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Shield load is the main performance indicator of the shield, accurate load prediction is significant to ensure the safety and efficiency of the shield and the stability of the surrounding environment. Recognizing the limitations of the traditional prediction methods, this paper proposes a hybrid model (CBM), combining convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM) and attention mechanism, to predict the shield load accurately based on the high-dimensional feature and time series characteristic of the data. The proposed model not only can extract the high-dimensional features and time series characteristics of the data, but also can highlight the importance of high-dimensional features and important time node information. The experiment results show that compared with the existing models, the proposed model achieves a higher prediction performance, the prediction accuracy of the thrust and torque is 94.2% and 96.2%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Jiangsu University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hazard Control in Tunnelling & Underground Engineering is the property of Shandong University Scientific Journals Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jiangsu University (Natural Science Edition) / Jiangsu Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Jiangsu University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a new buried excavation support system, the application and popularization of steel tube slab ( STS) method are limited due to the unclear deformation mechanism of STS structure and lack of further optimization of the connection parameters between the tubes. So, a series of three-dimensional finite element models ( FEMs) is established and verified by laboratory tests to investigate the influence of the flange thickness and diameter of bolts on flexural stiffness of STS structure. By means of SPSS software and normalization method, it is indicated that when the ratio of the diameter of bolts to the flange thickness ranges from 2. 0 to 2. 5, the deformation state of the STS structure is the most appropriate. Subsequently, a calculation model of flexural stiffness of STS structure is proposed according to experimental and numerical results. Reasonability of the presented model is evaluated, and the error is less than 20% . [ABSTRACT FROM AUTHOR]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)