{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T04:25:05Z","timestamp":1777782305818,"version":"3.51.4"},"reference-count":93,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"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\/s00366-020-01225-2","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T03:03:04Z","timestamp":1609815784000},"page":"3811-3827","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance"],"prefix":"10.1007","volume":"38","author":[{"given":"Jie","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bishwajit","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Salih","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danial Jahed","family":"Armaghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edy Tonnizam","family":"Mohamad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"key":"1225_CR1","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s00366-017-0526-x","volume":"34","author":"DJ Armaghani","year":"2018","unstructured":"Armaghani DJ, Faradonbeh RS, Momeni E et al (2018) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Eng Comput 34:129\u2013141","journal-title":"Eng Comput"},{"key":"1225_CR2","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":"1225_CR3","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s00603-015-0796-9","volume":"49","author":"H Yang","year":"2016","unstructured":"Yang H, Wang H, Zhou X (2016) Analysis on the rock\u2013cutter interaction mechanism during the TBM tunneling process. Rock Mech Rock Eng 49:1073\u20131090","journal-title":"Rock Mech Rock Eng"},{"key":"1225_CR4","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":"1225_CR5","first-page":"361","volume-title":"International journal of rock mechanics and mining sciences & geomechanics abstracts","author":"FF Roxborough","year":"1975","unstructured":"Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. International journal of rock mechanics and mining sciences & geomechanics abstracts. Elsevier, Amsterdam, pp 361\u2013366"},{"key":"1225_CR6","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":"1225_CR7","unstructured":"Bamford WF (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Proceedings of the 5th Australian Tunneling Conference, Melbourne, pp 9\u201322"},{"key":"1225_CR8","unstructured":"Sato K, Gong F, Itakura K (1991) Prediction of disc cutter performance using a circular rock cutting ring. In: Proceedings 1st International Mine Mechanization and Automation Symposium, Colorado School of Mines, Golden, Colorado, USA"},{"key":"1225_CR9","unstructured":"Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBM. In: Bowerman LD et al (eds) Proceedings of RETC, Boston, MA, pp 793\u2013809"},{"key":"1225_CR10","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 (Ph.D. Thesis). Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA, p 289"},{"key":"1225_CR11","first-page":"202","volume-title":"Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate","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 Netherlands, Delft, p 202"},{"key":"1225_CR12","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s00603-017-1395-8","volume":"51","author":"H Yang","year":"2018","unstructured":"Yang H, Liu J, Liu B (2018) Investigation on the cracking character of jointed rock mass beneath TBM disc cutter. Rock Mech Rock Eng 51:1263\u20131277","journal-title":"Rock Mech Rock Eng"},{"key":"1225_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105822","author":"EY Bejarbaneh","year":"2019","unstructured":"Bejarbaneh EY, Bagheri A, Bejarbaneh BY, Buyamin SCS (2019) A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2019.105822","journal-title":"Appl Soft Comput"},{"key":"1225_CR14","doi-asserted-by":"crossref","first-page":"4016003","DOI":"10.1061\/(ASCE)CP.1943-5487.0000553","volume":"30","author":"J Zhou","year":"2016","unstructured":"Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: Comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003","journal-title":"J Comput Civ Eng"},{"issue":"1","key":"1225_CR15","doi-asserted-by":"publisher","first-page":"237","DOI":"10.2174\/1874836802014010237","volume":"14","author":"Y Abdi","year":"2020","unstructured":"Abdi Y, Momeni E, Khabir RR (2020) A Reliable PSO-based ANN Approach for Predicting Unconfined Compressive Strength of Sandstones. Open Constr Build Technol J 14(1):237\u2013249. https:\/\/doi.org\/10.2174\/1874836802014010237","journal-title":"Open Constr Build Technol J"},{"key":"1225_CR16","doi-asserted-by":"publisher","first-page":"298","DOI":"10.2174\/1874836802014010298","volume":"14","author":"BR Murlidhar","year":"2020","unstructured":"Murlidhar BR, Armaghani DJ, Mohamad ET (2020) Intelligence prediction of some selected environmental issues of blasting: a review. Open Constr Build Technol J 14:298\u2013308. https:\/\/doi.org\/10.2174\/1874836802014010298","journal-title":"Open Constr Build Technol