Multi-step prediction model enhanced by adaptive denoising and encoder-decoder for shield machine cutterhead torque in complex conditions
•A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working condi...
Uloženo v:
| Vydáno v: | Tunnelling and underground space technology Ročník 158; s. 106398 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.04.2025
|
| Témata: | |
| ISSN: | 0886-7798 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working conditions.
Cutterhead torque reflects the obstruction extent of geological environment on the shield machine, and its prediction can assist operators to adjust control parameters to improve construction efficiency and avoid machine jamming. However, tunneling in complex geological or working conditions often results in high cutterhead torque fluctuations and noise, which seriously affects the accuracy of torque prediction. This study proposes a multi-step prediction model for cutterhead torque enhanced by adaptive denoising and encoder-decoder. In this model, a novel adaptive denoising method for cutterhead torque is employed to improve prediction accuracy under complex conditions. Moreover, by introducing encoder-decoder method, the processing capability for multi-time dimensional data and multi-step prediction performance of LSTM neural networks are further improved. The effectiveness of proposed model is verified through an application to the Heyan Road River Crossing project. The results of this study can assist operators in achieving precise adjustment of control parameters under complex conditions. |
|---|---|
| AbstractList | •A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working conditions.
Cutterhead torque reflects the obstruction extent of geological environment on the shield machine, and its prediction can assist operators to adjust control parameters to improve construction efficiency and avoid machine jamming. However, tunneling in complex geological or working conditions often results in high cutterhead torque fluctuations and noise, which seriously affects the accuracy of torque prediction. This study proposes a multi-step prediction model for cutterhead torque enhanced by adaptive denoising and encoder-decoder. In this model, a novel adaptive denoising method for cutterhead torque is employed to improve prediction accuracy under complex conditions. Moreover, by introducing encoder-decoder method, the processing capability for multi-time dimensional data and multi-step prediction performance of LSTM neural networks are further improved. The effectiveness of proposed model is verified through an application to the Heyan Road River Crossing project. The results of this study can assist operators in achieving precise adjustment of control parameters under complex conditions. |
| ArticleNumber | 106398 |
| Author | Wang, Yuan Zhou, Kun Xu, Deming Huang, Jingqi Xu, Shujun |
| Author_xml | – sequence: 1 givenname: Deming surname: Xu fullname: Xu, Deming organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 2 givenname: Yuan surname: Wang fullname: Wang, Yuan organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 3 givenname: Jingqi surname: Huang fullname: Huang, Jingqi email: huangjingqi11@163.com organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 4 givenname: Shujun surname: Xu fullname: Xu, Shujun organization: China Railway 14th Bureau Group Co, Ltd, Nanjing, Jiangsu 210098, China – sequence: 5 givenname: Kun surname: Zhou fullname: Zhou, Kun organization: China Railway 14th Bureau Group Co, Ltd, Nanjing, Jiangsu 210098, China |
| BookMark | eNp9kEtOwzAQhr0oEi1wAVa-QIrtNI9KbFDFSwKxgbXleCZ0qtQOtlvRI3BrEsqKBatfGs03j2_GJs47ZOxSirkUsrzazNMuprkSqhgKZb6sJ2wq6rrMqmpZn7JZjBshRKHUcsq-nnddoiwm7HkfEMgm8o5vPWDH0a2Nswi8OXADpk-0Rw7oPEVy79w4GFrs0BoywJ_krQ88rgk74Ftj1-SQ211KGNZogCcfPnbIyXHrt32Hn0M6oHFlPGcnrekiXvzmGXu7u31dPWRPL_ePq5unzOZCpMwAFE3ewMK0qrL5Uiq5kFVVWyWaXDSikaUqi9pikbfQtmBM0diqXUiBFVSmyM-YOs61wccYsNV9oK0JBy2FHgXqjR4F6lGgPgocoPoPZCmZ8e4UDHX_o9dHFIen9oRBR0s4aqWANmnw9B_-DRLglUc |
| CitedBy_id | crossref_primary_10_1016_j_iintel_2025_100166 crossref_primary_10_1016_j_conbuildmat_2025_141234 crossref_primary_10_1016_j_soildyn_2025_109443 crossref_primary_10_1016_j_tust_2025_107123 crossref_primary_10_1016_j_rineng_2025_105898 crossref_primary_10_1016_j_tust_2025_107044 crossref_primary_10_1016_j_tust_2025_106660 |
