{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T22:11:12Z","timestamp":1773094272899,"version":"3.50.1"},"reference-count":98,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00366-021-01332-8","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T05:25:16Z","timestamp":1614403516000},"page":"4007-4025","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Prediction of ground vibration intensity in mine blasting using the novel hybrid MARS\u2013PSO\u2013MLP model"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-8314","authenticated-orcid":false,"given":"Hoang","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan-Nam","family":"Bui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quang-Hieu","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoa Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinh-An","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le Thi Thu","family":"Hoa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qui-Thao","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"issue":"5","key":"1332_CR1","first-page":"1","volume":"61","author":"NX Bui","year":"2020","unstructured":"Bui NX, Ho GS (2020) Vietnamese Surface Mining - Training and scientific research for integrating the Fourth Industrial Revolution. J Min Earth Sci 61(5):1\u201315","journal-title":"J Min Earth Sci"},{"key":"1332_CR2","first-page":"388","volume":"388","author":"S Bhandari","year":"1997","unstructured":"Bhandari S (1997) Engineering rock blasting operations. A A Balkema 388:388","journal-title":"A A Balkema"},{"key":"1332_CR3","volume-title":"Review of criteria for estimating damage to residences from blasting vibrations","author":"WI Duvall","year":"1962","unstructured":"Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibrations. US Department of the Interior, Bureau of Mines"},{"issue":"4","key":"1332_CR4","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s00366-012-0298-2","volume":"30","author":"M Esmaeili","year":"2014","unstructured":"Esmaeili M, Osanloo M, Rashidinejad F, Bazzazi AA, Taji M (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30(4):549\u2013558","journal-title":"Eng Comput"},{"issue":"3","key":"1332_CR5","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1061\/(ASCE)0887-3828(2005)19:3(222)","volume":"19","author":"EF Gad","year":"2005","unstructured":"Gad EF, Wilson JL, Moore AJ, Richards AB (2005) Effects of mine blasting on residential structures. J Perform Constr Facilit 19(3):222\u2013228","journal-title":"J Perform Constr Facilit"},{"key":"1332_CR6","unstructured":"Chen G, Huang S (2000) Analysis of ground vibrations caused by open pit production blasts. Explos Blast Tech pp 63\u201370"},{"key":"1332_CR7","unstructured":"Davies B, Farmer I, Attewell P (1964) Ground vibration from shallow sub-surface blasts. Engineer 217 (5644)"},{"key":"1332_CR8","first-page":"021","volume":"6","author":"R Dindarloo Saeid","year":"2015","unstructured":"Dindarloo Saeid R (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J 6:021","journal-title":"Int J"},{"issue":"2","key":"1332_CR9","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s00603-014-0604-y","volume":"48","author":"M Khandelwal","year":"2015","unstructured":"Khandelwal M, Saadat M (2015) A dimensional analysis approach to study blast-induced ground vibration. Rock Mech Rock Eng 48(2):727\u2013735","journal-title":"Rock Mech Rock Eng"},{"issue":"2","key":"1332_CR10","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.soildyn.2006.06.004","volume":"27","author":"M Khandelwal","year":"2007","unstructured":"Khandelwal M, Singh T (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116\u2013125","journal-title":"Soil Dyn Earthq Eng"},{"issue":"1","key":"1332_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.tust.2010.05.002","volume":"26","author":"M Monjezi","year":"2011","unstructured":"Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26(1):46\u201350","journal-title":"Tunn Undergr Space Technol"},{"issue":"15","key":"1332_CR12","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s12665-019-8491-x","volume":"78","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environ Earth Sci 78(15):479. https:\/\/doi.org\/10.1007\/s12665-019-8491-x","journal-title":"Environ Earth Sci"},{"key":"1332_CR13","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.soildyn.2019.01.011","volume":"119","author":"SA