{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:27:09Z","timestamp":1780964829804,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFC3802301"],"award-info":[{"award-number":["2022YFC3802301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["52178306"],"award-info":[{"award-number":["52178306"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFC3802301"],"award-info":[{"award-number":["2022YFC3802301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["52178306"],"award-info":[{"award-number":["52178306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the advancement of engineering techniques, underground shield tunneling projects have also started incorporating emerging technologies to monitor the forces and displacements during the construction and operation phases of shield tunnels. Monitoring devices installed on the tunnel segment components generate a large amount of data. However, due to various factors, data may be missing. Hence, the completion of the incomplete data is imperative to ensure the utmost safety of the engineering project. In this research, a missing data imputation technique utilizing Random Forest (RF) is introduced. The optimal combination of the number of decision trees, maximum depth, and number of features in the RF is determined by minimizing the Mean Squared Error (MSE). Subsequently, complete soil pressure data are artificially manipulated to create incomplete datasets with missing rates of 20%, 40%, and 60%. A comparative analysis of the imputation results using three methods\u2014median, mean, and RF\u2014reveals that this proposed method has the smallest imputation error. As the missing rate increases, the mean squared error of the Random Forest method and the other two methods also increases, with a maximum difference of about 70%. This indicates that the random forest method is suitable for imputing monitoring data.<\/jats:p>","DOI":"10.3390\/s24051560","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T09:26:17Z","timestamp":1709112377000},"page":"1560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Data Imputation of Soil Pressure on Shield Tunnel Lining Based on Random Forest Model"],"prefix":"10.3390","volume":"24","author":[{"given":"Min","family":"Wang","sequence":"first","affiliation":[{"name":"Polytechnic Institute, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0012-5842","authenticated-orcid":false,"given":"Xiao-Wei","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Hong","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Dian","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1298-1710","authenticated-orcid":false,"given":"Yang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Sun","sequence":"additional","affiliation":[{"name":"China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105006","DOI":"10.1016\/j.autcon.2023.105006","article-title":"Deep learning-based prediction of steady surface settlement due to shield tunnelling","volume":"154","author":"Wang","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_2","first-page":"17","article-title":"A long-term tunnel settlement prediction model based on BO-GPBE with SHM data","volume":"33","author":"Ding","year":"2024","journal-title":"Smart Struct. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.eng.2020.02.016","article-title":"Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network","volume":"7","author":"Elbaz","year":"2021","journal-title":"Engineering"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4941","DOI":"10.1007\/s11440-023-01859-8","article-title":"Significance and formulation of ground loss in tunneling-induced settlement prediction: A data-driven study","volume":"18","author":"Ren","year":"2023","journal-title":"Acta Geotech."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moghtader, T., Sharafati, A., Naderpour, H., and Gharouni Nik, M. (2023). Estimating Maximum Surface Settlement Caused by EPB Shield Tunneling Utilizing an Intelligent Approach. Buildings, 13.","DOI":"10.3390\/buildings13041051"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s40537-021-00516-9","article-title":"A survey on missing data in machine learning","volume":"8","author":"Emmanuel","year":"2021","journal-title":"J. Big Data"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Psuj, G. (2018). Multi-sensor data integration using deep learning for characterization of defects in steel elements. Sensors, 18.","DOI":"10.3390\/s18010292"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104668","DOI":"10.1016\/j.tust.2022.104668","article-title":"Automated crack classification for the CERN underground tunnel infrastructure using deep learning","volume":"131","author":"Osborne","year":"2023","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1093\/biomet\/63.3.581","article-title":"Inference and missing data","volume":"63","author":"Rubin","year":"1976","journal-title":"Biometrika"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Little, R.J., and Rubin, D.B. (2019). Statistical Analysis with Missing Data, John Wiley & Sons.","DOI":"10.1002\/9781119482260"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/32.962560","article-title":"Software cost estimation with incomplete data","volume":"27","author":"Strike","year":"2001","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1007\/s13349-023-00714-4","article-title":"Settlement prediction of existing metro induced by new metro construction with machine learning based on SHM data: A comparative study","volume":"13","author":"Ding","year":"2023","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1177\/1094428114548590","article-title":"Missing data: Five practical guidelines","volume":"17","author":"Newman","year":"2014","journal-title":"Organ. Res. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","article-title":"Missing value imputation: A review and analysis of the literature (2006\u20132017)","volume":"53","author":"Lin","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_16","unstructured":"Enders, C.K. (1999). The Relative Performance of Full-Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models, The University of Nebraska-Lincoln."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1056\/NEJMoa2012410","article-title":"Observational study of hydroxychloroquine in hospitalized patients with COVID-19","volume":"382","author":"Geleris","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1001\/jama.2013.2024","article-title":"Effect of phosphodiesterase-5 inhibition on exercise capacity and clinical status in heart failure with preserved ejection fraction: A randomized clinical trial","volume":"309","author":"Redfield","year":"2013","journal-title":"JAMA"},{"key":"ref_19","first-page":"1","article-title":"Estimation of settlement of pile group in clay using soft computing techniques","volume":"9","author":"Khatti","year":"2023","journal-title":"Geotech. Geol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Davey, A. (2009). Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach, Routledge.","DOI":"10.4324\/9780203866955"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.undsp.2023.05.010","article-title":"Real-time assessment of tunnelling-induced damage to structures within the building information modelling framework","volume":"14","author":"Gamra","year":"2024","journal-title":"Undergr. Space"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1080\/0952813X.2021.1948920","article-title":"An investigation of solutions for handling incomplete online review datasets with missing values","volume":"34","author":"Hu","year":"2022","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.eswa.2017.07.026","article-title":"An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers","volume":"89","author":"Garciarena","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124670","DOI":"10.1016\/j.jhydrol.2020.124670","article-title":"A survey on river water quality modelling using artificial intelligence models: 2000\u20132020","volume":"585","author":"Tung","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116452","DOI":"10.1016\/j.apenergy.2021.116452","article-title":"A review of machine learning in building load prediction","volume":"285","author":"Zhang","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.atmosenv.2019.01.027","article-title":"Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China","volume":"202","author":"Chen","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v074.i07","article-title":"Imputation with the R Package VIM","volume":"74","author":"Kowarik","year":"2016","journal-title":"J. Stat. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1139\/cjfr-2013-0401","article-title":"Mapping attributes of Canada\u2019s forests at moderate resolution through kNN and MODIS imagery","volume":"44","author":"Beaudoin","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jakobsen, J.C., Gluud, C., Wetterslev, J., and Winkel, P. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials\u2014A practical guide with flowcharts. BMC Med. Res. Methodol., 17.","DOI":"10.1186\/s12874-017-0442-1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1177\/0962280210395740","article-title":"Review of inverse probability weighting for dealing with missing data","volume":"22","author":"Seaman","year":"2013","journal-title":"Stat. Methods Med. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"060901","DOI":"10.1117\/1.OE.59.6.060901","article-title":"Fiber Bragg grating sensors for monitoring of physical parameters: A comprehensive review","volume":"59","author":"Sahota","year":"2020","journal-title":"Opt. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7394","DOI":"10.3390\/s140407394","article-title":"Fiber Bragg grating sensors toward structural health monitoring in composite materials: Challenges and solutions","volume":"14","author":"Kinet","year":"2014","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Genuer, R., Poggi, J.M., Genuer, R., and Poggi, J.M. (2020). Random Forests, Springer International Publishing.","DOI":"10.1007\/978-3-030-56485-8"},{"key":"ref_34","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:06:33Z","timestamp":1760105193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051560"],"URL":"https:\/\/doi.org\/10.3390\/s24051560","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]}}}