| Cites_doi | 10.1016/j.ymssp.2020.107386 10.1016/j.tust.2013.09.004 10.1016/j.sigpro.2016.02.011 10.1016/j.autcon.2018.03.030 10.1016/j.bspc.2020.102337 10.3390/electronics7120450 10.1016/j.jrmge.2022.03.002 10.1016/j.ymssp.2022.109148 10.1016/B978-0-12-820127-5.00004-6 10.1016/j.tust.2020.103699 10.1016/j.autcon.2011.04.010 10.1016/j.autcon.2021.103741 10.1016/j.isatra.2020.10.022 10.1007/s00603-021-02714-6 10.1016/j.autcon.2016.12.004 10.1016/j.knosys.2021.107213 10.1016/j.asoc.2022.108686 10.1016/j.measurement.2020.107901 10.1016/j.autcon.2018.11.013 10.1162/neco.1997.9.8.1735 10.1016/j.jhydrol.2020.124631 10.1016/j.simpat.2010.03.005 10.1016/j.tust.2019.103002 10.1016/j.jhydrol.2021.126378 10.1016/j.jhydrol.2021.126526 10.1016/j.eng.2020.02.016 10.1016/j.tust.2018.01.025 10.1007/978-3-7091-8452-3_3 10.1016/j.undsp.2023.05.006 10.1016/j.jrmge.2021.11.008 10.1016/j.ymssp.2021.108312 10.1109/TSP.2013.2288675 10.1016/j.tust.2020.103593 10.1016/j.tust.2020.103677 10.1016/j.optcom.2014.03.083 10.1016/j.energy.2020.118371 10.1016/j.ijepes.2019.105771 10.1016/j.ymssp.2020.106664 10.1016/j.measurement.2021.109277 10.1016/j.tust.2017.09.016 10.1016/j.apenergy.2021.117461 10.1016/j.tust.2016.04.002 10.1080/21642583.2019.1708830 10.1016/j.measurement.2021.109815 10.1162/neco_a_01199 10.1016/j.measurement.2019.106941 10.1016/j.autcon.2018.05.020 10.1016/j.isatra.2020.12.041 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Ltd |
| Copyright_xml | – notice: 2025 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.tust.2025.106398 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | 10_1016_j_tust_2025_106398 S0886779825000367 |
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29Q 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXKI AAXUO ABFNM ABJNI ABMAC ABQEM ABQYD ABWVN ABXDB ACDAQ ACGFS ACIWK ACLVX ACNNM ACRLP ACRPL ACSBN ADBBV ADEZE ADMUD ADNMO ADTZH AEBSH AECPX AEIPS AEKER AENEX AFJKZ AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ASPBG ATOGT AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HVGLF HZ~ IHE IMUCA J1W JJJVA KOM LY3 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SEP SES SET SEW SPC SPCBC SSE SST SSZ T5K WUQ ZMT ~02 ~G- 9DU AATTM AAYWO AAYXX ACLOT ACVFH ADCNI AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c300t-add5b3bd4af27c3912141778c20b30b0b162658ce53fdffdaa5bc7f410e7d7a53 |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001401883500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0886-7798 |
| IngestDate | Sat Nov 29 08:10:02 EST 2025 Tue Nov 18 22:40:01 EST 2025 Sat Mar 08 15:44:44 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Shield machine Long short-term memory Cutterhead torque Encoder-decoder model Multi-step prediction |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-add5b3bd4af27c3912141778c20b30b0b162658ce53fdffdaa5bc7f410e7d7a53 |
| ParticipantIDs | crossref_primary_10_1016_j_tust_2025_106398 crossref_citationtrail_10_1016_j_tust_2025_106398 elsevier_sciencedirect_doi_10_1016_j_tust_2025_106398 |
| PublicationCentury | 2000 |
| PublicationDate | April 2025 2025-04-00 |
| PublicationDateYYYYMMDD | 2025-04-01 |
| PublicationDate_xml | – month: 04 year: 2025 text: April 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Tunnelling and underground space technology |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Dragomiretskiy, Zosso (b0040) 2014; 62 Elbaz, Shen, Zhou, Yin, Lyu (b0045) 2021; 7 Zhao, Pan, Wang, Yu (b0250) 2017; 76 Huang (b0090) 2020 Rostami, Ozdemir (b0155) 1993 Shi, Yang, Gong, Wang (b0165) 2011; 20 Avunduk, Copur (b0015) 2018; 71 Wang, Yang, Gong, Du (b0190) 2018; 93 Fu, Feng, Zhang (b0055) 2022; 120 Hochreiter, Schmidhuber (b0085) 1997; 9 Xue, Shen (b0200) 2020; 8 Huo, Xu, Meng, Li, Dong, Wang (b0105) 2020; 140 Zhao, Gong, Tian, Zhou, Jiang (b0255) 2019; 91 Krause, H. 1976. Geologische Erfahrungen beim Einsatz von Tunnelvortriebsmaschinen in Baden-Württemberg. Gao, Shi, Song, Zhang, Zhang (b0065) 2019; 98 Yin, Zhang, Wang, Zhang, Xia, Jin (b0225) 2021; 598 Zhang, Li, Zhang (b0235) 2020; 213 Butusov, Karimov, Voznesenskiy, Kaplun, Andreev, Ostrovskii (b0025) 2018; 7 Chen, Wang, Jiao (b0030) 2021 Gai, Shen, Hu, Wang (b0060) 2020; 162 Zhang, Wu, Chen, Dai, Meng, Wang (b0240) 2020; 106 