Hosseini","year":"2019","unstructured":"Hosseini SA, Tavana A, Abdolahi SM, Darvishmaslak S (2019) Prediction of blast-induced ground vibrations in quarry sites: a comparison of GP, RSM and MARS. Soil Dyn Earthq Eng 119:118\u2013129","journal-title":"Soil Dyn Earthq Eng"},{"key":"1332_CR14","unstructured":"Hoang Nguyen, Nam Xuan Bui, Hieu Quang Tran, Giang Huong Thi Le, (2020) A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming. Journal of Mining and Earth Sciences 61 (5):107-116"},{"key":"1332_CR15","volume-title":"Technical manual simplified computer model of air blast effects on building walls","author":"R Mayor","year":"1990","unstructured":"Mayor R, Flanders R (1990) Technical manual simplified computer model of air blast effects on building walls. US Department of State, Office of Diplomatic Security, Washington DC"},{"key":"1332_CR16","unstructured":"Army U (1998) Technical manual design and analysis of hardened structures to conventional weapons effects. Army TM5\u2013855\u20131, Washington DC"},{"issue":"1","key":"1332_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3390\/s20010132","volume":"20","author":"H Nguyen","year":"2019","unstructured":"Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2019) 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":"1332_CR18","unstructured":"Duvall WI, Petkof B (1958) Spherical propagation of explosion-generated strain pulses in rock. Bureau of Mines"},{"key":"1332_CR19","volume-title":"The modern techniques of rock blasting","author":"U Langefors","year":"1963","unstructured":"Langefors U, Kihlstrom B (1963) The modern techniques of rock blasting. Wiley, New York"},{"key":"1332_CR20","unstructured":"Ambraseys N (1968) Rock mechanics in engineering practice"},{"key":"1332_CR21","unstructured":"Ghosh A, Daemen JJ A simple new blast vibration predictor (based on wave propagation laws). In: The 24th US Symposium on Rock Mechanics (USRMS), 1983. American Rock Mechanics Association"},{"issue":"2","key":"1332_CR22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/0167-9031(91)91642-U","volume":"12","author":"PP Roy","year":"1991","unstructured":"Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12(2):157\u2013165","journal-title":"Min Sci Technol"},{"issue":"9","key":"1332_CR23","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.soildyn.2007.11.006","volume":"28","author":"H Ak","year":"2008","unstructured":"Ak H, Konuk A (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dyn Earthq Eng 28(9):686\u2013694","journal-title":"Soil Dyn Earthq Eng"},{"key":"1332_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijrmms.2015.08.004","volume":"79","author":"GM Simangunsong","year":"2015","unstructured":"Simangunsong GM, Wahyudi S (2015) Effect of bedding plane on prediction blast-induced ground vibration in open pit coal mines. Int J Rock Mech Min Sci 79:1\u20138","journal-title":"Int J Rock Mech Min Sci"},{"issue":"5","key":"1332_CR25","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1177\/1077546312437002","volume":"19","author":"E Ghasemi","year":"2013","unstructured":"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(5):755\u2013770","journal-title":"J Vib Control"},{"issue":"1","key":"1332_CR26","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s00521-012-0845-1","volume":"22","author":"A Verma","year":"2013","unstructured":"Verma A, Singh T (2013) Comparative study of cognitive systems for ground vibration measurements. Neural Comput Appl 22(1):341\u2013350","journal-title":"Neural Comput Appl"},{"issue":"7\u20138","key":"1332_CR27","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1007\/s00521-012-0856-y","volume":"22","author":"M Monjezi","year":"2013","unstructured":"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(7\u20138):1637\u20131643","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1332_CR28","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s10064-014-0657-x","volume":"74","author":"M Hajihassani","year":"2015","unstructured":"Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74(3):873\u2013886","journal-title":"Bull Eng Geol Environ"},{"issue":"3","key":"1332_CR29","doi-asserted-by":"publisher","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"},{"key":"1332_CR30","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.measurement.2015.07.019","volume":"75","author":"M