{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:56:43Z","timestamp":1776131803219,"version":"3.50.1"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100018625","name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["19DZ1201004"],"award-info":[{"award-number":["19DZ1201004"]}],"id":[{"id":"10.13039\/501100018625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41877227"],"award-info":[{"award-number":["41877227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51509186"],"award-info":[{"award-number":["51509186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52378407"],"award-info":[{"award-number":["52378407"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104630","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:39:52Z","timestamp":1774514392000},"page":"104630","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Mechanism-Driven vs. Data-Driven: A comparison of physics-informed bayesian estimation and deep learning models for EPB shield chamber pressure forecasting"],"prefix":"10.1016","volume":"74","author":[{"given":"Tong","family":"Yin","sequence":"first","affiliation":[]},{"given":"Yeting","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shuaifeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yudan","family":"Gou","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Dias","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zixin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104630_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2024.105642","article-title":"Predictive machine learning in earth pressure balanced tunneling for main drive torque estimation of tunnel boring machines","volume":"146","author":"Glab","year":"2024","journal-title":"Tunneling and Underground Space Technology"},{"key":"10.1016\/j.aei.2026.104630_b0010","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.autcon.2010.11.002","article-title":"Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO","volume":"20","author":"Liu","year":"2011","journal-title":"Autom. Constr."},{"issue":"4","key":"10.1016\/j.aei.2026.104630_b0015","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1680\/geot.1980.30.4.397","article-title":"The stability of shallow tunnels and underground openings in cohesive material","volume":"30","author":"Davis","year":"1980","journal-title":"G\u00e9otechnique"},{"issue":"6","key":"10.1016\/j.aei.2026.104630_b0020","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1061\/(ASCE)1532-3641(2009)9:6(237)","article-title":"Probabilistic analysis and design of circular tunnels against face stability","volume":"9","author":"Mollon","year":"2009","journal-title":"Int. J. Geomech."},{"issue":"4","key":"10.1016\/j.aei.2026.104630_b0025","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1680\/geot.1990.40.4.581","article-title":"Upper and lower bound solutions for the face stability of shallow circular tunnels in frictional material","volume":"40","author":"Leca","year":"2015","journal-title":"G\u00e9otechnique"},{"issue":"9","key":"10.1016\/j.aei.2026.104630_b0030","first-page":"86","article-title":"A mathematical model and the related parameters for EPB shield tunneling","volume":"39","author":"Wang","year":"2006","journal-title":"Chin. Civil Eng. J."},{"issue":"21","key":"10.1016\/j.aei.2026.104630_b0035","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3901\/JME.2014.21.105","article-title":"Intelligent control for earth pressure in chamber of earth pressure balance shield machine based on multi-system coordination","volume":"50","author":"Shao","year":"2014","journal-title":"Journal of Mechanical Engineering"},{"issue":"3","key":"10.1016\/j.aei.2026.104630_b0040","doi-asserted-by":"crossref","first-page":"598","DOI":"10.3901\/CJME.2016.0330.042","article-title":"Pressure regulation for earth pressure balance control on shield tunneling machine by using adaptive robust control","volume":"29","author":"Xie","year":"2016","journal-title":"Chinese Journal of Mechanical Engineering"},{"key":"10.1016\/j.aei.2026.104630_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2020.103457","article-title":"A simplified excavation chamber pressure model for EPBM tunneling","volume":"103","author":"Yu","year":"2020","journal-title":"Tunneling and Underground Space Technology"},{"key":"10.1016\/j.aei.2026.104630_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106702","article-title":"Autonomous intelligent control of earth pressure balance shield machine based on deep reinforcement learning","volume":"125","author":"Liu","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"issue":"1","key":"10.1016\/j.aei.2026.104630_b0055","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"issue":"2","key":"10.1016\/j.aei.2026.104630_b0060","first-page":"107","article-title":"Novel approach to nonlinear\/non-Gaussian Bayesian state estimation","volume":"140","author":"Gordon","year":"1993","journal-title":"IEE Proceedings F"},{"key":"10.1016\/j.aei.2026.104630_b0065","doi-asserted-by":"crossref","unstructured":"S.J. Julier, J.K. Uhlmann, New extension of the Kalman filter to nonlinear systems, Proceedings of the SPIE 3068 (1997) 182\u2013193.","DOI":"10.1117\/12.280797"},{"key":"10.1016\/j.aei.2026.104630_b0070","doi-asserted-by":"crossref","first-page":"2416","DOI":"10.1175\/1520-0493(1997)125<2416:UEFFMV>2.0.CO;2","article-title":"Using ensemble forecasts for model validation","volume":"125","author":"Houtekamer","year":"1997","journal-title":"Mon. Weather Rev."