Qin, Shi, Tao, Yu, Jin, Xiao, Zhang, Liu (b0150) 2022; 175 Xu, Liu, Wang, Wang (b0195) 2021; 109 Yan, Shen, Zhou, Chen (b0210) 2022 Shi, Qin, Tao, Liu (b0160) 2021; 228 Kang, Chen, Li, Luo, Liu (b0115) 2023; 13 Alizadeh, Ghaderi Bafti, Kamangir, Zhang, Wright, Franz (b0005) 2021; 601 Tian, Cao, Liang, Zhang, Yi, Wang, Cheng (b0180) 2014; 325 Wang, Xie, Shahrour, Huang (b0185) 2021; 128 Yao, Zhou, Wang, Hu, Liu (b0220) 2021; 177 Yao, Wang, Liu, Wang, Shang (b0215) 2021; 109 Feng, Wei, Qi, Pei, Wang (b0050) 2021; 184 Jin, Qin, Tao, Liu (b0110) 2022; 165 Zhang, Yu, Liu, Lai (b0245) 2010; 18 Ates, Bilgin, Copur (b0010) 2014; 40 Géron (b0070) 2019 Qin, Shi, Tao, Yu, Jin, Lei, Liu (b0145) 2021; 151 Yan, Shen, Zhou (b0205) 2022 Gong, Yin, Ma, Zhao (b0075) 2016; 57 Sun, Shi, Zhang, Zhao, Song (b0175) 2018; 92 Yu, Si, Hu, Zhang (b0230) 2019; 31 Dibaj, Hassannejad, Ettefagh, Ehghaghi (b0035) 2021; 114 Bai, Liu, Ding, Ma (b0020) 2021; 301 Kaur, Bisht, Singh, Joshi (b0125) 2021; 65 Sun, Lin, Jiang, Wang, Chen, Zhong (b0170) 2020; 118 Kao, Zhou, Chang, Chang (b0120) 2020; 583 Huang, Zhang, Liu, Liu, Liu, Wang, Yin (b0100) 2022; 14 Gu, Chen, Hong, Wang, Wu (b0080) 2020; 149 Huang, Ninić, Zhang (b0095) 2021; 108 Springer, 49-60. Zhou, Zhai (b0260) 2018; 74 Liu, Yang, Li, Yin (b0140) 2016; 125 Liu, Liao, Men, Xing, Liu, Sun (b0135) 2022; 55 Butusov (10.1016/j.tust.2025.106398_b0025) 2018; 7 Liu (10.1016/j.tust.2025.106398_b0140) 2016; 125 Xue (10.1016/j.tust.2025.106398_b0200) 2020; 8 Yan (10.1016/j.tust.2025.106398_b0205) 2022 Jin (10.1016/j.tust.2025.106398_b0110) 2022; 165 Zhang (10.1016/j.tust.2025.106398_b0240) 2020; 106 Elbaz (10.1016/j.tust.2025.106398_b0045) 2021; 7 Fu (10.1016/j.tust.2025.106398_b0055) 2022; 120 Bai (10.1016/j.tust.2025.106398_b0020) 2021; 301 Gao (10.1016/j.tust.2025.106398_b0065) 2019; 98 Feng (10.1016/j.tust.2025.106398_b0050) 2021; 184 Kang (10.1016/j.tust.2025.106398_b0115) 2023; 13 Qin (10.1016/j.tust.2025.106398_b0150) 2022; 175 Shi (10.1016/j.tust.2025.106398_b0160) 2021; 228 Sun (10.1016/j.tust.2025.106398_b0170) 2020; 118 Chen (10.1016/j.tust.2025.106398_b0030) 2021 10.1016/j.tust.2025.106398_b0130 Zhang (10.1016/j.tust.2025.106398_b0245) 2010; 18 Zhao (10.1016/j.tust.2025.106398_b0255) 2019; 91 Yu (10.1016/j.tust.2025.106398_b0230) 2019; 31 Gai (10.1016/j.tust.2025.106398_b0060) 2020; 162 Alizadeh (10.1016/j.tust.2025.106398_b0005) 2021; 601 Kaur (10.1016/j.tust.2025.106398_b0125) 2021; 65 Wang (10.1016/j.tust.2025.106398_b0190) 2018; 93 Rostami (10.1016/j.tust.2025.106398_b0155) 1993 Zhao (10.1016/j.tust.2025.106398_b0250) 2017; 76 Yan (10.1016/j.tust.2025.106398_b0210) 2022 Géron (10.1016/j.tust.2025.106398_b0070) 2019 Hochreiter (10.1016/j.tust.2025.106398_b0085) 1997; 9 Zhou (10.1016/j.tust.2025.106398_b0260) 2018; 74 Shi (10.1016/j.tust.2025.106398_b0165) 2011; 20 Dibaj (10.1016/j.tust.2025.106398_b0035) 2021; 114 Dragomiretskiy (10.1016/j.tust.2025.106398_b0040) 2014; 62 Qin (10.1016/j.tust.2025.106398_b0145) 2021; 151 Tian (10.1016/j.tust.2025.106398_b0180) 2014; 325 Xu (10.1016/j.tust.2025.106398_b0195) 2021; 109 Yin (10.1016/j.tust.2025.106398_b0225) 2021; 598 Gu (10.1016/j.tust.2025.106398_b0080) 2020; 149 Gong (10.1016/j.tust.2025.106398_b0075) 2016; 57 Huang (10.1016/j.tust.2025.106398_b0095) 2021; 108 Huang (10.1016/j.tust.2025.106398_b0090) 2020 Huang (10.1016/j.tust.2025.106398_b0100) 2022; 14 Kao (10.1016/j.tust.2025.106398_b0120) 2020; 583 Wang (10.1016/j.tust.2025.106398_b0185) 2021; 128 Liu (10.1016/j.tust.2025.106398_b0135) 2022; 55 Sun (10.1016/j.tust.2025.106398_b0175) 2018; 92 Avunduk (10.1016/j.tust.2025.106398_b0015) 2018; 71 Yao (10.1016/j.tust.2025.106398_b0220) 2021; 177 Huo (10.1016/j.tust.2025.106398_b0105) 2020; 140 Yao (10.1016/j.tust.2025.106398_b0215) 2021; 109 Zhang (10.1016/j.tust.2025.106398_b0235) 2020; 213 Ates (10.1016/j.tust.2025.106398_b0010) 2014; 40 |