Hasanipanah","year":"2015","unstructured":"Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289\u2013297","journal-title":"Measurement"},{"key":"1332_CR31","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ijinfomgt.2019.01.021","volume":"48","author":"Y Duan","year":"2019","unstructured":"Duan Y, Edwards JS, Dwivedi YK (2019) Artificial intelligence for decision making in the era of Big Data\u2013evolution, challenges and research agenda. Int J Inf Manag 48:63\u201371","journal-title":"Int J Inf Manag"},{"key":"1332_CR32","doi-asserted-by":"crossref","unstructured":"Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, Duan Y, Dwivedi R, Edwards J, Eirug A (2019) Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag 101994","DOI":"10.1016\/j.ijinfomgt.2019.08.002"},{"issue":"4","key":"1332_CR33","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.bushor.2018.03.007","volume":"61","author":"MH Jarrahi","year":"2018","unstructured":"Jarrahi MH (2018) Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Bus Horiz 61(4):577\u2013586","journal-title":"Bus Horiz"},{"key":"1332_CR34","doi-asserted-by":"crossref","unstructured":"Kordon AK (2020) Applying data science: how to create value with artificial intelligence. Springer, New York","DOI":"10.1007\/978-3-030-36375-8"},{"issue":"6","key":"1332_CR35","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1038\/s42256-020-0183-4","volume":"2","author":"F Wu","year":"2020","unstructured":"Wu F, Lu C, Zhu M, Chen H, Zhu J, Yu K, Li L, Li M, Chen Q, Li X (2020) Towards a new generation of artificial intelligence in China. Nat Mach Intell 2(6):312\u2013316","journal-title":"Nat Mach Intell"},{"issue":"5","key":"1332_CR36","first-page":"16","volume":"61","author":"HQ Tran","year":"2020","unstructured":"Tran HQ, Bui NX, Nguyen H, Nguyen TA, Nguyen LQ (2020) Applicable posssibility of advanced technologies and equipment in surface mines of Vietnam. J Min Earth Sci 61(5):16\u201332","journal-title":"J Min Earth Sci"},{"issue":"7","key":"1332_CR37","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1080\/01900692.2018.1498103","volume":"42","author":"BW Wirtz","year":"2019","unstructured":"Wirtz BW, Weyerer JC, Geyer C (2019) Artificial intelligence and the public sector\u2014applications and challenges. Int J Public Admin 42(7):596\u2013615","journal-title":"Int J Public Admin"},{"key":"1332_CR38","doi-asserted-by":"crossref","unstructured":"Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl 1\u201320","DOI":"10.1007\/s00521-019-04663-2"},{"key":"1332_CR39","doi-asserted-by":"crossref","unstructured":"Asteris PG, Argyropoulos I, Cavaleri L, Rodrigues H, Varum H, Thomas J, Louren\u00e7o PB (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, New York, pp 200\u2013224","DOI":"10.1007\/978-3-030-12960-6_14"},{"issue":"2","key":"1332_CR40","first-page":"137","volume":"24","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Ashrafian A, Rezaie-Balf M (2019) Prediction of the compressive strength of self-compacting concrete using surrogate models. Comput Concr 24(2):137\u2013150","journal-title":"Comput Concr"},{"issue":"5","key":"1332_CR41","first-page":"58","volume":"61","author":"BD Tran","year":"2020","unstructured":"Tran BD, Vu TD, Pham VV, Nguyen TA, Nguyen AD, Le GHT (2020) Developing a mathematical model to optimize long - term quarrying planing for limestone quarries producing cement in Vietnam. J Min Earth Sci 61(5):58\u201370","journal-title":"J Min Earth Sci"},{"issue":"4","key":"1332_CR42","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1007\/s12665-015-4274-1","volume":"74","author":"M Hajihassani","year":"2015","unstructured":"Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799\u20132817","journal-title":"Environ Earth Sci"},{"issue":"4","key":"1332_CR43","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1007\/s12665-015-4305-y","volume":"74","author":"DJ Armaghani","year":"2015","unstructured":"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(4):2845\u20132860","journal-title":"Environ Earth Sci"},{"issue":"4","key":"1332_CR44","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s00366-016-0442-5","volume":"32","author":"M Amiri","year":"2016","unstructured":"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(4):631\u2013644","journal-title":"Eng