{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:32:26Z","timestamp":1781796746960,"version":"3.54.5"},"reference-count":105,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"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":[[2021,12]]},"DOI":"10.1007\/s00521-021-06217-x","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T16:02:21Z","timestamp":1624809741000},"page":"16149-16179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm"],"prefix":"10.1007","volume":"33","author":[{"given":"Hooman","family":"Harandizadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danial Jahed","family":"Armaghani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7142-4981","authenticated-orcid":false,"given":"Panagiotis G.","family":"Asteris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"key":"6217_CR1","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.engappai.2009.03.007","volume":"22","author":"S Yagiz","year":"2009","unstructured":"Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808\u2013814","journal-title":"Eng Appl Artif Intell"},{"key":"6217_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00701-8","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor M, Fahimifar A, Ghaleini EN et al (2019) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00701-8","journal-title":"Eng Comput"},{"key":"6217_CR3","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.ijrmms.2015.09.019","volume":"80","author":"S Yagiz","year":"2015","unstructured":"Yagiz S, Karahan H (2015) Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. Int J Rock Mech Min Sci 80:308\u2013315","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR4","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0886-7798(00)00055-9","volume":"15","author":"MA Grima","year":"2000","unstructured":"Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259\u2013269","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR5","first-page":"202","volume":"173","author":"P Bruines","year":"1998","unstructured":"Bruines P (1998) Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate. Mem Cent Eng Geol Neth Delft 173:202","journal-title":"Mem Cent Eng Geol Neth Delft"},{"key":"6217_CR6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s00603-004-0032-5","volume":"38","author":"R Ribacchi","year":"2005","unstructured":"Ribacchi R, Fazio AL (2005) Influence of rock mass parameters on the performance of a TBM in a gneissic formation (Varzo Tunnel). Rock Mech Rock Eng 38:105\u2013127","journal-title":"Rock Mech Rock Eng"},{"key":"6217_CR7","unstructured":"Innaurato N, Mancini A, Rondena E, Zaninetti A (1991) Forecasting and effective TBM performances in a rapid excavation of a tunnel in Italy. In: 7th ISRM Congress. International Society for Rock Mechanics and Rock Engineering"},{"key":"6217_CR8","volume-title":"Hard rock tunnel boring","author":"A Bruland","year":"1998","unstructured":"Bruland A (1998) Hard rock tunnel boring. Norwegian University of Science and Technology, Trondheim"},{"key":"6217_CR9","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/BF02832128","volume":"11","author":"W Shijing","year":"2006","unstructured":"Shijing W, Bo Q, Zhibo G (2006) The time and cost prediction of tunnel boring machine in tunnelling. Wuhan Univ J Nat Sci 11:385\u2013388","journal-title":"Wuhan Univ J Nat Sci"},{"key":"6217_CR10","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.tust.2007.04.011","volume":"23","author":"S Yagiz","year":"2008","unstructured":"Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Sp Technol 23:326\u2013339","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.tust.2018.07.023","volume":"81","author":"HQ Yang","year":"2018","unstructured":"Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Sp Technol 81:112\u2013120","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR12","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijrmms.2014.03.003","volume":"69","author":"HQ Yang","year":"2014","unstructured":"Yang HQ, Zeng YY, Lan YF, Zhou XP (2014) Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading. Int J rock Mech Min Sci 69:59\u201366","journal-title":"Int J rock Mech Min Sci"},{"key":"6217_CR13","doi-asserted-by":"crossref","DOI":"10.1002\/9783433600122","volume-title":"Hardrock tunnel boring machines","author":"B Maidl","year":"2008","unstructured":"Maidl B, Schmid L, Ritz W, Herrenknecht M (2008) Hardrock tunnel boring machines. Wiley, New Jersey"},{"key":"6217_CR14","first-page":"22","volume":"12","author":"IW Farmer","year":"1980","unstructured":"Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunnels Tunn 12:22\u201325","journal-title":"Tunnels Tunn"},{"key":"6217_CR15","unstructured":"Rostami J (1997) Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure"},{"issue":"3","key":"6217_CR16","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/0148-9062(82)91151-2","volume":"19","author":"RA Snowdon","year":"1982","unstructured":"Snowdon RA, Ryley MD, Temporal J (1982) A study of disc cutting in selected British rocks. Int J Rock Mech Min Sci Geomech Abst 19(3):107\u2013121","journal-title":"Int J Rock Mech Min Sci Geomech Abst"},{"key":"6217_CR17","unstructured":"Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machines"},{"key":"6217_CR18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ijrmms.2008.03.003","volume":"46","author":"Q-M Gong","year":"2009","unstructured":"Gong Q-M, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46:8\u201318","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR19","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.tust.2011.04.004","volume":"26","author":"J Hassanpour","year":"2011","unstructured":"Hassanpour J, Rostami J, Zhao J (2011) A new hard rock TBM performance prediction model for project planning. Tunn Undergr Sp Technol 26:595\u2013603","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR20","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.ijrmms.2014.09.012","volume":"72","author":"S Mahdevari","year":"2014","unstructured":"Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214\u2013229","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR21","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.tust.2004.02.128","volume":"19","author":"AG Benardos","year":"2004","unstructured":"Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Sp Technol 19:597\u2013605","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR22","doi-asserted-by":"crossref","unstructured":"Simoes MG, Kim T (2006) Fuzzy modeling approaches for the prediction of machine utilization in hard rock tunnel boring machines. In: Conference Record of the 2006 IEEE Industry Applications Conference 41st IAS Annual Meeting, pp 947\u2013954","DOI":"10.1109\/IAS.2006.256639"},{"key":"6217_CR23","first-page":"727","volume":"20","author":"G Javad","year":"2010","unstructured":"Javad G, NARGES T, (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Technol 20:727\u2013733","journal-title":"Min Sci Technol"},{"key":"6217_CR24","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/978-3-642-40849-6_40","volume-title":"International conference on intelligent robotics and applications","author":"C Shao","year":"2013","unstructured":"Shao C, Li X, Su H (2013) Performance prediction of hard rock TBM based on extreme learning machine. In: Lee J, Lee MC, Liu H, Ryu JH (eds) International conference on intelligent robotics and applications. Springer, Berlin, pp 409\u2013416"},{"key":"6217_CR25","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s12517-011-0415-3","volume":"6","author":"SR Torabi","year":"2013","unstructured":"Torabi SR, Shirazi H, Hajali H, Monjezi M (2013) Study of the influence of geotechnical parameters on the TBM performance in Tehran-Shomal highway project using ANN and SPSS. Arab J Geosci 6:1215\u20131227","journal-title":"Arab J Geosci"},{"key":"6217_CR26","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1007\/s10064-018-1349-8","volume":"78","author":"M Koopialipoor","year":"2018","unstructured":"Koopialipoor M, Nikouei SS, Marto A et al (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bull Eng Geol Environ 78:3799\u20133813","journal-title":"Bull Eng Geol Environ"},{"key":"6217_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrmge.2019.01.002","author":"DJ Armaghani","year":"2019","unstructured":"Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng. https:\/\/doi.org\/10.1016\/j.jrmge.2019.01.002","journal-title":"J Rock Mech Geotech Eng"},{"key":"6217_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01217-2","author":"H Yang","year":"2020","unstructured":"Yang H, Wang Z, Song K (2020) A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01217-2","journal-title":"Eng Comput"},{"key":"6217_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.undsp.2020.05.008","author":"J Zhou","year":"2020","unstructured":"Zhou J, Qiu Y, Zhu S et al (2020) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Undergr Sp. https:\/\/doi.org\/10.1016\/j.undsp.2020.05.008","journal-title":"Undergr Sp"},{"key":"6217_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsf.2020.09.020","author":"J Zhou","year":"2020","unstructured":"Zhou J, Qiu Y, Armaghani DJ et al (2020) Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geosci Front. https:\/\/doi.org\/10.1016\/j.gsf.2020.09.020","journal-title":"Geosci Front"},{"key":"6217_CR31","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/S0148-9062(99)00025-X","volume":"36","author":"MA