| References_xml | – volume: 91 year: 2019 ident: b0255 article-title: Torque fluctuation fluctuation analysis and penetration prediction of EPB TBM in rock-soil interface mixed ground publication-title: Tunn. Undergr. Space Technol. – volume: 162 year: 2020 ident: b0060 article-title: An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing publication-title: Measurement – volume: 71 start-page: 340 year: 2018 end-page: 353 ident: b0015 article-title: Empirical modeling for predicting excavation performance of EPB TBM based on soil properties publication-title: Tunn. Undergr. Space Technol. – volume: 213 year: 2020 ident: b0235 article-title: Short-term wind power forecasting approach based on Seq2Seq model using NWP data publication-title: Energy – reference: . Springer, 49-60. – volume: 18 start-page: 1019 year: 2010 end-page: 1031 ident: b0245 article-title: Dynamic characteristic analysis of TBM tunnelling in mixed-face conditions publication-title: Simul. Model. Pract. Theory – volume: 325 start-page: 54 year: 2014 end-page: 59 ident: b0180 article-title: Improved empirical mode decomposition based denoising method for lidar signals publication-title: Opt. Commun. – volume: 128 year: 2021 ident: b0185 article-title: Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions publication-title: Autom. Constr. – volume: 140 year: 2020 ident: b0105 article-title: Coupled modeling and dynamic characteristics of TBM cutterhead system under uncertain factors publication-title: Mech. Syst. Sig. Process. – volume: 165 year: 2022 ident: b0110 article-title: An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network publication-title: Mech. Syst. Sig. Process. – volume: 57 start-page: 4 year: 2016 end-page: 17 ident: b0075 article-title: TBM tunnelling under adverse geological conditions: An overview publication-title: Tunn. Undergr. Space Technol. – volume: 55 start-page: 1481 year: 2022 end-page: 1498 ident: b0135 article-title: Field Monitoring of TBM Vibration During Excavating Changing Stratum: Patterns and Ground Identification publication-title: Rock Mech. Rock Eng. – volume: 20 start-page: 1087 year: 2011 end-page: 1095 ident: b0165 article-title: Determination of the cutterhead torque for EPB shield tunneling machine publication-title: Autom. Constr. – volume: 114 start-page: 413 year: 2021 end-page: 433 ident: b0035 article-title: Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold publication-title: ISA Trans. – volume: 62 start-page: 531 year: 2014 end-page: 544 ident: b0040 article-title: Variational mode decomposition publication-title: IEEE Trans. Signal Process. – volume: 65 year: 2021 ident: b0125 article-title: EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression publication-title: Biomed. Signal Process. Control – volume: 74 start-page: 217 year: 2018 end-page: 229 ident: b0260 article-title: Estimation of the cutterhead torque for earth pressure balance TBM under mixed-face conditions publication-title: Tunn. Undergr. Space Technol. – volume: 7 start-page: 238 year: 2021 end-page: 251 ident: b0045 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 publication-title: Engineering – volume: 108 year: 2021 ident: b0095 article-title: BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives publication-title: Tunn. Undergr. Space Technol. – volume: 93 start-page: 192 year: 2018 end-page: 199 ident: b0190 article-title: Pose and trajectory control of shield tunneling machine in complicated stratum publication-title: Autom. Constr. – volume: 40 start-page: 46 year: 2014 end-page: 63 ident: b0010 article-title: Estimating torque, thrust and other design parameters of different type TBMs with some criticism to TBMs used in Turkish tunneling projects publication-title: Tunn. Undergr. Space Technol. – year: 2022 ident: b0205 article-title: Identification of geological characteristics from construction parameters during shield tunnelling publication-title: Acta Geotech. – volume: 598 year: 2021 ident: b0225 article-title: Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model publication-title: J. Hydrol. – volume: 601 year: 2021 ident: b0005 article-title: A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction publication-title: J. Hydrol. – volume: 184 year: 2021 ident: b0050 article-title: A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm publication-title: Measurement – start-page: 119 year: 2021 end-page: 161 ident: b0030 article-title: Shield machine type selection publication-title: . Elsevier – volume: 109 year: 2021 ident: b0195 article-title: Prediction of tunnel boring machine operating parameters using various machine learning algorithms publication-title: Tunn. Undergr. Space Technol. – volume: 109 start-page: 