Comput"},{"issue":"2","key":"1332_CR45","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s00366-016-0475-9","volume":"33","author":"F Hasanipanah","year":"2017","unstructured":"Hasanipanah F, Amnieh A, Monjezi (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33(2):307\u2013316","journal-title":"Eng Comput"},{"issue":"3","key":"1332_CR46","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s00366-016-0497-3","volume":"33","author":"K Taheri","year":"2017","unstructured":"Taheri K, Hasanipanah M, Golzar SB, Majid MZA (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689\u2013700","journal-title":"Eng Comput"},{"issue":"2","key":"1332_CR47","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s00366-017-0546-6","volume":"34","author":"H Sheykhi","year":"2018","unstructured":"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(2):357\u2013365","journal-title":"Eng Comput"},{"issue":"2","key":"1332_CR48","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1007\/s11053-019-09503-7","volume":"29","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 29(2):723\u2013737. https:\/\/doi.org\/10.1007\/s11053-019-09503-7","journal-title":"Nat Resour Res"},{"key":"1332_CR49","doi-asserted-by":"crossref","unstructured":"Ding X, Hasanipanah M, Rad HN, Zhou W (2020) Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm. Eng Comput 1\u201312","DOI":"10.1007\/s00366-020-00937-9"},{"issue":"2","key":"1332_CR50","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s11053-019-09515-3","volume":"29","author":"H Yang","year":"2019","unstructured":"Yang H, Hasanipanah M, Tahir MM, Bui DT (2019) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res 29(2):739\u2013750. https:\/\/doi.org\/10.1007\/s11053-019-09515-3","journal-title":"Nat Resour Res"},{"key":"1332_CR51","doi-asserted-by":"crossref","unstructured":"Fattahi H, Hasanipanah M (2020) Prediction of blast-induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms. Nat Resour Res 1\u201315","DOI":"10.1007\/s11053-020-09764-7"},{"issue":"2","key":"1332_CR52","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11053-019-09492-7","volume":"29","author":"X Zhang","year":"2019","unstructured":"Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2019) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res 29(2):711\u2013721. https:\/\/doi.org\/10.1007\/s11053-019-09492-7","journal-title":"Nat Resour Res"},{"key":"1332_CR53","doi-asserted-by":"crossref","unstructured":"Chen W, Hasanipanah M, Rad HN, Armaghani DJ, Tahir M (2019) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng Comput 1\u201317","DOI":"10.1007\/s00366-019-00895-x"},{"issue":"1","key":"1332_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-50262-5","volume":"9","author":"X-N Bui","year":"2019","unstructured":"Bui X-N, Jaroonpattanapong P, Nguyen H, Tran Q-H, Long NQ (2019) A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle Swarm optimization. Sci Rep 9(1):1\u201314","journal-title":"Sci Rep"},{"issue":"2","key":"1332_CR55","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s11053-019-09577-3","volume":"29","author":"Q Fang","year":"2019","unstructured":"Fang Q, Nguyen H, Bui X-N, Nguyen-Thoi T (2019) Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Nat Resour Res 29(2):791\u2013806. https:\/\/doi.org\/10.1007\/s11053-019-09577-3","journal-title":"Nat Resour Res"},{"key":"1332_CR56","doi-asserted-by":"publisher","first-page":"106874","DOI":"10.1016\/j.measurement.2019.106874","volume":"147","author":"Y Azimi","year":"2019","unstructured":"Azimi Y, Khoshrou SH, Osanloo M (2019) Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement 147:106874","journal-title":"Measurement"},{"key":"1332_CR57","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.apacoust.2019.03.023","volume":"152","author":"X Xue","year":"2019","unstructured":"Xue X (2019) Neuro-fuzzy based approach for prediction of blast-induced ground vibration. Appl Acoust 152:73\u201378","journal-title":"Appl Acoust"},{"key":"1332_CR58","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"},{"key":"1332_CR59","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09548-8","author":"Z Ding","year":"2019","unstructured":"Ding Z, Nguyen H, Bui X-N, Zhou J, Moayedi H (2019) Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-019-09548-8","journal-title":"Nat Resour