{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T17:29:19Z","timestamp":1781890159879,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61633019"],"award-info":[{"award-number":["61633019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07179-4","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T10:14:57Z","timestamp":1648548897000},"page":"13485-13498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ViRFD: a virtual-realistic fused dataset for rock size analysis in TBM construction"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9593-9429","authenticated-orcid":false,"given":"Zhenfeng","family":"Xue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5791-1823","authenticated-orcid":false,"given":"Weijie","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"issue":"2","key":"7179_CR1","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/0886-7798(96)00017-X","volume":"11","author":"G Anagnostou","year":"1996","unstructured":"Anagnostou G, Kov\u00e1ri K (1996) Face stability conditions with earth-pressure-balanced shields. Tunn Undergr Space Technol 11(2):165\u2013173","journal-title":"Tunn Undergr Space Technol"},{"key":"7179_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cviu.2017.12.002","volume":"167","author":"IB Barbosa","year":"2018","unstructured":"Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T (2018) Looking beyond appearances: synthetic training data for deep CNNS in re-identification. Comput Vis Image Underst 167:50\u201362","journal-title":"Comput Vis Image Underst"},{"issue":"2","key":"7179_CR3","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: a high-definition ground truth database. Pattern Recogn Lett 30(2):88\u201397","journal-title":"Pattern Recogn Lett"},{"key":"7179_CR4","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"issue":"8","key":"7179_CR5","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1109\/LSP.2018.2850222","volume":"25","author":"C Cruz","year":"2018","unstructured":"Cruz C, Foi A, Katkovnik V, Egiazarian K (2018) Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process Lett 25(8):1216\u20131220. https:\/\/doi.org\/10.1109\/LSP.2018.2850222","journal-title":"IEEE Signal Process Lett"},{"key":"7179_CR6","doi-asserted-by":"crossref","unstructured":"Devaranjan J, Kar A, Fidler S (2020) Meta-sim2: unsupervised learning of scene structure for synthetic data generation. In: Proceeding of the European conference on computer vision, pp 715\u2013733","DOI":"10.1007\/978-3-030-58520-4_42"},{"key":"7179_CR7","unstructured":"Erben H (2016) Real-time material analysis and development of a collaboration and trading platform for mineral resources from underground construction projects. Doctoral thesis in Montanuniversity Leoben"},{"issue":"6","key":"7179_CR8","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.tust.2008.01.005","volume":"23","author":"E Farrokh","year":"2008","unstructured":"Farrokh E, Rostami J (2008) Correlation of tunnel convergence with tbm operational parameters and chip size in the ghomroud tunnel, iran. Tunn Undergr Space Technol 23(6):700\u2013710","journal-title":"Tunn Undergr Space Technol"},{"key":"7179_CR9","doi-asserted-by":"crossref","unstructured":"Gaidon A, Wang Q, Cabon Y, Vig E (2016) Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4340\u20134349","DOI":"10.1109\/CVPR.2016.470"},{"issue":"11","key":"7179_CR10","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res 32(11):1231\u20131237","journal-title":"Int J Robot Res"},{"key":"7179_CR11","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3354\u20133361. IEEE","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"7179_CR12","doi-asserted-by":"publisher","first-page":"103655","DOI":"10.1016\/j.tust.2020.103655","volume":"107","author":"Q Gong","year":"2021","unstructured":"Gong Q, Zhou X, Liu Y, Han B, Yin L (2021) Development of a real-time muck analysis system for assistant intelligence tbm tunnelling. Tunn Undergr Space Technol 107:103655","journal-title":"Tunn Undergr Space Technol"},{"issue":"1","key":"7179_CR13","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton A, Borgwardt KM, Rasch MJ, Sch\u00f6lkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13(1):723\u2013773","journal-title":"J Mach Learn Res"},{"issue":"11\u201312","key":"7179_CR14","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1016\/j.mineng.2004.05.017","volume":"17","author":"O Guyot","year":"2004","unstructured":"Guyot O, Monredon T, Larosa D, Broussaud A (2004) Visiorock, an integrated vision technology for advanced control of aggregate circuits. Miner Eng 17(11\u201312):1227\u20131235","journal-title":"Miner