Grima","year":"1999","unstructured":"Grima MA, Verhoef PNW (1999) Forecasting rock trencher performance using fuzzy logic. Int J Rock Mech Min Sci 36:413\u2013432","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR32","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ijrmms.2011.02.013","volume":"48","author":"S Yagiz","year":"2011","unstructured":"Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427\u2013433","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR33","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1504\/IJMME.2013.053172","volume":"4","author":"A Salimi","year":"2013","unstructured":"Salimi A, Esmaeili M (2013) Utilising of linear and non-linear prediction tools for evaluation of penetration rate of tunnel boring machine in hard rock condition. Int J Min Miner Eng 4:249\u2013264","journal-title":"Int J Min Miner Eng"},{"key":"6217_CR34","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10064-016-0969-0","volume":"77","author":"A Salimi","year":"2018","unstructured":"Salimi A, Faradonbeh RS, Monjezi M, Moormann C (2018) TBM performance estimation using a classification and regression tree (CART) technique. Bull Eng Geol Environ 77:429\u2013440","journal-title":"Bull Eng Geol Environ"},{"key":"6217_CR35","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1007\/s10064-019-01626-8","volume":"79","author":"J Zhou","year":"2020","unstructured":"Zhou J, Yazdani Bejarbaneh B, Jahed Armaghani D, Tahir MM (2020) Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bull Eng Geol Environ 79:2069\u20132084. https:\/\/doi.org\/10.1007\/s10064-019-01626-8","journal-title":"Bull Eng Geol Environ"},{"key":"6217_CR36","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10064-013-0497-0","volume":"73","author":"E Ghasemi","year":"2014","unstructured":"Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Environ 73:23\u201335","journal-title":"Bull Eng Geol Environ"},{"key":"6217_CR37","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.tust.2016.12.009","volume":"63","author":"DJ Armaghani","year":"2017","unstructured":"Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29\u201343. https:\/\/doi.org\/10.1016\/j.tust.2016.12.009","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR38","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.tust.2016.05.009","volume":"58","author":"A Salimi","year":"2016","unstructured":"Salimi A, Rostami J, Moormann C, Delisio A (2016) Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunn Undergr Sp Technol 58:236\u2013246","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR39","first-page":"159","volume":"6","author":"H Fattahi","year":"2016","unstructured":"Fattahi H (2016) Adaptive neuro fuzzy inference system based on Fuzzy C-means clustering algorithm, a technique for estimation of Tbm penetration rate. Iran Univ Sci Technol 6:159\u2013171","journal-title":"Iran Univ Sci Technol"},{"key":"6217_CR40","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1515\/eng-2017-0012","volume":"7","author":"VT Minh","year":"2017","unstructured":"Minh VT, Katushin D, Antonov M, Veinthal R (2017) Regression models and fuzzy logic prediction of TBM penetration rate. Open Eng 7:60\u201368","journal-title":"Open Eng"},{"key":"6217_CR41","doi-asserted-by":"crossref","first-page":"4650","DOI":"10.3390\/app9214650","volume":"9","author":"M Hajihassani","year":"2019","unstructured":"Hajihassani M, Abdullah SS, Asteris PG, Armaghani DJ (2019) A gene expression programming model for predicting tunnel convergence. Appl Sci 9:4650","journal-title":"Appl Sci"},{"issue":"13","key":"6217_CR42","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1080\/15376494.2018.1430874","volume":"26","author":"PG Asteris","year":"2018","unstructured":"Asteris PG, Nozhati S, Nikoo M et al (2018) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146\u20131153","journal-title":"Mech Adv Mater Struct"},{"key":"6217_CR43","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1016\/j.prostr.2019.08.123","volume":"17","author":"DJ Armaghani","year":"2019","unstructured":"Armaghani DJ, Hatzigeorgiou GD, Karamani C et al (2019) Soft computing-based techniques for concrete beams shear strength. Procedia Struct Integr 17:924\u2013933","journal-title":"Procedia Struct Integr"},{"key":"6217_CR44","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 Concr 24:329\u2013345","journal-title":"Comput Concr"},{"key":"6217_CR45","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1016\/j.eswa.2009.07.046","volume":"37","author":"S Yagiz","year":"2010","unstructured":"Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37:2265\u20132272","journal-title":"Expert Syst Appl"},{"key":"6217_CR46","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1016\/j.eswa.2008.06.051","volume":"36","author":"A