":"L Fahrmeir","year":"2001","unstructured":"Fahrmeir L, Lang S (2001) Bayesian inference for generalized additive mixed models based on Markov random field priors. J R Stat Soc: Ser C (Appl Stat) 50(2):201\u2013220","journal-title":"J R Stat Soc: Ser C (Appl Stat)"},{"issue":"2","key":"5197_CR68","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337\u2013407","journal-title":"Ann Stat"},{"key":"5197_CR69","volume-title":"The elements of statistical learning. Springer series in statistics","author":"J Friedman","year":"2001","unstructured":"Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 10. Springer, New York, NY"},{"issue":"4","key":"5197_CR70","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1111\/j.1541-0420.2006.00578.x","volume":"62","author":"G Tutz","year":"2006","unstructured":"Tutz G, Binder H (2006) Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 62(4):961\u2013971","journal-title":"Biometrics"},{"issue":"2","key":"5197_CR71","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ecolmodel.2006.05.022","volume":"199","author":"J Leathwick","year":"2006","unstructured":"Leathwick J, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199(2):188\u2013196","journal-title":"Ecol Model"},{"issue":"3","key":"5197_CR72","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1467-9876.2011.01033.x","volume":"61","author":"A Mayr","year":"2012","unstructured":"Mayr A, Fenske N, Hofner B, Kneib T, Schmid M (2012) Generalized additive models for location, scale and shape for high dimensional data\u2014a flexible approach based on boosting. J R Stat Soc: Ser C (Appl Stat) 61(3):403\u2013427","journal-title":"J R Stat Soc: Ser C (Appl Stat)"},{"issue":"2","key":"5197_CR73","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1111\/1468-0262.00200","volume":"69","author":"JL Horowitz","year":"2001","unstructured":"Horowitz JL (2001) Nonparametric estimation of a generalized additive model with an unknown link function. Econometrica 69(2):499\u2013513","journal-title":"Econometrica"},{"issue":"1","key":"5197_CR74","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/ejss.12193","volume":"66","author":"D de Brogniez","year":"2015","unstructured":"de Brogniez D, Ballabio C, Stevens A, Jones R, Montanarella L, van Wesemael B (2015) A map of the topsoil organic carbon content of Europe generated by a generalized additive model. Eur J Soil Sci 66(1):121\u2013134","journal-title":"Eur J Soil Sci"},{"issue":"2","key":"5197_CR75","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/S0165-7836(00)00306-4","volume":"53","author":"WA Walsh","year":"2001","unstructured":"Walsh WA, Kleiber P (2001) Generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery. Fish Res 53(2):115\u2013131","journal-title":"Fish Res"},{"key":"5197_CR76","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2017.09.007","volume":"297","author":"W Chen","year":"2017","unstructured":"Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69\u201385","journal-title":"Geomorphology"},{"issue":"6","key":"5197_CR77","doi-asserted-by":"crossref","first-page":"04018037","DOI":"10.1061\/(ASCE)CO.1943-7862.0001495","volume":"144","author":"X Liu","year":"2018","unstructured":"Liu X, Song Y, Yi W, Wang X, Zhu J (2018) Comparing the random forest with the generalized additive model to evaluate the impacts of outdoor ambient environmental factors on scaffolding construction productivity. J Constr Eng Manag 144(6):04018037","journal-title":"J Constr Eng Manag"},{"issue":"7","key":"5197_CR78","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.csda.2011.02.004","volume":"55","author":"G Marra","year":"2011","unstructured":"Marra G, Wood SN (2011) Practical variable selection for generalized additive models. Comput Stat Data Anal 55(7):2372\u20132387","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"5197_CR79","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11222-007-9040-0","volume":"18","author":"H Binder","year":"2008","unstructured":"Binder H, Tutz G (2008) A comparison of methods for the fitting of generalized additive models. Stat Comput 18(1):87\u201399","journal-title":"Stat Comput"},{"issue":"02","key":"5197_CR80","doi-asserted-by":"crossref","first-page":"168","DOI":"10.3414\/ME11-02-0021","volume":"51","author":"A Groll","year":"2012","unstructured":"Groll A, Tutz G (2012) Regularization for generalized additive mixed models by likelihood-based boosting. Methods Inf Med 51(02):168\u2013177","journal-title":"Methods Inf Med"},{"key":"5197_CR81","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.swevo.2013.06.001","volume":"13","author":"I Fister","year":"2013","unstructured":"Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34\u201346","journal-title":"Swarm Evol Comput"},{"key":"5197_CR82","first-page":"1","volume":"36","author":"J Zhou","year":"2019","unstructured":"Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2019) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput 36:1\u201310","journal-title":"Eng