Energy-driven TBM health status estimation with a hybrid deep learning approach

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dc.authoridTapkin, Serkan/0000-0003-1417-9972
dc.authoridZhang, Limao/0000-0002-7245-3741
dc.contributor.authorLi, Yongsheng
dc.contributor.authorZhang, Limao
dc.contributor.authorPan, Yue
dc.contributor.authorTapkin, Serkan
dc.contributor.authorSong, Xieqing
dc.date.accessioned2024-10-04T18:49:43Z
dc.date.available2024-10-04T18:49:43Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractThis study provides a hybrid deep learning approach that enables the researchers to estimate the Tunnel Boring Machine (TBM) health status during excavation process using energy consumption data. By analyzing the energy forms and paths in the tunneling process, the key energy factors' consumption performance behavior affecting TBM safety is identified. The Ensemble Empirical Mode Decomposition (EEMD) method and a Convolution neural network-Long short term memory-Depth Neural Network (CNN-LSTM-DNN, CLDNN) model are integrated for Health Performance Parameter (HPP) prediction. A classification method is constructed by the Generative Adversarial Imputation Networks (GAIN) model for health status estimation. The SHapley Additive exPlanations (SHAP) method is performed to quantify the relationships between energy consumption and system health status. A tunneling case in Singapore is used to test the effectiveness of the developed methodology. It is found that (1) the proposed method provides TBM cutter wear and cooling oil prediction with a R-square (R2) of 0.8591 and 0.8727, respectively, and (2) classification results indicate that the estimation F1-score of 0.9863 can be achieved based on health status evaluation indicator (HSEI). The novelty of the proposed approach lies in its capabilities of (1) fully extracting and utilizing the energy consumption information in every regard to predict TBM health performance; (2) providing a good one-step-ahead health status estimation for the TBM excavation process. Therefore, this study proposes a feasible direction for the application of energy consumption data in TBM health status estimation.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [72271101, 72201171]; Outstanding Youth Fund of Hubei Province [2022CFA062]; Start-Up Grant at Huazhong University of Science and Technologyen_US
dc.description.sponsorshipThis work is supported in part by the National Natural Science Foundation of China (No. 72271101, No. 72201171), the Outstanding Youth Fund of Hubei Province (No. 2022CFA062), and the Start-Up Grant at Huazhong University of Science and Technology.en_US
dc.identifier.doi10.1016/j.eswa.2024.123701
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85189102369en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.123701
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3264
dc.identifier.volume249en_US
dc.identifier.wosWOS:001218647100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnergy-driven estimationen_US
dc.subjectTunnel boring machineen_US
dc.subjectHealth status estimationen_US
dc.subjectExcavation processen_US
dc.subjectDeep learningen_US
dc.titleEnergy-driven TBM health status estimation with a hybrid deep learning approachen_US
dc.typeArticleen_US

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