J"},{"key":"1225_CR17","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.3390\/app9081621","volume":"9","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Wei H et al (2019) random forests and cubist algorithms for predicting shear strengths of Rockfill materials. Appl Sci 9:1621","journal-title":"Appl Sci"},{"key":"1225_CR18","doi-asserted-by":"crossref","first-page":"4019024","DOI":"10.1061\/(ASCE)CF.1943-5509.0001292","volume":"33","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Wang M et al (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33:4019024","journal-title":"J Perform Constr Facil"},{"key":"1225_CR19","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2020.013280","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Douvika MG, Karamani CA, Skentou AD, Chlichlia K, Cavaleri L, Daras T, Armaghani DJ, Zaoutis TE (2020) A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. Comput Model Eng Sci. https:\/\/doi.org\/10.32604\/cmes.2020.013280","journal-title":"Comput Model Eng Sci"},{"key":"1225_CR20","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.tra.2020.04.013","volume":"136","author":"M Aghaabbasi","year":"2020","unstructured":"Aghaabbasi M, Shekari ZA, Shah MZ et al (2020) Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques. Transp Res Part A Policy Pract 136:262\u2013281","journal-title":"Transp Res Part A Policy Pract"},{"key":"1225_CR21","doi-asserted-by":"publisher","first-page":"8255","DOI":"10.1007\/s13369-020-04683-4","volume":"45","author":"E Momeni","year":"2020","unstructured":"Momeni E, Dowlatshahi MB, Omidinasab F et al (2020) Gaussian process regression technique to estimate the pile bearing capacity. Arab J Sci Eng 45:8255\u20138267. https:\/\/doi.org\/10.1007\/s13369-020-04683-4","journal-title":"Arab J Sci Eng"},{"key":"1225_CR22","doi-asserted-by":"publisher","unstructured":"Momeni E, Yarivand A, Dowlatshahi MB, Armaghani DJ (2020) An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transp Geotech:100446. https:\/\/doi.org\/10.1016\/j.trgeo.2020.100446","DOI":"10.1016\/j.trgeo.2020.100446"},{"key":"1225_CR23","doi-asserted-by":"crossref","unstructured":"Marto A, Hajihassani M, Momeni E (2014) Bearing capacity of shallow foundation\u2019s prediction through hybrid artificial neural networks. In: Applied mechanics and materials. Trans Tech Publ, pp 681\u2013686","DOI":"10.4028\/www.scientific.net\/AMM.567.681"},{"key":"1225_CR24","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1680\/jgeen.14.00177","volume":"168","author":"E Momeni","year":"2015","unstructured":"Momeni E, Nazir R, Armaghani DJ, Sohaie H (2015) Bearing capacity of precast thin-walled foundation in sand. Proc Inst Civ Eng Eng 168:539\u2013550","journal-title":"Proc Inst Civ Eng Eng"},{"key":"1225_CR25","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ijrmms.2011.12.007","volume":"51","author":"M Singh","year":"2012","unstructured":"Singh M, Singh B (2012) Modified Mohr-Coulomb criterion for non-linear triaxial and polyaxial strength of jointed rocks. Int J Rock Mech Min 51:43\u201352","journal-title":"Int J Rock Mech Min"},{"key":"1225_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2456-8","author":"ANK Abad","year":"2016","unstructured":"Abad ANK, SV, Yilmaz M, Jahed Armaghani D, Tugrul A, (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-016-2456-8","journal-title":"Neural Comput Appl"},{"key":"1225_CR27","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s10064-016-0983-2","volume":"77","author":"BY Bejarbaneh","year":"2018","unstructured":"Bejarbaneh BY, Bejarbaneh EY, Fahimifar A et al (2018) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ 77:345\u2013361","journal-title":"Bull Eng Geol Environ"},{"key":"1225_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09515-3","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. https:\/\/doi.org\/10.1007\/s11053-019-09515-3","journal-title":"Nat Resour Res"},{"key":"1225_CR29","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.resourpol.2014.01.003","volume":"39","author":"H Dehghani","year":"2014","unstructured":"Dehghani H, Ataee-pour M, Esfahanipour A (2014) Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree. Resour Policy 39:124\u2013133","journal-title":"Resour Policy"},{"key":"1225_CR30","doi-asserted-by":"crossref","first-page":"106167","DOI":"10.1016\/j.cemconres.2020.106167","volume":"136","author":"M Apostolopoulou","year":"2020","unstructured":"Apostolopoulou M, Asteris PG, Armaghani DJ et al (2020) Mapping and holistic design of natural hydraulic lime mortars. Cem Concr Res 136:106167","journal-title":"Cem Concr Res"},{"key":"1225_CR31","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":"1225_CR32","first-page":"1","volume":"1","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Momeni E, Asteris P (2020) Application of group method of data handling technique in assessing deformation of