{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:20:37Z","timestamp":1774671637421,"version":"3.50.1"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s12145-023-01101-9","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:02:14Z","timestamp":1694217734000},"page":"4273-4284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Anomaly detection method for TBM construction based on improved VMD-XGBoost-BILSTM combined model"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhipeng","family":"Lu","sequence":"first","affiliation":[]},{"given":"Kebin","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"issue":"3","key":"1101_CR1","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"Dragomiretskiy K, Zosso D (2013) Variational mode decomposition[J]. IEEE Trans Signal Process 62(3):531\u2013544","journal-title":"IEEE Trans Signal Process"},{"issue":"06","key":"1101_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.13807\/j.cnki.mtt.2022.06.002","volume":"59","author":"QF Du","year":"2022","unstructured":"Du QF, Zhang SL, Zhang CX et al (2022) Mud-water balanced shield tunneling speed prediction method based on mean filter denoising and XGBoost algorithm[J]. Modern Tunneling Technology 59(06):14\u201323. https:\/\/doi.org\/10.13807\/j.cnki.mtt.2022.06.002","journal-title":"Modern Tunneling Technology"},{"key":"1101_CR3","doi-asserted-by":"publisher","unstructured":"Hou JZ, Jia GP, Liu B et al (2022a) Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series. Proc Inst Mech Eng C J Mech Eng Sci 236(10). https:\/\/doi.org\/10.1177\/09544062211061682","DOI":"10.1177\/09544062211061682"},{"issue":"01","key":"1101_CR4","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jrmge.2021.05.004","volume":"14","author":"SK Hou","year":"2022","unstructured":"Hou SK, Liu YR, Yang Q (2022b) Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning. J Rock Mech Geotech Eng 14(01):123\u2013143","journal-title":"J Rock Mech Geotech Eng"},{"key":"1101_CR5","doi-asserted-by":"publisher","unstructured":"Li ZM, Yazdani BB, Behn A et al (2021) A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Comput 25(17). https:\/\/doi.org\/10.1007\/S00500-021-06005-8","DOI":"10.1007\/S00500-021-06005-8"},{"key":"1101_CR6","doi-asserted-by":"publisher","unstructured":"Liu FP, Liu Y, Yang C, et al (2022) A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling[J]. Water Resour Manag (12). https:\/\/doi.org\/10.1007\/S11269-022-03277-Z","DOI":"10.1007\/S11269-022-03277-Z"},{"key":"1101_CR7","doi-asserted-by":"publisher","unstructured":"Liang Y, Jiang K, Gao SJ, Yin YH (2022) Prediction of Tunnelling Parameters for Underwater Shield Tunnels, Based on the GA-BPNN Method. Sustainability https:\/\/doi.org\/10.3390\/SU142013420","DOI":"10.3390\/SU142013420"},{"key":"1101_CR8","doi-asserted-by":"publisher","unstructured":"Lu YH, Tang LQ, Chen CB, et al (2023) Reconstruction of structural long-term acceleration response based on BILSTM networks. Eng Struct https:\/\/doi.org\/10.1016\/J.ENGSTRUCT.2023.116000","DOI":"10.1016\/J.ENGSTRUCT.2023.116000"},{"key":"1101_CR9","doi-asserted-by":"crossref","unstructured":"Niu DX, Yu M, Sun LJ, et al (2022) Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism[J]. Appl Energy, 313","DOI":"10.1016\/j.apenergy.2022.118801"},{"key":"1101_CR10","doi-asserted-by":"publisher","unstructured":"Wang Q, Xie XY, Shahrour I, Huang Y (2021) Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions. Autom Constr https:\/\/doi.org\/10.1016\/J.AUTCON.2021.103741","DOI":"10.1016\/J.AUTCON.2021.103741"},{"key":"1101_CR11","doi-asserted-by":"crossref","unstructured":"Wang KY, Zhang LM, Fu XL (2023) Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI)[J]. Autom Constr, 147","DOI":"10.1016\/j.autcon.2022.104730"},{"key":"1101_CR12","unstructured":"Yan CB, Gao ZA, Yao XT, et al (2023) A weighted random forest prediction model for TBM construction speed considering uncertainty[J\/OL]. J Geotech Eng, pp. 1\u20139"},{"issue":"03","key":"1101_CR13","first-page":"984","volume":"54","author":"GF Zhao","year":"2023","unstructured":"Zhao GF, Jiang YB, Rui FX et al (2023) Numerical simulation-based model for evaluating TBM tunneling performance in complex rock bodie [J]s. Journal of Central South University (Natural Science Edition) 54(03):984\u2013997","journal-title":"Journal of Central South University (Natural Science Edition)"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01101-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-01101-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01101-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T06:32:36Z","timestamp":1702017156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-01101-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":13,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1101"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-01101-9","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]},"assertion":[{"value":"12 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate are"}},{"value":"Written informed consent for publication was obtained from all participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}