Khajeh","year":"2009","unstructured":"Khajeh A, Modarress H, Rezaee B (2009) Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Syst Appl 36:5728\u20135732","journal-title":"Expert Syst Appl"},{"key":"6217_CR47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.enggeo.2018.03.023","volume":"239","author":"HQ Yang","year":"2018","unstructured":"Yang HQ, Xing SG, Wang Q, Li Z (2018) Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides. Eng Geol 239:119\u2013125","journal-title":"Eng Geol"},{"key":"6217_CR48","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.enggeo.2014.11.016","volume":"185","author":"HQ Yang","year":"2015","unstructured":"Yang HQ, Lan YF, Lu L, Zhou XP (2015) A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion. Eng Geol 185:20\u201332","journal-title":"Eng Geol"},{"key":"6217_CR49","doi-asserted-by":"crossref","first-page":"121878","DOI":"10.1016\/j.conbuildmat.2020.121878","volume":"271","author":"J Huang","year":"2021","unstructured":"Huang J, Zhang J, Ren J, Chen H (2021) Anti-rutting performance of the damping asphalt mixtures (DAMs) made with a high content of asphalt rubber (AR). Constr Build Mater 271:121878","journal-title":"Constr Build Mater"},{"key":"6217_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1617\/s11527-021-01647-4","volume":"54","author":"J Huang","year":"2021","unstructured":"Huang J, Wang Q-A (2021) Influence of crumb rubber particle sizes on rutting, low temperature cracking, fracture, and bond strength properties of asphalt binder. Mater Struct 54:1\u201315","journal-title":"Mater Struct"},{"key":"6217_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01305-x","author":"J Huang","year":"2021","unstructured":"Huang J, Sun Y, Zhang J (2021) Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01305-x","journal-title":"Eng Comput"},{"key":"6217_CR52","first-page":"469","volume":"24","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Armaghani DJ, Hatzigeorgiou GD et al (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24:469\u2013488","journal-title":"Comput Concr"},{"key":"6217_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00808-y","author":"P Sarir","year":"2019","unstructured":"Sarir P, Chen J, Asteris PG et al (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00808-y","journal-title":"Eng Comput"},{"key":"6217_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00752-x","author":"W Chen","year":"2019","unstructured":"Chen W, Sarir P, Bui X-N et al (2019) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-019-00752-x","journal-title":"Eng Comput"},{"key":"6217_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-03965-1","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-03965-1","journal-title":"Neural Comput Appl"},{"key":"6217_CR56","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s12145-014-0144-8","volume":"8","author":"M Najafzadeh","year":"2015","unstructured":"Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inf 8:187\u2013196","journal-title":"Earth Sci Inf"},{"key":"6217_CR57","doi-asserted-by":"crossref","first-page":"6016003","DOI":"10.1061\/(ASCE)PS.1949-1204.0000249","volume":"8","author":"M Najafzadeh","year":"2016","unstructured":"Najafzadeh M, Bonakdari H (2016) Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers. J Pipeline Syst Eng Pract 8:6016003","journal-title":"J Pipeline Syst Eng Pract"},{"key":"6217_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04663-2","author":"PG Asteris","year":"2019","unstructured":"Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04663-2","journal-title":"Neural Comput Appl"},{"key":"6217_CR59","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.prostr.2019.08.122","volume":"17","author":"M Apostolopoulou","year":"2019","unstructured":"Apostolopoulou M, Armaghani DJ, Bakolas A et al (2019) Compressive strength of natural hydraulic lime mortars using soft computing techniques. Procedia Struct Integr 17:914\u2013923","journal-title":"Procedia Struct Integr"},{"key":"6217_CR60","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/72.159060","volume":"3","author":"J-SR Jang","year":"1992","unstructured":"Jang J-SR (1992) Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Netw 3:714\u2013723","journal-title":"IEEE Trans Neural Netw"},{"key":"6217_CR61","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","volume":"42","author":"J-SR Jang","year":"1997","unstructured":"Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans Automat Contr 42:1482\u20131484","journal-title":"IEEE Trans Automat Contr"},{"key":"6217_CR62","volume-title":"Neurofuzzy adaptive modelling and control","author":"M Brown","year":"1994","unstructured":"Brown M, Harris CJ (1994) Neurofuzzy adaptive modelling and control. Prentice Hall, New Jersey"},{"key":"6217_CR63","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.eswa.2005.04.008","volume":"29","author":"AI Nuno","year":"2005","unstructured":"Nuno AI, Arcay B, Cotos JM, Varela J (2005) Optimisation of fishing predictions by means of artificial neural networks, anfis, functional networks and remote sensing images. Expert Syst Appl 29:356\u2013363","journal-title":"Expert Syst Appl"},{"key":"6217_CR64","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.catena.2017.05.034","volume":"157","author":"W Chen","year":"2017","unstructured":"Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310\u2013324","journal-title":"CATENA"},{"key":"6217_CR65","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cageo.2011.10.031","volume":"45","author":"DT Bui","year":"2012","unstructured":"Bui DT, Pradhan B, Lofman O et al (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199\u2013211","journal-title":"Comput Geosci"},{"key":"6217_CR66","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.catena.2015.07.020","volume":"135","author":"A Dehnavi","year":"2015","unstructured":"Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122\u2013148","journal-title":"CATENA"},{"key":"6217_CR67","first-page":"43","volume":"13","author":"AG Ivakhnenko","year":"1968","unstructured":"Ivakhnenko AG (1968) The group method of data of handling; a rival of the method of stochastic approximation. Sov Autom Control 13:43\u201355","journal-title":"Sov Autom Control"},{"key":"6217_CR68","volume-title":"Charles Darwin\u2019s natural selection: being the second part of his big species book written from 1856 to 1858","author":"C Darwin","year":"1987","unstructured":"Darwin C (1987) Charles Darwin\u2019s natural selection: being the second part of his big species book written from 1856 to 1858. Cambridge University Press, Cambridge"},{"key":"6217_CR69","volume-title":"Self-organizing methods in modeling: GMDH type algorithms","author":"SJ Farlow","year":"1984","unstructured":"Farlow SJ (1984) Self-organizing methods in modeling: GMDH type algorithms. CRC Press, London"},{"key":"6217_CR70","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.engappai.2008.11.005","volume":"22","author":"A Jamali","year":"2009","unstructured":"Jamali A, Nariman-Zadeh N, Darvizeh A et al (2009) Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng Appl Artif Intell 22:676\u2013687","journal-title":"Eng Appl Artif Intell"},{"key":"6217_CR71","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1243\/09544050360673161","volume":"217","author":"N Nariman-Zadeh","year":"2003","unstructured":"Nariman-Zadeh N, Darvizeh A, Ahmad-Zadeh GR (2003) Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc Inst Mech Eng Part B J Eng Manuf 217:779\u2013790","journal-title":"Proc Inst Mech Eng Part B J Eng Manuf"},{"key":"6217_CR72","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1243\/09544054JEM1746","volume":"224","author":"F Jolai","year":"2010","unstructured":"Jolai F, Sangari MS, Babaie M (2010) Pareto simulated annealing and colonial competitive algorithm to solve an offline scheduling problem with rejection. Proc Inst Mech Eng Part B J Eng Manuf 224:1119\u20131131","journal-title":"Proc Inst Mech Eng Part B J Eng Manuf"},{"key":"6217_CR73","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s00419-011-0548-6","volume":"82","author":"A Nicknam","year":"2012","unstructured":"Nicknam A, Hosseini MH (2012) Structural damage localization and evaluation based on modal data via a new evolutionary algorithm. Arch Appl Mech 82:191\u2013203","journal-title":"Arch Appl Mech"},{"key":"6217_CR74","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s10064-014-0657-x","volume":"74","author":"M Hajihassani","year":"2014","unstructured":"Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873\u2013886. https:\/\/doi.org\/10.1007\/s10064-014-0657-x","journal-title":"Bull Eng Geol Environ"},{"key":"6217_CR75","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ijrmms.2016.03.018","volume":"85","author":"D Jahed Armaghani","year":"2016","unstructured":"Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174\u2013186. https:\/\/doi.org\/10.1016\/j.ijrmms.2016.03.018","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR76","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary computation, CEC 2007, IEEE, pp 4661\u20134667","DOI":"10.1109\/CEC.2007.4425083"},{"key":"6217_CR77","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s12665-017-6726-2","volume":"76","author":"M Khandelwal","year":"2017","unstructured":"Khandelwal M, Mahdiyar A, Armaghani DJ et al (2017) An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Environ Earth Sci 76:399. https:\/\/doi.org\/10.1007\/s12665-017-6726-2","journal-title":"Environ Earth Sci"},{"key":"6217_CR78","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.3233\/IFS-141443","volume":"29","author":"Y Qin","year":"2015","unstructured":"Qin Y, Langari R, Gu L (2015) A new modeling algorithm based on ANFIS and GMDH. J Intell Fuzzy Syst 29:1321\u20131329","journal-title":"J Intell Fuzzy Syst"},{"key":"6217_CR79","unstructured":"Sundaram M (2007) The effects of ground conditions on TBM performance in tunnel excavation\u2013a case history. In: Proceedings of the 10th Australia New Zealand Conference on Geomechanics, Queensland, Australia pp. 21\u201324"},{"key":"6217_CR80","unstructured":"Sundaram NM, Rafek AG, Komoo I (1998) The influence of rock mass properties in the assessment of TBM performance. In: Proceedings of the 8th IAEG Congress, Vancouver, British Columbia, Canada. pp. 3553\u20133559"},{"key":"6217_CR81","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/S1365-1609(02)00069-2","volume":"39","author":"M Sapigni","year":"2002","unstructured":"Sapigni M, Berti M, Bethaz E et al (2002) TBM performance estimation using rock mass classifications. Int J Rock Mech Min Sci 39:771\u2013788","journal-title":"Int J Rock Mech Min Sci"},{"key":"6217_CR82","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.tust.2012.02.012","volume":"30","author":"E Farrokh","year":"2012","unstructured":"Farrokh E, Rostami J, Laughton C (2012) Study of various models for estimation of penetration rate of hard rock TBMs. Tunn Undergr Sp Technol 30:110\u2013123","journal-title":"Tunn Undergr Sp Technol"},{"key":"6217_CR83","unstructured":"Ulusay R, Hudson JA (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974\u20132006. Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628"},{"key":"6217_CR84","doi-asserted-by":"crossref","first-page":"7286","DOI":"10.1016\/j.jfranklin.2020.04.024","volume":"357","author":"H Tao","year":"2020","unstructured":"Tao H, Wang P, Chen Y et al (2020) An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks. J Franklin Inst 357:7286\u20137307","journal-title":"J Franklin Inst"},{"key":"6217_CR85","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1049\/iet-cta.2019.1316","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong X, He S, Stojanovic V (2020) Robust fault detection filter design for a class of discrete-time conic-type non-linear Markov jump systems with jump fault signals. IET Control Theory Appl 14:1912\u20131919","journal-title":"IET Control Theory Appl"},{"key":"6217_CR86","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-2913-x","author":"X Zhang","year":"2020","unstructured":"Zhang X, Shuping H, Stojanovic V et al (2020) Finite-time asynchronous dissipative filtering of conic-type nonlinear markov jump systems. Sci CHINA Inf Sci. https:\/\/doi.org\/10.1007\/s11432-020-2913-x","journal-title":"Sci CHINA Inf Sci"},{"key":"6217_CR87","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.3390\/math8091528","volume":"8","author":"L Zhou","year":"2020","unstructured":"Zhou L, Tao H, Paszke W et al (2020) PD-type iterative learning control for uncertain spatially interconnected systems. Mathematics 8:1528","journal-title":"Mathematics"},{"key":"6217_CR88","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.neucom.2020.08.063","volume":"417","author":"Z Chen","year":"2020","unstructured":"Chen Z, Zhang B, Stojanovic V et al (2020) Event-based fuzzy control for TS fuzzy networked systems with various data missing. Neurocomputing 417:322\u2013332","journal-title":"Neurocomputing"},{"key":"6217_CR89","doi-asserted-by":"crossref","unstructured":"Tahmassebi A, Gandomi AH, Meyer-Baese A (2018) A Pareto front based evolutionary model for airfoil self-noise prediction. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 1\u20138","DOI":"10.1109\/CEC.2018.8477987"},{"key":"6217_CR90","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01207-4","author":"J Huang","year":"2020","unstructured":"Huang J, Asteris PG, Pasha SMK et al (2020) A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01207-4","journal-title":"Eng Comput"},{"key":"6217_CR91","doi-asserted-by":"publisher","first-page":"109","DOI":"10.3390\/info12030109","volume":"12","author":"I Rahimi","year":"2021","unstructured":"Rahimi I, Gandomi AH, Chen F (2021) Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases. Information 12:109. https:\/\/doi.org\/10.3390\/info12030109","journal-title":"Information"},{"key":"6217_CR92","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05214-w","author":"H Ly","year":"2020","unstructured":"Ly H, Pham BT, Le LM et al (2020) Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05214-w","journal-title":"Neural Comput Appl"},{"key":"6217_CR93","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-05626-3","author":"PG Asteris","year":"2021","unstructured":"Asteris PG, Cavaleri L, Ly H-B, Pham BT (2021) Surrogate models for the compressive strength mapping of cement mortar materials. