{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T17:14:09Z","timestamp":1722532449283},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,8,26]],"date-time":"2015-08-26T00:00:00Z","timestamp":1440547200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2016,11]]},"DOI":"10.1007\/s00521-015-2017-6","type":"journal-article","created":{"date-parts":[[2015,8,25]],"date-time":"2015-08-25T06:31:14Z","timestamp":1440484274000},"page":"2465-2473","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Modeling concrete strength with high-order neural networks"],"prefix":"10.1007","volume":"27","author":[{"given":"Hsing-Chih","family":"Tsai","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,8,26]]},"reference":[{"issue":"6","key":"2017_CR1","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s00521-011-0595-5","volume":"21","author":"PG Ranjith","year":"2012","unstructured":"Ranjith PG, Khandelwal M (2012) Artificial neural network for prediction of air flow in a single rock joint. Neural Comput Appl 21(6):1413\u20131422","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2017_CR2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s00521-012-0944-z","volume":"23","author":"R Singh","year":"2013","unstructured":"Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499\u2013506","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2017_CR3","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s00521-012-0925-2","volume":"23","author":"A Majdi","year":"2013","unstructured":"Majdi A, Rezaei M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Comput Appl 23(2):381\u2013389","journal-title":"Neural Comput Appl"},{"issue":"7\u20138","key":"2017_CR4","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1007\/s00521-012-1175-z","volume":"23","author":"R Khatibi","year":"2013","unstructured":"Khatibi R, Ghorbani MA, Aalami MT (2013) Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations. Neural Comput Appl 23(7\u20138):2241\u20132252","journal-title":"Neural Comput Appl"},{"issue":"3\u20134","key":"2017_CR5","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s00521-014-1573-5","volume":"25","author":"J O\u00f1a De","year":"2014","unstructured":"De O\u00f1a J, Garrido C (2014) Extracting the contribution of independent variables in neural network models: a new approach to handle instability. Neural Comput Appl 25(3\u20134):859\u2013869","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2017_CR6","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s00521-013-1451-6","volume":"25","author":"M Kankal","year":"2014","unstructured":"Kankal M, Y\u00fcksek O (2014) Artificial neural network for estimation of harbor oscillation in a cargo harbor basin. Neural Comput Appl 25(1):95\u2013103","journal-title":"Neural Comput Appl"},{"issue":"4","key":"2017_CR7","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.engappai.2009.10.003","volume":"23","author":"MY Cheng","year":"2010","unstructured":"Cheng MY, Tsai HC, Sudjono E (2010) Evolutionary fuzzy hybrid neural network for project cash flow control. Eng Appl Artif Intell 23(4):604\u2013613","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"2017_CR8","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.asoc.2011.09.010","volume":"12","author":"R Singh","year":"2012","unstructured":"Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40\u201345","journal-title":"Appl Soft Comput"},{"key":"2017_CR9","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. Meas J Int Meas Confed 68:302\u2013309. doi: 10.1016\/j.measurement.2015.03.009","journal-title":"Meas J Int Meas Confed"},{"key":"2017_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-015-9869-5","author":"R Trivedi","year":"2015","unstructured":"Trivedi R, Singh TN, Gupta N (2015) Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech Geol Eng. doi: 10.1007\/s10706-015-9869-5","journal-title":"Geotech Geol Eng"},{"key":"2017_CR11","volume-title":"Introduction to artificial neural systems","author":"JM Zurada","year":"1992","unstructured":"Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company, St. Paul"},{"issue":"6","key":"2017_CR12","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.patrec.2004.09.029","volume":"26","author":"E Artyomov","year":"2005","unstructured":"Artyomov E, Yadid-Pecht O (2005) Modified high-order neural network for invariant pattern recognition. Pattern Recognit Lett 26(6):843\u2013851","journal-title":"Pattern Recognit Lett"},{"issue":"2","key":"2017_CR13","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TSMCB.2003.818538","volume":"34","author":"GL Foresti","year":"2004","unstructured":"Foresti GL, Dolso T (2004) An adaptive high-order neural tree for pattern recognition. IEEE Trans Syst Man Cybern Part B Cybern 34(2):988\u2013996","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"issue":"1","key":"2017_CR14","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/TSMCB.2003.811767","volume":"34","author":"GA Rovithakis","year":"2004","unstructured":"Rovithakis GA, Chalkiadakis I, Zervakis ME (2004) High-order neural network structure selection for function approximation applications using genetic algorithms. IEEE Trans Syst Man Cybern Part B Cybern 34(1):150\u2013158","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"key":"2017_CR15","first-page":"35","volume":"1","author":"JH Wang","year":"1995","unstructured":"Wang JH, Lin JH (1995) Qualitative analysis of the BP composed of product units and summing units. Proc IEEE Int Conf Syst Man Cybern 1:35\u201339","journal-title":"Proc IEEE Int Conf Syst Man Cybern"},{"issue":"2","key":"2017_CR16","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.asoc.2009.08.010","volume":"10","author":"MG Epitropakis","year":"2010","unstructured":"Epitropakis MG, Plagianakos VP, Vrahatis MN (2010) Hardware-friendly higher-order neural network training using distributed evolutionary algorithms. Appl Soft Comput 10(2):398\u2013408","journal-title":"Appl Soft Comput"},{"key":"2017_CR17","first-page":"1257","volume":"2","author":"A Abdelbar","year":"1996","unstructured":"Abdelbar A, Tagliarini G (1996) HONEST: a new high order feedforward neural network. Proc IEEE Int Conf Neural Netw 2:1257\u20131262","journal-title":"Proc IEEE Int Conf Neural Netw"},{"key":"2017_CR18","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.asoc.2008.11.007","volume":"9","author":"HC Tsai","year":"2009","unstructured":"Tsai HC (2009) Hybrid high order neural networks. Appl Soft Comput 9:874\u2013881","journal-title":"Appl Soft Comput"},{"key":"2017_CR19","doi-asserted-by":"publisher","unstructured":"Eberhart RC, Kennedy J (1995) A new optimizer using particles swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39\u201343. doi: 10.1109\/MHS.1995.494215","DOI":"10.1109\/MHS.1995.494215"},{"key":"2017_CR20","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"RC Eberhart","year":"1995","unstructured":"Eberhart RC, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942\u20131948","journal-title":"Proc IEEE Int Conf Neural Netw"},{"key":"2017_CR21","unstructured":"Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme. In: Proceedings of the international conference of computational methods in sciences and engineering, ICCMSE 2004. Lecture series on computer and computational sciences, vol 1, pp 868\u2013873"},{"issue":"4\u20136","key":"2017_CR22","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.neucom.2006.10.002","volume":"70","author":"Y Liu","year":"2007","unstructured":"Liu Y, Qin Z, Shi Z, Lu J (2007) Center particle swarm optimization. Neurocomputing 70(4\u20136):672\u2013679","journal-title":"Neurocomputing"},{"issue":"12","key":"2017_CR23","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/0305215X.2012.654787","volume":"44","author":"HC Tsai","year":"2012","unstructured":"Tsai HC, Tyan YY, Wu YW, Lin YH (2012) Isolated particle swarm optimization with particle migration and global best adoption. Eng Optim 44(12):1405\u20131424","journal-title":"Eng Optim"},{"issue":"1\u20132","key":"2017_CR24","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.mcm.2006.12.019","volume":"46","author":"KE Parsopoulos","year":"2007","unstructured":"Parsopoulos KE, Vrahatis MN (2007) Parameter selection and adaptation in unified particle swarm optimization. Math Comput Model 46(1\u20132):198\u2013213","journal-title":"Math Comput Model"},{"key":"2017_CR25","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.amc.2006.07.025","volume":"185","author":"JR Zhang","year":"2007","unstructured":"Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization algorithm for feedforward neural network training. Appl Math Comput 185:1026\u20131037","journal-title":"Appl Math Comput"},{"issue":"2","key":"2017_CR26","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.eswa.2009.06.093","volume":"37","author":"HC Tsai","year":"2010","unstructured":"Tsai HC (2010) Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization. Expert Syst Appl 37(2):1104\u20131112","journal-title":"Expert Syst Appl"},{"issue":"5","key":"2017_CR27","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/72.248452","volume":"4","author":"R Reed","year":"1993","unstructured":"Reed R (1993) Pruning algorithms\u2014a survey. IEEE Trans Neural Netw 4(5):740\u2013747","journal-title":"IEEE Trans Neural Netw"},{"key":"2017_CR28","doi-asserted-by":"publisher","unstructured":"Meng J (2005) Penalty OBS scheme for feedforward neural network. In: Proceedings of international conference on tools with artificial intelligence, pp 479\u2013483. doi: 10.1109\/ICTAI.2005.93","DOI":"10.1109\/ICTAI.2005.93"},{"key":"2017_CR29","doi-asserted-by":"publisher","unstructured":"Tu J, Zhan Y, Han FA (2010) A neural network pruning method optimized with PSO algorithm. In: Proceedings of ICCMS 2010\u20132010 international conference on computer modeling and simulation, vol 3. pp 57\u2013259. doi: 10.1109\/ICCMS.2010.424","DOI":"10.1109\/ICCMS.2010.424"},{"issue":"10","key":"2017_CR30","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ins.2013.09.015","volume":"258","author":"HC Tsai","year":"2014","unstructured":"Tsai HC (2014) Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Inf Sci 258(10):80\u201393","journal-title":"Inf Sci"},{"key":"2017_CR31","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s00521-013-1528-2","volume":"25","author":"HC Tsai","year":"2014","unstructured":"Tsai HC (2014) Integrating artificial bee colony and bees algorithm for solving numerical function optimization. Neural Comput Appl 25:635\u2013651","journal-title":"Neural Comput Appl"},{"issue":"15","key":"2017_CR32","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.amc.2014.09.079","volume":"247","author":"HC