Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine
It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foragi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9926104/ |
| _version_ | 1818470575794814976 |
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| author | Long Li Zaobao Liu Yuchi Lu Fei Wang Seokwon Jeon |
| author_facet | Long Li Zaobao Liu Yuchi Lu Fei Wang Seokwon Jeon |
| author_sort | Long Li |
| collection | DOAJ |
| description | It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency. |
| first_indexed | 2024-04-13T21:39:54Z |
| format | Article |
| id | doaj.art-63ba3ecb6a3442fbbe3016cd64cd9635 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T21:39:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-63ba3ecb6a3442fbbe3016cd64cd96352022-12-22T02:28:49ZengIEEEIEEE Access2169-35362022-01-011011269511271210.1109/ACCESS.2022.32162949926104Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning MachineLong Li0https://orcid.org/0000-0003-3726-8054Zaobao Liu1https://orcid.org/0000-0002-2047-5463Yuchi Lu2https://orcid.org/0000-0001-7840-6471Fei Wang3Seokwon Jeon4https://orcid.org/0000-0002-8806-066XKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaKey Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, ChinaDepartment of Energy Systems Engineering, Seoul National University, Seoul, South KoreaIt is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency.https://ieeexplore.ieee.org/document/9926104/Hard rock TBMconstruction big datathrust predictiontwo-hidden-layer extreme learning machine |
| spellingShingle | Long Li Zaobao Liu Yuchi Lu Fei Wang Seokwon Jeon Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine IEEE Access Hard rock TBM construction big data thrust prediction two-hidden-layer extreme learning machine |
| title | Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine |
| title_full | Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine |
| title_fullStr | Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine |
| title_full_unstemmed | Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine |
| title_short | Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine |
| title_sort | hard rock tbm thrust prediction using an improved two hidden layer extreme learning machine |
| topic | Hard rock TBM construction big data thrust prediction two-hidden-layer extreme learning machine |
| url | https://ieeexplore.ieee.org/document/9926104/ |
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