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-021-05626-3","journal-title":"Soft Comput"},{"key":"6217_CR94","doi-asserted-by":"crossref","first-page":"908","DOI":"10.3390\/app11030908","volume":"11","author":"J Zeng","year":"2021","unstructured":"Zeng J, Asteris PG, Mamou AP et al (2021) The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand. Appl Sci 11:908","journal-title":"Appl Sci"},{"key":"6217_CR95","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01272-9","author":"H Zhang","year":"2021","unstructured":"Zhang H, Nguyen H, Bui X-N et al (2021) A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01272-9","journal-title":"Eng Comput"},{"key":"6217_CR96","doi-asserted-by":"crossref","first-page":"106831","DOI":"10.1016\/j.asoc.2020.106831","volume":"97","author":"C Bai","year":"2020","unstructured":"Bai C, Nguyen H, Asteris PG et al (2020) A refreshing view of soft computing models for predicting the deflection of reinforced concrete beams. Appl Soft Comput 97:106831","journal-title":"Appl Soft Comput"},{"key":"6217_CR97","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s11771-021-4619-8","volume":"28","author":"S Wang","year":"2021","unstructured":"Wang S, Zhou J, Li C et al (2021) Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. J Cent South Univ 28:527\u2013542","journal-title":"J Cent South Univ"},{"key":"6217_CR98","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-021-09826-4","author":"C Yu","year":"2021","unstructured":"Yu C, Koopialipoor M, Murlidhar BR et al (2021) Optimal ELM\u2013Harris Hawks optimization and ELM\u2013Grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-021-09826-4","journal-title":"Nat Resour Res"},{"key":"6217_CR99","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.3390\/app10061904","volume":"10","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Asteris PG, Fatemi SA et al (2020) On the Use of Neuro-Swarm System to Forecast the Pile Settlement. Appl Sci 10:1904","journal-title":"Appl Sci"},{"key":"6217_CR100","first-page":"191","volume":"20","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Mirzaei F, Shariati M et al (2020) Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech Eng 20:191\u2013205","journal-title":"Geomech Eng"},{"key":"6217_CR101","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-00997-x","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Kumar D, Samui P et al (2020) A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-00997-x","journal-title":"Eng Comput"},{"key":"6217_CR102","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.3390\/app9183715","volume":"9","author":"H Xu","year":"2019","unstructured":"Xu H, Zhou J, Asteris PG et al (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9:3715","journal-title":"Appl Sci"},{"key":"6217_CR103","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1016\/j.asoc.2012.10.009","volume":"13","author":"MA Ahmadi","year":"2013","unstructured":"Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085\u20131098","journal-title":"Appl Soft Comput"},{"issue":"8","key":"6217_CR104","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1016\/j.measurement.2013.04.077","volume":"46","author":"H Taghavifar","year":"2013","unstructured":"Taghavifar H, Mardani A, Taghavifar L (2013) A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility. Measurement 46(8):2288\u20132299","journal-title":"Measurement"},{"key":"6217_CR105","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s00366-015-0408-z","volume":"32","author":"DJ Armaghani","year":"2016","unstructured":"Armaghani DJ, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32:155\u2013171. https:\/\/doi.org\/10.1007\/s00366-015-0408-z","journal-title":"Eng Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06217-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06217-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06217-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T18:12:55Z","timestamp":1635963175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06217-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,27]]},"references-count":105,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6217"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06217-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,27]]},"assertion":[{"value":"20 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm that this article content has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}