{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T02:12:44Z","timestamp":1774145564796,"version":"3.50.1"},"reference-count":19,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":1787,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["KSCE Journal of Civil Engineering"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s12205-019-0302-0","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T17:03:10Z","timestamp":1567616590000},"page":"4529-4537","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":32,"title":["Modelling the Torque with Artificial Neural Networks on a Tunnel Boring Machine"],"prefix":"10.1016","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0556-1056","authenticated-orcid":false,"given":"Paulo","family":"Cachim","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-0461","authenticated-orcid":false,"given":"Adam","family":"Bezuijen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1007\/s12205-019-0302-0_bib1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0886-7798(00)00055-9","article-title":"Modeling tunnel boring machine performance by neuro-fuzzy methods","volume":"15","author":"Alvarez Grima","year":"2000","journal-title":"Tunn. Undergr. Sp. Technol."},{"issue":"6","key":"10.1007\/s12205-019-0302-0_bib2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.tust.2004.02.128","article-title":"Modelling TBM performance with artificial neural networks","volume":"19","author":"Benardos","year":"2004","journal-title":"Tunn. Undergr. Sp. Technol."},{"key":"10.1007\/s12205-019-0302-0_bib3","series-title":"Tunnelling. A Decade of Progress","first-page":"35","article-title":"The influence of soil permeability on the properties of a foam mixture in a TBM","author":"Bezuijen","year":"2006"},{"issue":"1","key":"10.1007\/s12205-019-0302-0_bib4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11063-016-9498-x","article-title":"Inverse back analysis based on evolutionary neural networks for underground engineering","volume":"44","author":"Gao","year":"2016","journal-title":"Neural Process. Lett."},{"key":"10.1007\/s12205-019-0302-0_bib5","unstructured":"Godinez, R., Yu, H., Mooney, M., and Contractors, J. D. (2015). \u201cEarth pressure balance machine cutterhead torque modeling: Learning from machine data.\u201d Rapid Excavation and Tunneling Conference 2015. New Orleans, USA, pp. 1261-1271."},{"issue":"1","key":"10.1007\/s12205-019-0302-0_bib6","first-page":"1","article-title":"Neural Network Applications in Geotechnical Engineering","volume":"8","author":"Goh","year":"2001","journal-title":"Sci. Iran."},{"key":"10.1007\/s12205-019-0302-0_bib7","first-page":"1243","article-title":"Encog: Library of interchangeable machine learning models for java and C#","volume":"16","author":"Heaton","year":"2015","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"10.1007\/s12205-019-0302-0_bib8","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/S0266-352X(01)00011-8","article-title":"Neural network based prediction of ground surface settlements due to tunnelling","volume":"28","author":"Kim","year":"2001","journal-title":"Comput. Geotech."},{"key":"10.1007\/s12205-019-0302-0_bib9","unstructured":"Mori, L. (2016). Advancing understanding of the relationship between soil conditioning and earth pressure balance tunnel boring machine chamber and shield annulus behavior, PhD Thesis, Colorado School of Mines, Colorado, USA, 11124\/170474."},{"issue":"2","key":"10.1007\/s12205-019-0302-0_bib10","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.tust.2005.07.001","article-title":"Prediction of tunnelinginduced ground movement with the multi-layer perceptron","volume":"21","author":"Neaupane","year":"2006","journal-title":"Tunn. Undergr. Sp. Technol."},{"issue":"4","key":"10.1007\/s12205-019-0302-0_bib11","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/S0886-7798(00)00011-0","article-title":"EPBMs for the north east line project","volume":"14","author":"Reilly","year":"1999","journal-title":"Tunn. Undergr. Sp. Technol."},{"issue":"1","key":"10.1007\/s12205-019-0302-0_bib12","first-page":"49","article-title":"Artificial neural network applications in geotechnical engineering","volume":"36","author":"Shahin","year":"2001","journal-title":"Aust. Geomech. J."},{"key":"10.1007\/s12205-019-0302-0_bib13","first-page":"1","article-title":"State of the art of artificial neural networks in geotechnical engineering","volume":"8","author":"Shahin","year":"2008","journal-title":"Electron. J. Geotech. Eng., Bouquet"},{"issue":"8","key":"10.1007\/s12205-019-0302-0_bib14","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.autcon.2011.04.010","article-title":"Determination of the cutterhead torque for EPB shield tunneling machine","volume":"20","author":"Shi","year":"2011","journal-title":"Autom. Constr."},{"issue":"4","key":"10.1007\/s12205-019-0302-0_bib15","first-page":"231","article-title":"Applying artificial neural networks for analysis of geotechnical problems","volume":"18","author":"Sulewska","year":"2011","journal-title":"Comput. Assist. Mech. Eng. Sci."},{"issue":"8","key":"10.1007\/s12205-019-0302-0_bib16","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.tust.2005.06.007","article-title":"Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling","volume":"21","author":"Suwansawat","year":"2006","journal-title":"Tunn. Undergr. Sp. Technol."},{"issue":"6","key":"10.1007\/s12205-019-0302-0_bib17","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1007\/s11431-012-4749-1","article-title":"A new calculation model of cutterhead torque and investigation of its influencing factors","volume":"55","author":"Wang","year":"2012","journal-title":"Sci. China Technol. Sci."},{"key":"10.1007\/s12205-019-0302-0_bib18","author":"Xiangjun"},{"key":"10.1007\/s12205-019-0302-0_bib19","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.autcon.2013.03.001","article-title":"PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River","volume":"36","author":"Zhou","year":"2013","journal-title":"Autom. Constr."}],"container-title":["KSCE Journal of Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12205-019-0302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12205-019-0302-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1226798824031970?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1226798824031970?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12205-019-0302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T16:49:12Z","timestamp":1735836552000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1226798824031970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":19,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["S1226798824031970"],"URL":"https:\/\/doi.org\/10.1007\/s12205-019-0302-0","relation":{},"ISSN":["1226-7988"],"issn-type":[{"value":"1226-7988","type":"print"}],"subject":[],"published":{"date-parts":[[2019,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Modelling the Torque with Artificial Neural Networks on a Tunnel Boring Machine","name":"articletitle","label":"Article Title"},{"value":"KSCE Journal of Civil Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1007\/s12205-019-0302-0","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2019 \u00a9 Korean Society of Civil Engineers 2019 published by Elsevier inc. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}