rock mass. Metaheuristic Comput Appl 1:1\u201318","journal-title":"Metaheuristic Comput Appl"},{"key":"1225_CR33","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.3390\/s17061344","volume":"17","author":"P Asteris","year":"2017","unstructured":"Asteris P, Roussis P, Douvika M (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17:1344","journal-title":"Sensors"},{"key":"1225_CR34","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":"1225_CR35","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ssci.2019.05.046","volume":"118","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li E, Yang S et al (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505\u2013518","journal-title":"Saf Sci"},{"key":"1225_CR36","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1842-3","volume":"79","author":"J Zhou","year":"2015","unstructured":"Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79:291\u2013316","journal-title":"Nat Hazards"},{"key":"1225_CR37","doi-asserted-by":"publisher","first-page":"106390","DOI":"10.1016\/j.soildyn.2020.106390","volume":"139","author":"J Zhou","year":"2020","unstructured":"Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https:\/\/doi.org\/10.1016\/j.soildyn.2020.106390","journal-title":"Soil Dyn Earthq Eng"},{"key":"1225_CR38","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.tust.2018.08.029","volume":"81","author":"J Zhou","year":"2018","unstructured":"Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: State-of-the-art literature review. Tunn Undergr Sp Technol 81:632\u2013659","journal-title":"Tunn Undergr Sp Technol"},{"key":"1225_CR39","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.3390\/su12062229","volume":"12","author":"D Jahed Armaghani","year":"2020","unstructured":"Jahed Armaghani D, Asteris PG, Askarian B et al (2020) Examining hybrid and single SVM models with different kernels to predict rock brittleness. Sustainability 12:2229","journal-title":"Sustainability"},{"key":"1225_CR40","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1007\/s12613-019-1885-7","volume":"26","author":"X Zhao","year":"2019","unstructured":"Zhao X, Fourie A, Qi C (2019) An analytical solution for evaluating the safety of an exposed face in a paste backfill stope incorporating the arching phenomenon. Int J Miner Metall Mater 26:1206\u20131216","journal-title":"Int J Miner Metall Mater"},{"key":"1225_CR41","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s12613-019-1937-z","volume":"27","author":"C Qi","year":"2020","unstructured":"Qi C (2020) Big data management in the mining industry. Int J Miner Metall Mater 27:131\u2013139","journal-title":"Int J Miner Metall Mater"},{"key":"1225_CR42","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1007\/s12613-020-2006-3","volume":"27","author":"X Zhao","year":"2020","unstructured":"Zhao X, Fourie A, Veenstra R, Qi C (2020) Safety of barricades in cemented paste-backfilled stopes. Int J Miner Metall Mater 27:1054\u20131064","journal-title":"Int J Miner Metall Mater"},{"key":"1225_CR43","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s12613-020-2004-5","volume":"27","author":"X Zhao","year":"2020","unstructured":"Zhao X, Fourie A, Qi C (2020) Mechanics and safety issues in tailing-based backfill: A review. Int J Miner Metall Mater 27:1165\u20131178","journal-title":"Int J Miner Metall Mater"},{"key":"1225_CR44","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1007\/s10706-018-0570-3","volume":"36","author":"S Yagiz","year":"2018","unstructured":"Yagiz S, Ghasemi E, Adoko AC (2018) Prediction of rock brittleness using genetic algorithm and particle swarm optimization techniques. Geotech Geol Eng 36:3767\u20133777","journal-title":"Geotech Geol Eng"},{"key":"1225_CR45","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.compgeo.2018.03.012","volume":"100","author":"E Momeni","year":"2018","unstructured":"Momeni E, Poormoosavian M, Mahdiyar A, Fakher A (2018) Evaluating random set technique for reliability analysis of deep urban excavation using Monte Carlo simulation. Comput Geotech 100:203\u2013215","journal-title":"Comput Geotech"},{"key":"1225_CR46","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":"1225_CR47","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":"1225_CR48","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":"1225_CR49","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":"1225_CR50","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":"1225_CR51","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":"1225_CR52","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":"1225_CR53","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":"1225_CR54","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":"1225_CR55","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":"1225_CR56","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":"1225_CR57","doi-asserted-by":"crossref","first-page":"104015","DOI":"10.1016\/j.engappai.2020.104015","volume":"97","author":"J Zhou","year":"2020","unstructured":"Zhou J, Qiu Y, Zhu S et al (2020) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015","journal-title":"Eng Appl Artif Intell"},{"key":"1225_CR58","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":"1225_CR59","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":"1225_CR60","unstructured":"Eftekhari M, Baghbanan A, Bayati M (2010) Predicting penetration rate of a tunnel boring machine using artificial neural network. In: Proceedings of the ISRM International Symposium-6th Asian Rock Mechanics Symposium. International Society for Rock Mechanics, New Delhi, India, 23\u201327 October 2010"},{"key":"1225_CR61","unstructured":"Gholami M, Shahriar K, Sharifzadeh M, Hamidi JK (2012) A comparison of artificial neural network and multiple regression analysis in TBM performance prediction. In: Proceedings of the ISRM Regional Symposium-7th Asian Rock Mechanics Symposium. International Society for Rock Mechanics, Seoul, Korea, 15\u201319 October 2012"},{"key":"1225_CR62","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":"1225_CR63","unstructured":"Oraee K, Khorami MT, Hosseini N (2012) Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (ANFIS). In: Proceeding of SME Annual Meeting & Exhibit, From the Mine to the Market, Now It\u2019s Global, Seattle, pp 297\u2013302"},{"key":"1225_CR64","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.enggeo.2017.06.014","volume":"226","author":"AC Adoko","year":"2017","unstructured":"Adoko AC, Gokceoglu C, Yagiz S (2017) Bayesian prediction of TBM penetration rate in rock mass. Eng Geol 226:245\u2013256","journal-title":"Eng Geol"},{"key":"1225_CR65","doi-asserted-by":"publisher","DOI":"10.1007\/s10064-019-01538-7","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor M, Tootoonchi H, Jahed Armaghani D et al (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Environ. https:\/\/doi.org\/10.1007\/s10064-019-01538-7","journal-title":"Bull Eng Geol Environ"},{"key":"1225_CR66","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.patcog.2018.02.019","volume":"79","author":"D Cui","year":"2018","unstructured":"Cui D, Huang G-B, Liu T (2018) ELM based smile detection using Distance Vector. Pattern Recognit 79:356\u2013369","journal-title":"Pattern Recognit"},{"key":"1225_CR67","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1007\/s00500-018-3012-5","volume":"22","author":"H Zhu","year":"2018","unstructured":"Zhu H, Tsang ECC, Zhu J (2018) Training an extreme learning machine by localized generalization error model. Soft Comput 22:3477\u20133485","journal-title":"Soft Comput"},{"key":"1225_CR68","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.swevo.2015.05.003","volume":"24","author":"P Mohapatra","year":"2015","unstructured":"Mohapatra P, Chakravarty S, Dash PK (2015) An improved cuckoo search based extreme learning machine for medical data classification. Swarm Evol Comput 24:25\u201349","journal-title":"Swarm Evol Comput"},{"key":"1225_CR69","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ref.2017.08.001","volume":"21","author":"P Satapathy","year":"2017","unstructured":"Satapathy P, Dhar S, Dash PK (2017) An evolutionary online sequential extreme learning machine for maximum power point tracking and control in multi-photovoltaic microgrid system. Renew Energy Focus 21:33\u201353","journal-title":"Renew Energy Focus"},{"key":"1225_CR70","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.eswa.2019.03.002","volume":"127","author":"L-L Li","year":"2019","unstructured":"Li L-L, Sun J, Tseng M-L, Li Z-G (2019) Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation. Expert Syst Appl 127:58\u201367","journal-title":"Expert Syst Appl"},{"key":"1225_CR71","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11063-012-9236-y","volume":"36","author":"J Cao","year":"2012","unstructured":"Cao J, Lin Z, Huang G-B (2012) Self-adaptive evolutionary extreme learning machine. Neural Process Lett 36:285\u2013305","journal-title":"Neural Process Lett"},{"key":"1225_CR72","doi-asserted-by":"crossref","unstructured":"Chen S, Shang Y, Wu M (2016) Application of PSO\u2013ELM in electronic system fault diagnosis. In: Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada, 20\u201322 June 2016","DOI":"10.1109\/ICPHM.2016.7542818"},{"key":"1225_CR73","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2011","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B 42:513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"1225_CR74","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879\u2013892","journal-title":"IEEE Trans Neural Netw"},{"key":"1225_CR75","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.advengsoft.2017.09.004","volume":"115","author":"ZM Yaseen","year":"2018","unstructured":"Yaseen ZM, Deo RC, Hilal A