Eng"},{"key":"7179_CR15","doi-asserted-by":"crossref","unstructured":"Hattori H, Naresh\u00a0Boddeti V, Kitani K.M, Kanade T (2015) Learning scene-specific pedestrian detectors without real data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3819\u20133827","DOI":"10.1109\/CVPR.2015.7299006"},{"key":"7179_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"7179_CR17","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Syst 30"},{"issue":"5","key":"7179_CR18","first-page":"61","volume":"40","author":"W Hongxin","year":"2007","unstructured":"Hongxin W, Deming F (2007) Theoretical and test studies on balance control of epb shields. Chin Civil Eng J 40(5):61\u201368","journal-title":"Chin Civil Eng J"},{"key":"7179_CR19","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00521-017-3158-6","volume":"29","author":"F Jiang","year":"2018","unstructured":"Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29:1257\u20131265","journal-title":"Neural Comput Appl"},{"key":"7179_CR20","unstructured":"Juliani A, Berges V, Vckay E, Gao Y, Henry H, Mattar M, Lange D (2018) Unity: a general platform for intelligent agents. CoRR arxiv:1809.02627"},{"issue":"9","key":"7179_CR21","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1109\/TMM.2018.2798282","volume":"20","author":"B Kang","year":"2018","unstructured":"Kang B, Lee Y, Nguyen TQ (2018) Depth-adaptive deep neural network for semantic segmentation. IEEE Trans Multimedia 20(9):2478\u20132490. https:\/\/doi.org\/10.1109\/TMM.2018.2798282","journal-title":"IEEE Trans Multimedia"},{"key":"7179_CR22","doi-asserted-by":"crossref","unstructured":"Kar A, Prakash A, Liu M.Y, Cameracci E, Yuan J, Rusiniak M, Acuna D, Torralba A, Fidler S (2019) Meta-sim: learning to generate synthetic datasets. In: 2019 IEEE\/CVF International conference on computer vision (ICCV), pp 4550\u20134559","DOI":"10.1109\/ICCV.2019.00465"},{"key":"7179_CR23","unstructured":"Kipf T.N, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"issue":"4","key":"7179_CR24","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7179_CR25","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/TMM.2020.2991532","volume":"23","author":"WZ Nie","year":"2021","unstructured":"Nie WZ, Jia WW, Li WH, Liu AA, Zhao SC (2021) 3D pose estimation based on reinforce learning for 2D image-based 3D model retrieval. IEEE Trans Multimed 23:1021\u20131034. https:\/\/doi.org\/10.1109\/TMM.2020.2991532","journal-title":"IEEE Trans Multimed"},{"key":"7179_CR26","doi-asserted-by":"crossref","unstructured":"Nurzynska K, Iwaszenko S (2020) Application of texture features and machine learning methods to grain segmentation in rock material images. Image Anal Stereol 39(2):73\u201390","DOI":"10.5566\/ias.2186"},{"key":"7179_CR27","unstructured":"Outal S, Beucher S (2009) Controlling the ultimate opening residues for a robust delineation of fragmetned rocks. In: The 10th European Congress of Stereology and Image Analysis, Milan"},{"key":"7179_CR28","doi-asserted-by":"crossref","unstructured":"Outal S, Jeulin D, Schleifer J (2011) A new method for estimating the 3d size-distribution curve of fragmented rocks out of 2d images. Image Anal Stereol 27(2):97\u2013105","DOI":"10.5566\/ias.v27.p97-105"},{"key":"7179_CR29","doi-asserted-by":"crossref","unstructured":"Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters\u2013improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4353\u20134361","DOI":"10.1109\/CVPR.2017.189"},{"key":"7179_CR30","doi-asserted-by":"crossref","unstructured":"Pepik B, Stark M, Gehler P, Schiele B (2012) Teaching 3d geometry to deformable part models. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3362\u20133369. IEEE","DOI":"10.1109\/CVPR.2012.6248075"},{"key":"7179_CR31","doi-asserted-by":"crossref","unstructured":"Richter S.R, Vineet V, Roth S, Koltun V (2016) Playing for data: ground truth from computer games. In: European conference on computer vision","DOI":"10.1007\/978-3-319-46475-6_7"},{"issue":"10","key":"7179_CR32","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1007\/s00603-017-1256-5","volume":"50","author":"A Rispoli","year":"2017","unstructured":"Rispoli A, Ferrero AM, Cardu M, Farinetti A (2017) Determining the particle size of debris from a tunnel boring machine through photographic analysis and comparison between excavation performance and rock mass properties. Rock Mech Rock Eng 50(10):2805\u20132816","journal-title":"Rock Mech Rock Eng"},{"key":"7179_CR33","unstructured":"Ruiz N, Schulter S, Chandraker M (2019) Learning to simulate. In: International conference on learning representations"},{"key":"7179_CR34","doi-asserted-by":"crossref","unstructured":"Satkin