Comput"},{"issue":"7","key":"5197_CR83","doi-asserted-by":"crossref","first-page":"5134","DOI":"10.1016\/j.apm.2012.10.024","volume":"37","author":"R Ramezanian","year":"2013","unstructured":"Ramezanian R, Saidi-Mehrabad M (2013) Hybrid simulated annealing and MIP-based heuristics for stochastic lot-sizing and scheduling problem in capacitated multi-stage production system. Appl Math Model 37(7):5134\u20135147","journal-title":"Appl Math Model"},{"issue":"2","key":"5197_CR84","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1007\/s11053-019-09503-7","volume":"29","author":"Y Shang","year":"2020","unstructured":"Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2020) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res 29(2):723\u2013737. https:\/\/doi.org\/10.1007\/s11053-019-09503-7","journal-title":"Nat Resour Res"},{"key":"5197_CR85","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00822-0","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li C, Arslan CA, Hasanipanah M, Bakhshandeh Amnieh H (2019) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00822-0","journal-title":"Eng Comput"},{"key":"5197_CR86","first-page":"1","volume":"34","author":"PF Asl","year":"2017","unstructured":"Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2017) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34:1\u201311","journal-title":"Eng Comput"},{"issue":"6","key":"5197_CR87","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00521-016-2537-8","volume":"29","author":"RS Faradonbeh","year":"2018","unstructured":"Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl 29(6):269\u2013281","journal-title":"Neural Comput Appl"},{"key":"5197_CR88","volume-title":"Nature-inspired algorithms and applied optimization","author":"X-S Yang","year":"2017","unstructured":"Yang X-S (2017) Nature-inspired algorithms and applied optimization, vol 744. Springer, Berlin"},{"key":"5197_CR89","doi-asserted-by":"crossref","DOI":"10.1515\/9780691187563","volume-title":"Local search in combinatorial optimization","author":"E Aarts","year":"2003","unstructured":"Aarts E, Aarts EH, Lenstra JK (2003) Local search in combinatorial optimization. Princeton University Press, Princeton"},{"issue":"2","key":"5197_CR90","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s00366-010-0187-5","volume":"27","author":"A Bahrami","year":"2011","unstructured":"Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177\u2013181","journal-title":"Eng Comput"},{"issue":"2","key":"5197_CR91","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s00603-013-0415-6","volume":"47","author":"I Enayatollahi","year":"2014","unstructured":"Enayatollahi I, Bazzazi AA, Asadi A (2014) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47(2):799\u2013807","journal-title":"Rock Mech Rock Eng"},{"key":"5197_CR92","first-page":"1","volume":"2","author":"DJ Armaghani","year":"2018","unstructured":"Armaghani DJ (2018) Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr Res 2:1\u201312","journal-title":"Smart Constr Res"},{"issue":"8","key":"5197_CR93","doi-asserted-by":"crossref","first-page":"3903","DOI":"10.1016\/j.apm.2011.11.032","volume":"36","author":"A Nazari","year":"2012","unstructured":"Nazari A, Milani AA, Khalaj G (2012) Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS. Appl Math Model 36(8):3903\u20133915","journal-title":"Appl Math Model"},{"issue":"7","key":"5197_CR94","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1016\/j.apm.2014.10.016","volume":"39","author":"AM Abdulshahed","year":"2015","unstructured":"Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl Math Model 39(7):1837\u20131852","journal-title":"Appl Math Model"},{"issue":"4","key":"5197_CR95","first-page":"329","volume":"24","author":"PG Asteris","year":"2019","unstructured":"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 Concrete 24(4):329\u2013345","journal-title":"Comput Concrete"},{"key":"5197_CR96","doi-asserted-by":"crossref","first-page":"169","DOI":"10.2307\/2348250","volume":"41","author":"R Sakia","year":"1992","unstructured":"Sakia R (1992) The Box\u2013Cox transformation technique: a review. The Statistician 41:169\u2013178","journal-title":"The Statistician"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05197-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05197-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05197-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T23:40:31Z","timestamp":1627429231000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05197-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,28]]},"references-count":96,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5197"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05197-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,28]]},"assertion":[{"value":"22 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All the authors have participated in the conception, data collection, analysis, visualization, drafting, writing, editing, and revising of the manuscript. Also, all the authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}