315 year: 2021 end-page: 326 ident: b0215 article-title: An improved low-frequency noise reduction method in shock wave pressure measurement based on mode classification and recursion extraction publication-title: ISA Trans. – year: 2019 ident: b0070 article-title: Hands-on machine learning with Scikit-Learn, Keras publication-title: And TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – start-page: 600 year: 2020 end-page: 607 ident: b0090 publication-title: Real-Time Aarly Warning of Clogging Risk in Slurry Shield Tunneling: A Self-Updating Machine Learning Approach – volume: 118 year: 2020 ident: b0170 article-title: A novel adaptive single-phase reclosure scheme based on improved variational mode decomposition and energy entropy publication-title: Int. J. Electr. Power Energy Syst. – year: 2022 ident: b0210 article-title: Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm publication-title: J. Rock Mech. Geotech. Eng. – volume: 120 year: 2022 ident: b0055 article-title: Data-driven estimation of TBM performance in soft soils using density-based spatial clustering and random forest publication-title: Appl. Soft Comput. – volume: 125 start-page: 349 year: 2016 end-page: 364 ident: b0140 article-title: Variational mode decomposition denoising combined the detrended fluctuation analysis publication-title: Signal Process. – volume: 7 start-page: 450 year: 2018 ident: b0025 article-title: Filtering Techniques for Chaotic Signal Processing publication-title: Electronics – volume: 13 start-page: 335 year: 2023 end-page: 350 ident: b0115 article-title: Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling publication-title: Underground Space – volume: 151 year: 2021 ident: b0145 article-title: Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network publication-title: Mech. Syst. Sig. Process. – reference: Krause, H. 1976. Geologische Erfahrungen beim Einsatz von Tunnelvortriebsmaschinen in Baden-Württemberg. – volume: 106 year: 2020 ident: b0240 article-title: A critical evaluation of machine learning and deep learning in shield-ground interaction prediction publication-title: Tunn. Undergr. Space Technol. – volume: 92 start-page: 23 year: 2018 end-page: 34 ident: b0175 article-title: Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data publication-title: Autom. Constr. – volume: 31 start-page: 1235 year: 2019 end-page: 1270 ident: b0230 article-title: A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures publication-title: Neural Comput. – volume: 583 year: 2020 ident: b0120 article-title: Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting publication-title: J. Hydrol. – volume: 8 start-page: 22 year: 2020 end-page: 34 ident: b0200 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. – volume: 177 year: 2021 ident: b0220 article-title: An adaptive seismic signal denoising method based on variational mode decomposition publication-title: Measurement – volume: 301 year: 2021 ident: b0020 article-title: Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition publication-title: Appl. Energy – volume: 98 start-page: 225 year: 2019 end-page: 235 ident: b0065 article-title: Recurrent neural networks for real-time prediction of TBM operating parameters publication-title: Autom. Constr. – start-page: 793 year: 1993 end-page: 809 ident: b0155 article-title: New model for performance production of hard rock TBMs – volume: 14 year: 2022 ident: b0100 article-title: A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm publication-title: J. Rock Mech. Geotech. Eng. – volume: 175 year: 2022 ident: b0150 article-title: An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine publication-title: Mech. Syst. Sig. Process. – volume: 228 year: 2021 ident: b0160 article-title: A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque publication-title: Knowl.