S, Lin J, Hebert M (2012) Data-driven scene understanding from 3d models. In: British machine vision conference, pp 128.1\u2013128.11","DOI":"10.5244\/C.26.128"},{"key":"7179_CR35","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s00521-016-2313-9","volume":"28","author":"V Senniappan","year":"2017","unstructured":"Senniappan V, Subramanian J, Papageorgiou EI, Mohan S (2017) Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput Appl 28:107\u2013117","journal-title":"Neural Comput Appl"},{"key":"7179_CR36","unstructured":"Shao C, Liao J, Li X, Su H (2015) An adaptive robust control for hard rock tunnel boring machine cutterhead driving system. In: ASME 2015 Dynamic systems and control conference, pp. V003T48A001\u2013V003T48A001. American Society of Mechanical Engineers"},{"key":"7179_CR37","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of international conference on learning representations, pp 1\u201314"},{"issue":"3\/4","key":"7179_CR38","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1076\/frag.6.3.301.14055","volume":"6","author":"B Smith","year":"2002","unstructured":"Smith B (2002) Improvements in blast fragmentation using measurement while drilling parameters. Fragblast 6(3\/4):301\u2013310","journal-title":"Fragblast"},{"key":"7179_CR39","doi-asserted-by":"crossref","unstructured":"Sun X, Zheng L (2019) Dissecting person re-identification from the viewpoint of viewpoint. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 608\u2013617","DOI":"10.1109\/CVPR.2019.00070"},{"key":"7179_CR40","doi-asserted-by":"crossref","unstructured":"Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, To T, Cameracci E, Boochoon S, Birchfield S (2018) Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 969\u2013977","DOI":"10.1109\/CVPRW.2018.00143"},{"issue":"3\u20134","key":"7179_CR41","first-page":"229","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3\u20134):229\u2013256","journal-title":"Mach Learn"},{"key":"7179_CR42","doi-asserted-by":"crossref","unstructured":"Xue Z, Chen L, Liu Z, Lin F, Mao W (2021) Rock segmentation visual system for assisting driving in tbm construction. Mach Vis Appl 32(4):1\u201312","DOI":"10.1007\/s00138-021-01203-8"},{"key":"7179_CR43","doi-asserted-by":"crossref","unstructured":"Xue Z, Jia L, Sun W, Lin F, Liu Z, Mao W (2019) Multi mask learning of stone segmentation for auto-monitoring system in tbm construction. In: 2019 Chinese Control Conference (CCC), pp 8733\u20138738. 10.23919\/ChiCC.2019.8865323","DOI":"10.23919\/ChiCC.2019.8865323"},{"key":"7179_CR44","doi-asserted-by":"crossref","unstructured":"Xue Z, Mao W, Jiang W (2020) Ehanet: Efficient hybrid attention network towards real-time semantic segmentation. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp 787\u2013791. 10.1109\/ICCC51575.2020.9345050","DOI":"10.1109\/ICCC51575.2020.9345050"},{"key":"7179_CR45","doi-asserted-by":"crossref","unstructured":"Xue Z, Mao W, Zheng L (2021) Learning to simulate complex scenes for street scene segmentation. IEEE Transactions on Multimedia p 1. 10.1109\/TMM.2021.3062497","DOI":"10.1109\/TMM.2021.3062497"},{"issue":"3","key":"7179_CR46","doi-asserted-by":"publisher","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 Space Technol 23(3):326\u2013339","journal-title":"Tunn Undergr Space Technol"},{"issue":"10","key":"7179_CR47","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1007\/s11431-009-0245-7","volume":"52","author":"H Yang","year":"2009","unstructured":"Yang H, Shi H, Gong G, Hu G (2009) Earth pressure balance control for epb shield. Sci China Ser E: Technol Sci 52(10):2840\u20132848","journal-title":"Sci China Ser E: Technol Sci"},{"key":"7179_CR48","doi-asserted-by":"crossref","unstructured":"Yao Y, Zheng L, Yang X, Naphade M, Gedeon T (2020) Simulating content consistent vehicle datasets with attribute descent. In: Proceedings of European conference on computer vision, pp 775\u2013791","DOI":"10.1007\/978-3-030-58539-6_46"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07179-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07179-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07179-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T10:05:46Z","timestamp":1658657146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07179-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,27]]},"references-count":48,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7179"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07179-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,27]]},"assertion":[{"value":"14 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}