Shao et al 2023a - Revision history

Rimni at 09:46, 19 January 2024

2024-01-19T09:46:03Z

Shaojiacheng: correct the link of the reference [14]

2024-01-17T02:04:16Z

correct the link of the reference [14]

Rimni at 15:16, 16 January 2024

2024-01-16T15:16:45Z

Rimni at 15:16, 16 January 2024

2024-01-16T15:16:01Z

Rimni: /* References */

2024-01-16T15:07:48Z

‎References

Rimni at 15:06, 16 January 2024

2024-01-16T15:06:55Z

Rimni at 15:02, 16 January 2024

2024-01-16T15:02:49Z

Rimni: /* 2.2 PSO (Particle Swarm Optimization) */

2024-01-16T14:31:32Z

‎2.2 PSO (Particle Swarm Optimization)

Rimni at 14:28, 16 January 2024

2024-01-16T14:28:01Z

Rimni: /* 3.2 Spearman correlation analysis */

2024-01-16T14:26:18Z

‎3.2 Spearman correlation analysis

@@ Line 803: / Line 803: @@
 [13] Liu X., Xu S., Huang Y. Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming. ISA Transactions, 94:28-35, 2019. https://doi.org/10.1016/j.isatra.2019.04.007.
-[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/article/abstract?v=_NV5_o307Bx1YRYwtb-uBN8XnBYYdAIq-uDOIitJNGRycB6KQgTuVWBx9Z9nJxnPWG7vmi4oEAYKKmSmZtb7uNK3uZ5wPsdsHquc4X10AGfmHO3rNGCemoEHsO9KvXs4d2c1k-erWFDLodoNFC-mPg==&uniplatform=NZKPT&language=CHS
+[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFD2012&filename=1012276569.nh&uniplatform=OVERSEA&v=E60D5TP9SJIrmFT2djX4fI-7vn5r7A38JG9gv1pEeVHMLVKMZHtlrPs6SWS3gBa0
 [15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.

@@ Line 803: / Line 803: @@
 [13] Liu X., Xu S., Huang Y. Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming. ISA Transactions, 94:28-35, 2019. https://doi.org/10.1016/j.isatra.2019.04.007.
-[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
+[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/article/abstract?v=_NV5_o307Bx1YRYwtb-uBN8XnBYYdAIq-uDOIitJNGRycB6KQgTuVWBx9Z9nJxnPWG7vmi4oEAYKKmSmZtb7uNK3uZ5wPsdsHquc4X10AGfmHO3rNGCemoEHsO9KvXs4d2c1k-erWFDLodoNFC-mPg==&uniplatform=NZKPT&language=CHS
 [15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.

← Older revision		Revision as of 15:16, 16 January 2024
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	[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS		[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
−
−	~~http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFD2012&filename=1012276569.nh&uniplatform=OVERSEA&v=E60D5TP9SJIrmFT2djX4fI-7vn5r7A38JG9gv1pEeVEy4eIjFIZMkGC49IcD4tJT~~
−

	[15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.		[15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.

@@ Line 803: / Line 803: @@
 [13] Liu X., Xu S., Huang Y. Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming. ISA Transactions, 94:28-35, 2019. https://doi.org/10.1016/j.isatra.2019.04.007.
-[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
+[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, Master's Theses, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
 [15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.

@@ Line 793: / Line 793: @@
 [8] Cachim P., Bezuijen A. Modelling the torque with artificial neural networks on a tunnel boring machine. KSCE J. Civ. Eng., 23:4529–4537, 2019. [https://doi.org/10.1007/s12205-019-0302-0 https://doi.org/10.1007/s12205-019-0302-0]
-[9] Song X., Shi M., Wu J., Sun W. A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis. Mechanical Systems and Signal Processing, 133, 106279, 2019.[https://doi.org/10.1016/j.ymssp.2019.106279. https://doi.org/10.1016/j.ymssp.2019.106279.]
+[9] Song X., Shi M., Wu J., Sun W. A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis. Mechanical Systems and Signal Processing, 133, 106279, 2019. [https://doi.org/10.1016/j.ymssp.2019.106279 https://doi.org/10.1016/j.ymssp.2019.106279]
 [10] Shi M., Zhang T., Zhang L., Sun W., Song X. A fuzzy c-means algorithm based on the relationship among attributes of data and its application in tunnel boring machine. Knowledge-Based Systems, 191, 105229, 2020.
-[11] Sun W., Shi M., Zhang C., Zhao J., Song X. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92:23-34, 2018. [https://doi.org/10.1016/j.autcon.2018.03.030. https://doi.org/10.1016/j.autcon.2018.03.030.]
+[11] Sun W., Shi M., Zhang C., Zhao J., Song X. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92:23-34, 2018. [https://doi.org/10.1016/j.autcon.2018.03.030  https://doi.org/10.1016/j.autcon.2018.03.030]
 [12] Wen Z., Wang Z., Rong X. et al. Prediction of the amount of soil discharged by an earth pressure balanced shield machine based on feature engineering. KSCE J. Civ. Eng. 25:4868–4886, 2021.