-Based Syst. – volume: 76 start-page: 97 year: 2017 end-page: 107 ident: b0250 article-title: Dynamics research on grouping characteristics of a shield tunneling machine's thrust system publication-title: Autom. Constr. – volume: 149 year: 2020 ident: b0080 article-title: Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator publication-title: Measurement – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: b0085 article-title: Long short-term memory publication-title: Neural Comput. – volume: 151 year: 2021 ident: 10.1016/j.tust.2025.106398_b0145 article-title: Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2020.107386 – volume: 40 start-page: 46 year: 2014 ident: 10.1016/j.tust.2025.106398_b0010 article-title: Estimating torque, thrust and other design parameters of different type TBMs with some criticism to TBMs used in Turkish tunneling projects publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2013.09.004 – volume: 125 start-page: 349 year: 2016 ident: 10.1016/j.tust.2025.106398_b0140 article-title: Variational mode decomposition denoising combined the detrended fluctuation analysis publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.02.011 – volume: 92 start-page: 23 year: 2018 ident: 10.1016/j.tust.2025.106398_b0175 article-title: Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data publication-title: Autom. Constr. doi: 10.1016/j.autcon.2018.03.030 – volume: 65 year: 2021 ident: 10.1016/j.tust.2025.106398_b0125 article-title: EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2020.102337 – volume: 7 start-page: 450 year: 2018 ident: 10.1016/j.tust.2025.106398_b0025 article-title: Filtering Techniques for Chaotic Signal Processing publication-title: Electronics doi: 10.3390/electronics7120450 – year: 2022 ident: 10.1016/j.tust.2025.106398_b0210 article-title: Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm publication-title: J. Rock Mech. Geotech. Eng. doi: 10.1016/j.jrmge.2022.03.002 – volume: 175 year: 2022 ident: 10.1016/j.tust.2025.106398_b0150 article-title: An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2022.109148 – start-page: 119 year: 2021 ident: 10.1016/j.tust.2025.106398_b0030 article-title: Shield machine type selection publication-title: Shield Construction Techniques in Tunneling. Elsevier doi: 10.1016/B978-0-12-820127-5.00004-6 – volume: 109 year: 2021 ident: 10.1016/j.tust.2025.106398_b0195 article-title: Prediction of tunnel boring machine operating parameters using various machine learning algorithms publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2020.103699 – volume: 20 start-page: 1087 year: 2011 ident: 10.1016/j.tust.2025.106398_b0165 article-title: Determination of the cutterhead torque for EPB shield tunneling machine publication-title: Autom. Constr. doi: 10.1016/j.autcon.2011.04.010 – volume: 128 year: 2021 ident: 10.1016/j.tust.2025.106398_b0185 article-title: Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions publication-title: Autom. Constr. doi: 10.1016/j.autcon.2021.103741 – volume: 109 start-page: 315 year: 2021 ident: 10.1016/j.tust.2025.106398_b0215 article-title: An improved low-frequency noise reduction method in shock wave pressure measurement based on mode classification and recursion extraction publication-title: ISA Trans. doi: 10.1016/j.isatra.2020.10.022 – year: 2019 ident: 10.1016/j.tust.2025.106398_b0070 article-title: Hands-on machine learning with Scikit-Learn, Keras – volume: 55 start-page: 1481 year: 2022 ident: 10.1016/j.tust.2025.106398_b0135 article-title: Field Monitoring of TBM Vibration During Excavating Changing Stratum: Patterns and Ground Identification publication-title: Rock Mech. Rock Eng. doi: 10.1007/s00603-021-02714-6 – start-page: 600 year: 2020 ident: 10.1016/j.tust.2025.106398_b0090 – volume: 76 start-page: 97 year: 2017 ident: 10.1016/j.tust.2025.106398_b0250 article-title: Dynamics research on grouping characteristics of a shield tunneling machine's thrust system publication-title: Autom. Constr. doi: 10.1016/j.autcon.2016.12.004 – volume: 228 year: 2021 ident: 10.1016/j.tust.2025.106398_b0160 article-title: A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.107213 – volume: 120 year: 2022 ident: 10.1016/j.tust.2025.106398_b0055 article-title: Data-driven estimation of TBM performance in soft soils using density-based spatial clustering and random forest publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108686 – volume: 162 year: 2020 ident: 10.1016/j.tust.2025.106398_b0060 article-title: An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing publication-title: Measurement doi: 10.1016/j.measurement.2020.107901 – volume: 98 start-page: 225 year: 2019 ident: 10.1016/j.tust.2025.106398_b0065 article-title: Recurrent neural networks for real-time prediction of TBM