Res"},{"issue":"2","key":"1332_CR60","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s11053-019-09470-z","volume":"29","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 29(2):691\u2013709","journal-title":"Nat Resour Res"},{"issue":"4","key":"1332_CR61","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.3390\/app10041403","volume":"10","author":"Z Yu","year":"2020","unstructured":"Yu Z, Shi X, Zhou J, Chen X, Qiu X (2020) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a harris hawks optimization algorithm. Appl Sci 10(4):1403","journal-title":"Appl Sci"},{"key":"1332_CR62","doi-asserted-by":"crossref","unstructured":"Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 1\u201367","DOI":"10.1214\/aos\/1176347963"},{"key":"1332_CR63","doi-asserted-by":"publisher","DOI":"10.1177\/096228029500400303","volume-title":"An introduction to multivariate adaptive regression splines","author":"JH Friedman","year":"1995","unstructured":"Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Sage Publications Sage CA, Thousand Oaks, CA"},{"issue":"4","key":"1332_CR64","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1016\/j.csda.2004.11.006","volume":"50","author":"T-S Lee","year":"2006","unstructured":"Lee T-S, Chiu C-C, Chou Y-C, Lu C-J (2006) Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Comput Stat Data Anal 50(4):1113\u20131130","journal-title":"Comput Stat Data Anal"},{"issue":"8","key":"1332_CR65","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1016\/j.jss.2006.10.049","volume":"80","author":"Y Zhou","year":"2007","unstructured":"Zhou Y, Leung H (2007) Predicting object-oriented software maintainability using multivariate adaptive regression splines. J Syst Softw 80(8):1349\u20131361","journal-title":"J Syst Softw"},{"key":"1332_CR66","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.compgeo.2012.09.016","volume":"48","author":"W Zhang","year":"2013","unstructured":"Zhang W, Goh ATC (2013) Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Comput Geotech 48:82\u201395","journal-title":"Comput Geotech"},{"issue":"4","key":"1332_CR67","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1088\/1742-2140\/aaac5d","volume":"15","author":"M Anemangely","year":"2018","unstructured":"Anemangely M, Ramezanzadeh A, Tokhmechi B, Molaghab A, Mohammadian A (2018) Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network. J Geophys Eng 15(4):1146\u20131159","journal-title":"J Geophys Eng"},{"issue":"6","key":"1332_CR68","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.2478\/s11600-014-0207-8","volume":"62","author":"\u00c7 \u00c7aylak","year":"2014","unstructured":"\u00c7aylak \u00c7, Kaftan \u0130 (2014) Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm. Acta Geophys 62(6):1310\u20131327. https:\/\/doi.org\/10.2478\/s11600-014-0207-8","journal-title":"Acta Geophys"},{"key":"1332_CR69","doi-asserted-by":"publisher","first-page":"101555","DOI":"10.1016\/j.resourpol.2019.101555","volume":"65","author":"AA Ewees","year":"2020","unstructured":"Ewees AA, Elaziz MA, Alameer Z, Ye H, Jianhua Z (2020) Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resour Policy 65:101555","journal-title":"Resour Policy"},{"issue":"17","key":"1332_CR70","doi-asserted-by":"publisher","first-page":"7941","DOI":"10.1007\/s00500-018-3424-2","volume":"23","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Faris H, Aljarah I, Mirjalili S (2019) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23(17):7941\u20137958","journal-title":"Soft Comput"},{"key":"1332_CR71","doi-asserted-by":"crossref","unstructured":"Rosa JP, Guerra DJ, Horta NC, Martins RM, Louren\u00e7o NC (2020) Overview of artificial neural networks. In: Using artificial neural networks for analog integrated circuit design automation, Springer, pp 21\u201344","DOI":"10.1007\/978-3-030-35743-6_3"},{"issue":"2","key":"1332_CR72","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.bspc.2010.01.004","volume":"5","author":"AR Naghsh-Nilchi","year":"2010","unstructured":"Naghsh-Nilchi AR, Aghashahi M (2010) Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network. Biomed Signal Process Control 5(2):147\u2013157","journal-title":"Biomed Signal Process Control"},{"issue":"8","key":"1332_CR73","doi-asserted-by":"publisher","first-page":"3939","DOI":"10.1007\/s00521-018-3717-5","volume":"32","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 airblast overpressure at Deo Nai open-pit coal mine. Vietnam