et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112\u2013125","journal-title":"Adv Eng Softw"},{"key":"1225_CR76","first-page":"72","volume":"1","author":"K Deep","year":"2009","unstructured":"Deep K, Bansal JC (2009) Mean particle swarm optimisation for function optimisation. Int J Comput Intell Stud 1:72\u201392","journal-title":"Int J Comput Intell Stud"},{"key":"1225_CR77","unstructured":"Kennedy J, Eberhart RC (1995) A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, vol 5, pp 4104\u20134108"},{"key":"1225_CR78","doi-asserted-by":"crossref","unstructured":"Bao GQ, Mao KF (2009) Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients. In: Proceedings of IEEE international conference on robotics and biomimetics, pp 2134\u20132139","DOI":"10.1109\/ROBIO.2009.5420504"},{"key":"1225_CR79","doi-asserted-by":"crossref","unstructured":"Cui Z, Zeng J, Yin Y (2008) An improved PSO with time-varying accelerator coefficients. In: Eighth International Conference on Intelligent Systems Design and Applications, ISDA\u201908. vol. 2, IEEE, pp. 638\u2013643","DOI":"10.1109\/ISDA.2008.86"},{"key":"1225_CR80","doi-asserted-by":"crossref","unstructured":"Ziyu T, Dingxue Z (2009) A modified particle swarm optimization with an adaptive acceleration coefficients. In: Proceedings of the IEEE international conference on Information Processing, pp 330\u2013332","DOI":"10.1109\/APCIP.2009.217"},{"key":"1225_CR81","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ijepes.2012.04.060","volume":"42","author":"B Mohammadi-Ivatloo","year":"2012","unstructured":"Mohammadi-Ivatloo B, Rabiee A, Soroudi A, Ehsan M (2012) Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems. Int J Electr Power Energy Syst 42:508\u2013516","journal-title":"Int J Electr Power Energy Syst"},{"key":"1225_CR82","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":"1225_CR83","doi-asserted-by":"publisher","DOI":"10.1007\/s00603-019-01947-w","author":"B Liu","year":"2019","unstructured":"Liu B, Yang H, Karekal S (2019) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng. https:\/\/doi.org\/10.1007\/s00603-019-01947-w","journal-title":"Rock Mech Rock Eng"},{"key":"1225_CR84","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":"1225_CR85","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":"1225_CR86","unstructured":"ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974\u20132006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods, International Society for Rock Mechanics. ISRM Turkish National Group, Ankara"},{"key":"1225_CR87","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":"1225_CR88","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":"1225_CR89","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.3390\/app9061042","volume":"9","author":"H Chen","year":"2019","unstructured":"Chen H, Asteris PG, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042","journal-title":"Appl Sci"},{"key":"1225_CR90","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-020-01003-0","author":"J Duan","year":"2020","unstructured":"Duan J, Asteris PG, Nguyen H et al (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-020-01003-0","journal-title":"Eng Comput"},{"key":"1225_CR91","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09611-4","author":"H Han","year":"2020","unstructured":"Han H, Armaghani DJ, Tarinejad R et al (2020) Random forest and bayesian network techniques for probabilistic prediction of Flyrock induced by blasting in quarry sites. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-019-09611-4","journal-title":"Nat Resour Res"},{"key":"1225_CR92","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-020-09676-6","author":"BR Murlidhar","year":"2020","unstructured":"Murlidhar BR, Kumar D, Jahed Armaghani D et al (2020) A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced Flyrock. Nat Resour Res. https:\/\/doi.org\/10.1007\/s11053-020-09676-6","journal-title":"Nat Resour Res"},{"key":"1225_CR93","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05244-4","author":"DJ Armaghani","year":"2020","unstructured":"Armaghani DJ, Asteris PG (2020) A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05244-4","journal-title":"Neural Comput Appl"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-020-01225-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-020-01225-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-020-01225-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T07:10:37Z","timestamp":1670051437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-020-01225-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,5]]},"references-count":93,"journal-issue":{"issue":"S5","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1225"],"URL":"https:\/\/doi.org\/10.1007\/s00366-020-01225-2","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,5]]},"assertion":[{"value":"10 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}