operating parameters publication-title: Autom. Constr. doi: 10.1016/j.autcon.2018.11.013 – volume: 9 start-page: 1735 year: 1997 ident: 10.1016/j.tust.2025.106398_b0085 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 583 year: 2020 ident: 10.1016/j.tust.2025.106398_b0120 article-title: Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.124631 – volume: 18 start-page: 1019 year: 2010 ident: 10.1016/j.tust.2025.106398_b0245 article-title: Dynamic characteristic analysis of TBM tunnelling in mixed-face conditions publication-title: Simul. Model. Pract. Theory doi: 10.1016/j.simpat.2010.03.005 – volume: 91 year: 2019 ident: 10.1016/j.tust.2025.106398_b0255 article-title: Torque fluctuation fluctuation analysis and penetration prediction of EPB TBM in rock-soil interface mixed ground publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2019.103002 – volume: 598 year: 2021 ident: 10.1016/j.tust.2025.106398_b0225 article-title: Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2021.126378 – volume: 601 year: 2021 ident: 10.1016/j.tust.2025.106398_b0005 article-title: A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2021.126526 – volume: 7 start-page: 238 year: 2021 ident: 10.1016/j.tust.2025.106398_b0045 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 publication-title: Engineering doi: 10.1016/j.eng.2020.02.016 – volume: 74 start-page: 217 year: 2018 ident: 10.1016/j.tust.2025.106398_b0260 article-title: Estimation of the cutterhead torque for earth pressure balance TBM under mixed-face conditions publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2018.01.025 – ident: 10.1016/j.tust.2025.106398_b0130 doi: 10.1007/978-3-7091-8452-3_3 – volume: 13 start-page: 335 year: 2023 ident: 10.1016/j.tust.2025.106398_b0115 article-title: Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling publication-title: Underground Space doi: 10.1016/j.undsp.2023.05.006 – volume: 14 year: 2022 ident: 10.1016/j.tust.2025.106398_b0100 article-title: A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm publication-title: J. Rock Mech. Geotech. Eng. doi: 10.1016/j.jrmge.2021.11.008 – volume: 165 year: 2022 ident: 10.1016/j.tust.2025.106398_b0110 article-title: An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2021.108312 – volume: 62 start-page: 531 year: 2014 ident: 10.1016/j.tust.2025.106398_b0040 article-title: Variational mode decomposition publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2013.2288675 – volume: 106 year: 2020 ident: 10.1016/j.tust.2025.106398_b0240 article-title: A critical evaluation of machine learning and deep learning in shield-ground interaction prediction publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2020.103593 – volume: 108 year: 2021 ident: 10.1016/j.tust.2025.106398_b0095 article-title: BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2020.103677 – volume: 325 start-page: 54 year: 2014 ident: 10.1016/j.tust.2025.106398_b0180 article-title: Improved empirical mode decomposition based denoising method for lidar signals publication-title: Opt. Commun. doi: 10.1016/j.optcom.2014.03.083 – volume: 213 year: 2020 ident: 10.1016/j.tust.2025.106398_b0235 article-title: Short-term wind power forecasting approach based on Seq2Seq model using NWP data publication-title: Energy doi: 10.1016/j.energy.2020.118371 – volume: 118 year: 2020 ident: 10.1016/j.tust.2025.106398_b0170 article-title: A novel adaptive single-phase reclosure scheme based on improved variational mode decomposition and energy entropy publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2019.105771 – volume: 140 year: 2020 ident: 10.1016/j.tust.2025.106398_b0105 article-title: Coupled modeling and dynamic characteristics of TBM cutterhead system under uncertain factors publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2020.106664 – year: 2022 ident: 10.1016/j.tust.2025.106398_b0205 article-title: Identification of geological characteristics from construction parameters during shield tunnelling publication-title: Acta Geotech. – volume: 177 year: 2021 ident: 10.1016/j.tust.2025.106398_b0220 article-title: An adaptive seismic signal denoising method based on variational mode decomposition publication-title: Measurement doi: 10.1016/j.measurement.2021.109277 – volume: 71 start-page: 340 year: 2018 ident: 