Neural Comput Appl 32(8):3939\u20133955. https:\/\/doi.org\/10.1007\/s00521-018-3717-5","journal-title":"Vietnam Neural Comput Appl"},{"issue":"11","key":"1332_CR74","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1007\/s13369-015-1685-y","volume":"40","author":"S-A Ouadfeul","year":"2015","unstructured":"Ouadfeul S-A, Aliouane L (2015) Total organic carbon prediction in shale gas reservoirs from well logs data using the multilayer perceptron neural network with Levenberg Marquardt training algorithm: application to Barnett Shale. Arab J Sci Eng 40(11):3345\u20133349","journal-title":"Arab J Sci Eng"},{"key":"1332_CR75","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.icheatmasstransfer.2017.12.012","volume":"91","author":"H Ansari","year":"2018","unstructured":"Ansari H, Zarei M, Sabbaghi S, Keshavarz P (2018) A new comprehensive model for relative viscosity of various nanofluids using feedforward back-propagation MLP neural networks. Int Commun Heat Mass Transf 91:158\u2013164","journal-title":"Int Commun Heat Mass Transf"},{"key":"1332_CR76","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jngse.2014.07.022","volume":"21","author":"M Hamzehie","year":"2014","unstructured":"Hamzehie M, Mazinani S, Davardoost F, Mokhtare A, Najibi H, Van der Bruggen B, Darvishmanesh S (2014) Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions. J Nat Gas Sci Eng 21:19\u201325","journal-title":"J Nat Gas Sci Eng"},{"key":"1332_CR77","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO). In: Proc. IEEE International Conference on Neural Networks, Perth, Australia, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1332_CR78","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-020-09727-y","author":"H Nguyen","year":"2020","unstructured":"Nguyen H, Bui H-B, Bui X-N (2020) Rapid determination of gross calorific value of coal using artificial neural network and particle swarm optimization. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-020-09727-y","journal-title":"Nat Resour Res"},{"issue":"1","key":"1332_CR79","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10462-013-9400-4","volume":"44","author":"AA Esmin","year":"2015","unstructured":"Esmin AA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44(1):23\u201345","journal-title":"Artif Intell Rev"},{"key":"1332_CR80","doi-asserted-by":"crossref","unstructured":"Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, New York, pp 760\u2013766","DOI":"10.1007\/978-0-387-30164-8_630"},{"issue":"4","key":"1332_CR81","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.1007\/s13369-018-03713-6","volume":"44","author":"S Lalwani","year":"2019","unstructured":"Lalwani S, Sharma H, Satapathy SC, Deep K, Bansal JC (2019) A survey on parallel particle swarm optimization algorithms. Arab J Sci Eng 44(4):2899\u20132923","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"1332_CR82","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","volume":"1","author":"R Poli","year":"2007","unstructured":"Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33\u201357","journal-title":"Swarm Intell"},{"key":"1332_CR83","doi-asserted-by":"crossref","unstructured":"Shanthi M, Meenakshi DK, Ramesh PK (2018) Particle swarm optimization. In: Advances in swarm intelligence for optimizing problems in computer science, Chapman and Hall\/CRC, pp 115\u2013144","DOI":"10.1201\/9780429445927-5"},{"key":"1332_CR84","unstructured":"Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: evolutionary computation, Proceedings of the 2001 Congress on, 2001. IEEE, pp 81\u201386"},{"issue":"2","key":"1332_CR85","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"D Wang","year":"2018","unstructured":"Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387\u2013408","journal-title":"Soft Comput"},{"issue":"5","key":"1332_CR86","first-page":"117","volume":"61","author":"AD Nguyen","year":"2020","unstructured":"Nguyen AD, Nhu BV, Tran BD, Pham HV, Nguyen TA (2020) Definition of amount explosive per blast for spillway at the Nui Mot lake - Binh Dinh province. J Min Earth Sci 61(5):117\u2013124","journal-title":"J Min Earth Sci"},{"key":"1332_CR87","unstructured":"Jimeno EL, Jimino CL, Carcedo A (1995) Drilling and blasting of rocks. CRC Press, New York"},{"issue":"6","key":"1332_CR88","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s10661-018-6719-y","volume":"190","author":"RS