Tsai","year":"2014","unstructured":"Tsai HC (2014) Novel bees algorithm: stochastic self-adaptive neighborhood. Appl Math Comput 247(15):1161\u20131172","journal-title":"Appl Math Comput"},{"issue":"1","key":"2017_CR33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.engappai.2014.12.003","volume":"39","author":"HC Tsai","year":"2015","unstructured":"Tsai HC (2015) Roach infestation optimization with friendship centers. Eng Appl Artif Intell 39(1):109\u2013119","journal-title":"Eng Appl Artif Intell"},{"key":"2017_CR34","doi-asserted-by":"publisher","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In:\u00a0Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, pp 69\u201373. doi: 10.1109\/ICEC.1998.699146","DOI":"10.1109\/ICEC.1998.699146"},{"key":"2017_CR35","doi-asserted-by":"publisher","unstructured":"Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000, pp 84\u201388. doi: 10.1109\/CEC.2000.870279","DOI":"10.1109\/CEC.2000.870279"},{"issue":"4","key":"2017_CR36","first-page":"362","volume":"91","author":"FA Oluokun","year":"1994","unstructured":"Oluokun FA (1994) Fly ash concrete mix design and the water-cement ratio law. ACI Mater J 91(4):362\u2013371","journal-title":"ACI Mater J"},{"issue":"3","key":"2017_CR37","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/S0008-8846(96)85034-4","volume":"26","author":"KG Babu","year":"1996","unstructured":"Babu KG, Rao GSN (1996) Efficiency of fly ash in concrete with age. Cem Concr Res 26(3):465\u2013474","journal-title":"Cem Concr Res"},{"issue":"5","key":"2017_CR38","first-page":"517","volume":"87","author":"S Popovics","year":"1990","unstructured":"Popovics S (1990) Analysis of the concrete strength versus water\u2013cement ratio relationship. ACI Mater J 87(5):517\u2013529","journal-title":"ACI Mater J"},{"issue":"6","key":"2017_CR39","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/0008-8846(96)00065-8","volume":"26","author":"T Nagaraj","year":"1996","unstructured":"Nagaraj T, Banu Z (1996) Generalization of Abrams\u2019 law. Cem Concr Res 26(6):933\u2013942","journal-title":"Cem Concr Res"},{"issue":"12","key":"2017_CR40","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1016\/S0008-8846(98)00165-3","volume":"28","author":"IC Yeh","year":"1998","unstructured":"Yeh IC (1998) Modeling of strength of high performance concrete using artificial neural networks. Cem Concr Res 28(12):1797\u20131808","journal-title":"Cem Concr Res"},{"issue":"3","key":"2017_CR41","doi-asserted-by":"crossref","first-page":"203","DOI":"10.12989\/cac.2009.6.3.203","volume":"6","author":"CH Peng","year":"2009","unstructured":"Peng CH, Yeh IC, Lien LC (2009) Modeling strength of high-performance concrete using genetic operation trees with pruning techniques. Comput Concr 6(3):203\u2013223","journal-title":"Comput Concr"},{"key":"2017_CR42","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"JR Koza","year":"1992","unstructured":"Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge"},{"issue":"1","key":"2017_CR43","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.eswa.2010.06.034","volume":"38","author":"HC Tsai","year":"2011","unstructured":"Tsai HC (2011) Weighted operation structures to program strengths of concrete-typed specimens using genetic algorithm. Expert Syst Appl 38(1):161\u2013168","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2017_CR44","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00366-011-0208-z","volume":"27","author":"HC Tsai","year":"2011","unstructured":"Tsai HC, Lin YH (2011) Predicting high-strength concrete parameters using weighted genetic programming. Eng Comput 27(4):347\u2013355","journal-title":"Eng Comput"},{"issue":"3","key":"2017_CR45","doi-asserted-by":"crossref","first-page":"207","DOI":"10.2166\/hydro.2006.020b","volume":"8","author":"O Giustolisi","year":"2006","unstructured":"Giustolisi O, Savic DA (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinform 8(3):207\u2013222","journal-title":"J Hydroinform"},{"issue":"1","key":"2017_CR46","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.advengsoft.2011.11.001","volume":"47","author":"A Fiore","year":"2012","unstructured":"Fiore A, Berardi L, Marano GC (2012) Predicting torsional strength of RC beams by using evolutionary polynomial regression. Adv Eng Softw 47(1):178\u2013187","journal-title":"Adv Eng Softw"},{"issue":"3","key":"2017_CR47","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.engappai.2010.08.010","volume":"24","author":"HC Tsai","year":"2011","unstructured":"Tsai HC (2011) Using weighted genetic programming to program squat wall strengths and tune associated formulas. Eng Appl Artif Intell 24(3):526\u2013533","journal-title":"Eng Appl Artif Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2017-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-2017-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2017-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2017-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T22:08:23Z","timestamp":1653084503000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-2017-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,26]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["2017"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-2017-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,8,26]]}}}