10.1016/j.tust.2025.106398_b0015 article-title: Empirical modeling for predicting excavation performance of EPB TBM based on soil properties publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2017.09.016 – volume: 301 year: 2021 ident: 10.1016/j.tust.2025.106398_b0020 article-title: Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117461 – volume: 57 start-page: 4 year: 2016 ident: 10.1016/j.tust.2025.106398_b0075 article-title: TBM tunnelling under adverse geological conditions: An overview publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2016.04.002 – start-page: 793 year: 1993 ident: 10.1016/j.tust.2025.106398_b0155 article-title: New model for performance production of hard rock TBMs publication-title: Proceedings - Rapid Excavation and Tunneling Conference – volume: 8 start-page: 22 year: 2020 ident: 10.1016/j.tust.2025.106398_b0200 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2019.1708830 – volume: 184 year: 2021 ident: 10.1016/j.tust.2025.106398_b0050 article-title: A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm publication-title: Measurement doi: 10.1016/j.measurement.2021.109815 – volume: 31 start-page: 1235 year: 2019 ident: 10.1016/j.tust.2025.106398_b0230 article-title: A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures publication-title: Neural Comput. doi: 10.1162/neco_a_01199 – volume: 149 year: 2020 ident: 10.1016/j.tust.2025.106398_b0080 article-title: Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator publication-title: Measurement doi: 10.1016/j.measurement.2019.106941 – volume: 93 start-page: 192 year: 2018 ident: 10.1016/j.tust.2025.106398_b0190 article-title: Pose and trajectory control of shield tunneling machine in complicated stratum publication-title: Autom. Constr. doi: 10.1016/j.autcon.2018.05.020 – volume: 114 start-page: 413 year: 2021 ident: 10.1016/j.tust.2025.106398_b0035 article-title: Incipient fault diagnosis of bearings based on parameter-optimized VMD and envelope spectrum weighted kurtosis index with a new sensitivity assessment threshold publication-title: ISA Trans. doi: 10.1016/j.isatra.2020.12.041 |
| SSID | ssj0005229 |
| Score | 2.4564855 |
| Snippet | •A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 106398 |
| SubjectTerms | Cutterhead torque Encoder-decoder model Long short-term memory Multi-step prediction Shield machine |
| Title | Multi-step prediction model enhanced by adaptive denoising and encoder-decoder for shield machine cutterhead torque in complex conditions |
| URI | https://dx.doi.org/10.1016/j.tust.2025.106398 |
| Volume | 158 |
| WOSCitedRecordID | wos001401883500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0886-7798 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0005229 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6FlAMcEE-1vLQHbpYrv7a7OVaoqFSoQiJI4WR5H1YcpW5I7Cr8BH4Nf5HZl-2GUtEDFyda7Y6tzOfZ2ck3Mwi9Y1nMeMlZKHWdoSwVNCyOZBamkhUpl7xQ0nQt-UTPz9lsNvk8Gv3yuTBXS1rXbLudrP6rqmEMlK1TZ--g7k4oDMB3UDpcQe1w_SfFm5TaEJS30gUAZGV7gZuON4Gq5_Yff3A6C1msDG8ILM9ltfHJirqwpVTrUCrzaWiIm7nmuQUXhnipAmGaW4MVl-C5rnX9V0dmX6qtprHLqo8COr932ho-jb-Jzlxb64QSHbWHU7sKmj9C_LPW2sMLv7masL81Td_aHtSnrRs8g4nfq-vLv8zbRVsPQxsJGTBivAU8AvfftqnuzLUt9e4MbqxdLHbjXmDDEovDRueuaPGH_eTrhbd3NsSOpugZcItcy8i1jNzKuIf2EkombIz2jj-ezM4GrCLTGK97cpenZSmFu09ysy808G-mj9EjdzDBxxZQT9BI1U_Rw0G5ymfoZw8t3EMLG2hhDy3Mf2APLdxBC4PW8Q60MEALW2hhBy3cQwtbaOGqxg5auIfWc_T1w8n0_WnoWnmEIo2iJoRdlHB497OiTKhIJ3ESZzGlTCQRTyMe8RgO1oQJRdJSlqUsCsIFLbM4UlTSgqQv0Li-rNU-wpEiCXhYpe5Xm8HZkCnFGRHgWZeZFIofoNj_qLlwde51u5Vl_nd1HqCgW7OyVV5unU28rnLnp1r_Mwfo3bLu5Z3u8go96N-J12jcrFv1Bt0XV021Wb91uPsNMyK99g |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multi-step+prediction+model+enhanced+by+adaptive+denoising+and+encoder-decoder+for+shield+machine+cutterhead+torque+in+complex+conditions&rft.jtitle=Tunnelling+and+underground+space+technology&rft.au=Xu%2C+Deming&rft.au=Wang%2C+Yuan&rft.au=Huang%2C+Jingqi&rft.au=Xu%2C+Shujun&rft.date=2025-04-01&rft.issn=0886-7798&rft.volume=158&rft.spage=106398&rft_id=info:doi/10.1016%2Fj.tust.2025.106398&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_tust_2025_106398 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0886-7798&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0886-7798&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0886-7798&client=summon |