},{"key":"10.1016\/j.aei.2026.104630_b0075","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1002\/qj.699","article-title":"Nonlinear data assimilation in geosciences: an extremely efficient particle filter","volume":"136","author":"van Leeuwen","year":"2010","journal-title":"Q. J. R. Meteorolog. Soc."},{"issue":"4","key":"10.1016\/j.aei.2026.104630_b0080","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1175\/MWR-D-14-00108.1","article-title":"A second-order exact ensemble square root filter for nonlinear data assimilation","volume":"143","author":"T\u00f6dter","year":"2015","journal-title":"Mon. Weather Rev."},{"key":"10.1016\/j.aei.2026.104630_b0085","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1002\/qj.3551","article-title":"Particle filters for high-dimensional geoscience applications: a review","volume":"145","author":"van Leeuwen","year":"2019","journal-title":"Q. J. R. Meteorolog. Soc."},{"key":"10.1016\/j.aei.2026.104630_b0090","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.5194\/gmd-15-2773-2022","article-title":"Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5","volume":"15","author":"Park","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.aei.2026.104630_b0095","series-title":"Enhancing state estimation in robots: a data-driven approach with differentiable ensemble Kalman filters, IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","first-page":"1947","author":"Liu","year":"2023"},{"key":"10.1016\/j.aei.2026.104630_b0100","unstructured":"A. Gelb, J. F. Kasper, R. A Nash., C. F. Price, A. A. Sutherland, Applied optimal estimation. MIT press (1974)."},{"issue":"C5","key":"10.1016\/j.aei.2026.104630_b0105","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1029\/94JC00572","article-title":"Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics","volume":"99","author":"Evensen","year":"1994","journal-title":"J. Geophys. Res. Oceans"},{"key":"10.1016\/j.aei.2026.104630_b0110","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1175\/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2","article-title":"Hydrologic Data Assimilation with the Ensemble Kalman Filter","volume":"130","author":"Reichle","year":"2002","journal-title":"Mon. Weather Rev."},{"key":"10.1016\/j.aei.2026.104630_b0115","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.advwatres.2005.09.007","article-title":"Data Assimilation for Transient Flow in Geologic Formations via Ensemble Kalman Filter","volume":"29","author":"Chen","year":"2006","journal-title":"Adv. Water Resour."},{"key":"10.1016\/j.aei.2026.104630_b0120","doi-asserted-by":"crossref","unstructured":"A. Doucet, N. de Freitas, N. Gordon, Sequential Monte Carlo Methods in Practice, Springer-Verlag, New York, NY, USA (2001).","DOI":"10.1007\/978-1-4757-3437-9"},{"key":"10.1016\/j.aei.2026.104630_b0125","doi-asserted-by":"crossref","DOI":"10.3389\/fams.2022.904687","article-title":"Multifidelity ensemble Kalman filtering using surrogate models defined by theory-guided autoencoders","volume":"8","author":"Popov","year":"2022","journal-title":"Front. Appl. Math. Stat."},{"key":"10.1016\/j.aei.2026.104630_b0130","article-title":"A. physics-informed Bayesian data assimilation approach for real-time drilling tool lateral motion prediction","volume":"10","author":"Song","year":"2024","journal-title":"Front. Appl. Math. Stat."},{"issue":"2","key":"10.1016\/j.aei.2026.104630_b0135","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Comput."},{"issue":"8","key":"10.1016\/j.aei.2026.104630_b0140","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.aei.2026.104630_b0145","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1724","article-title":"Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation","author":"Cho","year":"2014"},{"key":"10.1016\/j.aei.2026.104630_b0150","article-title":"Predicting slurry pressure balance with a long short-term memory recurrent neural network in difficult ground condition","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.aei.2026.104630_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107213","article-title":"A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque","volume":"228","author":"Shi","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104630_b0160","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s11431-022-2218-9","article-title":"A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging","volume":"66","author":"Qin","year":"2023","journal-title":"Sci. China Technol. Sci."},{"key":"10.1016\/j.aei.2026.104630_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.trgeo.2022.100837","article-title":"Prediction of shield machine posture using the GRU algorithm with adaptive boosting: a case study of Chengdu Subway project","volume":"37","author":"Xiao","year":"2022","journal-title":"Transp. Geotech."