Faradonbeh","year":"2018","unstructured":"Faradonbeh RS, Hasanipanah M, Amnieh HB, Armaghani DJ, Monjezi M (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190(6):351","journal-title":"Environ Monit Assess"},{"issue":"3","key":"1332_CR89","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/s13762-017-1395-y","volume":"15","author":"M Hasanipanah","year":"2018","unstructured":"Hasanipanah M, Amnieh HB, Khamesi H, Armaghani DJ, Golzar SB, Shahnazar A (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15(3):551\u2013560","journal-title":"Int J Environ Sci Technol"},{"issue":"4","key":"1332_CR90","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1179\/037178404225006137","volume":"113","author":"T Singh","year":"2004","unstructured":"Singh T (2004) Artificial neural network approach for prediction and control of ground vibrations in mines. Min Technol 113(4):251\u2013256","journal-title":"Min Technol"},{"issue":"4","key":"1332_CR91","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/S1674-5264(09)60078-8","volume":"19","author":"TB Afeni","year":"2009","unstructured":"Afeni TB, Osasan SK (2009) Assessment of noise and ground vibration induced during blasting operations in an open pit mine\u2014a case study on Ewekoro limestone quarry. Nigeria Min Sci Technol (China) 19(4):420\u2013424","journal-title":"Nigeria Min Sci Technol (China)"},{"issue":"3","key":"1332_CR92","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10706-004-7068-x","volume":"23","author":"T Singh","year":"2005","unstructured":"Singh T, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23(3):249\u2013262","journal-title":"Geotech Geol Eng"},{"issue":"Suppl 1","key":"1332_CR93","first-page":"23","volume":"14","author":"EC Alexopoulos","year":"2010","unstructured":"Alexopoulos EC (2010) Introduction to multivariate regression analysis. Hippokratia 14(Suppl 1):23","journal-title":"Hippokratia"},{"issue":"2","key":"1332_CR94","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","volume":"26","author":"GE Box","year":"1964","unstructured":"Box GE, Cox DR (1964) An analysis of transformations. J Roy Stat Soc: Ser B (Methodol) 26(2):211\u2013243","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"1\u20133","key":"1332_CR95","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/S0925-2312(98)00111-8","volume":"25","author":"V Maiorov","year":"1999","unstructured":"Maiorov V, Pinkus A (1999) Lower bounds for approximation by MLP neural networks. Neurocomputing 25(1\u20133):81\u201391","journal-title":"Neurocomputing"},{"issue":"1","key":"1332_CR96","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/BF03325972","volume":"4","author":"G Bandyopadhyay","year":"2007","unstructured":"Bandyopadhyay G, Chattopadhyay S (2007) Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int J Environ Sci Technol 4(1):141\u2013149","journal-title":"Int J Environ Sci Technol"},{"key":"1332_CR97","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.neunet.2017.12.007","volume":"98","author":"NJ Guliyev","year":"2018","unstructured":"Guliyev NJ, Ismailov VE (2018) On the approximation by single hidden layer feedforward neural networks with fixed weights. Neural Netw 98:296\u2013304","journal-title":"Neural Netw"},{"key":"1332_CR98","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.jbi.2018.06.003","volume":"83","author":"S Belciug","year":"2018","unstructured":"Belciug S, Gorunescu F (2018) Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection. J Biomed Inf 83:159\u2013166","journal-title":"J Biomed Inf"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-021-01332-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-021-01332-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-021-01332-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T23:32:31Z","timestamp":1724542351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-021-01332-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":98,"journal-issue":{"issue":"S5","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1332"],"URL":"https:\/\/doi.org\/10.1007\/s00366-021-01332-8","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,27]]},"assertion":[{"value":"26 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2021","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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}