},{"key":"10.1016\/j.aei.2026.104630_b0170","doi-asserted-by":"crossref","first-page":"866","DOI":"10.3390\/s24030866","article-title":"Confining pressure forecasting of shield tunnel lining based on GRU model and RNN model","volume":"24","author":"Wang","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104630_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2023.105499","article-title":"Confining pressure forecasting of shield tunnel lining during construction based on LSTM-PSO models combined with the multi-output recursive strategy","volume":"143","author":"Ye","year":"2024","journal-title":"Tunneling and Underground Space Technology"},{"issue":"1","key":"10.1016\/j.aei.2026.104630_b0180","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jrmge.2023.06.010","article-title":"A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM","volume":"16","author":"Yu","year":"2024","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"10.1016\/j.aei.2026.104630_b0185","unstructured":"V. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, arXiv preprint arXiv:1706.03762 (2017)."},{"key":"10.1016\/j.aei.2026.104630_b0190","doi-asserted-by":"crossref","unstructured":"H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, Informer: Beyond efficient transformer for long sequence time-series forecasting, arXiv preprint arXiv:2012.07436 (2020).","DOI":"10.1609\/aaai.v35i12.17325"},{"issue":"1717","key":"10.1016\/j.aei.2026.104630_b0195","first-page":"22419","article-title":"Autoformer: decomposition transformers with auto-correlation for long-term series forecasting","volume":"35","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"A","key":"10.1016\/j.aei.2026.104630_b0200","article-title":"Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model","volume":"168","author":"Zhang","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104630_b0205","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.3390\/app15031674","article-title":"Long-distance shield tunneling performance prediction based on Informer","volume":"15","author":"Hu","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104630_b0210","article-title":"Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method","volume":"63","author":"Zhen","year":"2025","journal-title":"Eng. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104630_b0215","doi-asserted-by":"crossref","first-page":"15725","DOI":"10.1038\/s41598-025-98428-8","article-title":"Prediction of super-large diameter shield attitude based on LSTM-Transformer","volume":"15","author":"Dai","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104630_b0220","doi-asserted-by":"crossref","DOI":"10.1088\/1361-665X\/adbd0d","article-title":"Deep convolutional transformer network for quantifying crack width in tunnel lining structures using distributed fiber optic sensing data","volume":"34","author":"Liao","year":"2025","journal-title":"Smart Mater. Struct."},{"key":"10.1016\/j.aei.2026.104630_b0225","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1631\/jzus.A2400012","article-title":"Predicting Tunnel Boring Machine Performance with the Informer Model: a Case Study of the Guangzhou Metro Line Project","volume":"26","author":"Zhao","year":"2025","journal-title":"Journal of Zhejiang University-Science A (Applied Physics & Engineering)"},{"key":"10.1016\/j.aei.2026.104630_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2021.103946","article-title":"State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction","volume":"113","author":"Jong","year":"2021","journal-title":"Tunn. Undergr. Space Technol."},{"issue":"11","key":"10.1016\/j.aei.2026.104630_b0235","doi-asserted-by":"crossref","DOI":"10.1111\/cts.70056","article-title":"Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development","volume":"17","author":"Ponce-Bobadilla","year":"2024","journal-title":"Clin. Transl. Sci."},{"key":"10.1016\/j.aei.2026.104630_b0240","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1002\/for.3220","article-title":"Vector SHAP values for machine learning time series forecasting","volume":"44","author":"Choi","year":"2025","journal-title":"J. Forecast."},{"issue":"15","key":"10.1016\/j.aei.2026.104630_b0245","doi-asserted-by":"crossref","first-page":"7874","DOI":"10.1021\/acs.jcim.4c02414","article-title":"Integrating machine learning and SHAP analysis to advance the rational design of benzothiadiazole derivatives with tailored photophysical properties","volume":"65","author":"Ver\u00edssimo","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.aei.2026.104630_b0250","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.mineng.2018.12.004","article-title":"Machine learning applications in minerals processing: a review","volume":"132","author":"McCoy","year":"2019","journal-title":"Miner. Eng."},{"issue":"3","key":"10.1016\/j.aei.2026.104630_b0255","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ijmst.2014.03.003","article-title":"Sensing for advancing mining automation capability: a review of underground automation technology development","volume":"24","author":"Ralston","year":"2014","journal-title":"Int. J. Min. Sci. Technol."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003228?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003228?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:16:52Z","timestamp":1776129412000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":51,"alternative-id":["S1474034626003228"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104630","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mechanism-Driven vs. Data-Driven: A comparison of physics-informed bayesian estimation and deep learning models for EPB shield chamber pressure forecasting","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104630","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104630"}}