@@ Line 789: / Line 789: @@
 [6] Gao X., Shi M., Song X., Zhang C., Zhang h. Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 98:225-235, 2019. [https://doi.org/10.1016/j.autcon.2018.11.013 https://doi.org/10.1016/j.autcon.2018.11.013]
-[7] Liu z., Li l., Fang x., Qi w., Shen j., Zhou H., Zhang Y. Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network. Automation in Construction, 125, 103647, 2021. [https://doi.org/10.1016/j.autcon.2021.103647. https://doi.org/10.1016/j.autcon.2021.103647.]
+[7] Liu z., Li l., Fang x., Qi w., Shen j., Zhou H., Zhang Y. Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network. Automation in Construction, 125, 103647, 2021. [https://doi.org/10.1016/j.autcon.2021.103647 https://doi.org/10.1016/j.autcon.2021.103647]
-[8] Cachim P., Bezuijen A. Modelling the torque with artificial neural networks on a tunnel boring machine. KSCE J. Civ. Eng., 23:4529–4537, 2019. [https://doi.org/10.1007/s12205-019-0302-0 https://doi.org/10.1007/s12205-019-0302-0].
+[8] Cachim P., Bezuijen A. Modelling the torque with artificial neural networks on a tunnel boring machine. KSCE J. Civ. Eng., 23:4529–4537, 2019. [https://doi.org/10.1007/s12205-019-0302-0 https://doi.org/10.1007/s12205-019-0302-0]
 [9] Song X., Shi M., Wu J., Sun W. A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis. Mechanical Systems and Signal Processing, 133, 106279, 2019.[https://doi.org/10.1016/j.ymssp.2019.106279. https://doi.org/10.1016/j.ymssp.2019.106279.]

@@ Line 777: / Line 777: @@
 <div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">
-<span id='_Ref130840916'></span>[[#cite-_Ref130840916|[1]]] Hu G., Gong G.-F., Yang H. Realization of earth pressure balance for shield tunnelling machine (in Chinese). Journal of Zhejiang University (Engineering Science), 2006(05):874-877, 2006.
+[2] Wang H., Fu D. A mathematical model and the related parameters for EPB shield tunneling (in Chinese). China Civil Engineering Journal, 2006(09):86-90, 2006.
-<span id='_Ref130840930'></span>[[#cite-_Ref130840930|[2]]] Wang H., Fu D. A mathematical model and the related parameters for EPB shield tunneling (in Chinese). China Civil Engineering Journal, 2006(09):86-90, 2006.
+[3] Wang L., Gong G.-F., Yang H., Hou D. Earth pressure balance control based on feedforward-feedback compound control (in Chinese). Journal of Central South University (Science and Technology), 44(07):2726-2735, 2013.
-<span id='_Ref130840987'></span>[[#cite-_Ref130840987|[3]]] Wang L., Gong G.-F., Yang H., Hou D. Earth pressure balance control based on feedforward-feedback compound control (in Chinese). Journal of Central South University (Science and Technology), 44(07):2726-2735, 2013.
+[4] Yeh I.C. Application of neural networks to automatic soil pressure balance control for shield tunneling. Automation in Construction, 5(5):421-426, 1997.
-−
-<span id='_Ref130841013'></span>[[#cite-_Ref130841013|[4]]] Yeh I.C. Application of neural networks to automatic soil pressure balance control for shield tunneling. Automation in Construction, 5(5):421-426, 1997.
 [5] Wei M., Wang Z., Wang X. et al. Prediction of TBM penetration rate based on Monte Carlo-BP neural network. Neural Comput. & Applic., 33:603–611, 2021.
@@ Line 802: / Line 801: @@
 [12] Wen Z., Wang Z., Rong X. et al. Prediction of the amount of soil discharged by an earth pressure balanced shield machine based on feature engineering. KSCE J. Civ. Eng. 25:4868–4886, 2021.
-<span id='_Ref130840963'></span>[[#cite-_Ref130840963|[13]]] Liu X., Xu S., Huang Y. Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming. ISA Transactions, 94:28-35, 2019. https://doi.org/10.1016/j.isatra.2019.04.007.
+[13] Liu X., Xu S., Huang Y. Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming. ISA Transactions, 94:28-35, 2019. https://doi.org/10.1016/j.isatra.2019.04.007.
-<span id='_Ref130841043'></span>[[#cite-_Ref130841043|[14]]]Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
+[14] Zhang Y. Multi-point earth pressure prediction and optimal control method for EPB shield capsule (in Chinese). Dalian University of Technology, 2011. https://kns.cnki.net/kcms2/articl/abstract?v=A67obWUfw0lxTK8BeOwngvPL3MO_OuZ_T4CcFElsylpkirzW2IvOdmfAD7ItlhsZVvGzKIL80xofvgBmjQKlbTXqRHxg6CbC1-oyJ5P5S0LbPccE9PpoWB3ePDxgo42&uniplatform=NZKPT&language=CHS
-<span id='_Ref130841054'></span>[[#cite-_Ref130841054|[15]]] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.
+[15] Zhu H.-T. Research of earth pressure balance control of shield machine based on the Fuzzy PID algorithm (in Chinese). Chinese Hydraulics & Pneumatics, 262(06):103-107, 2013.
-<span id='_Ref130841087'></span>[[#cite-_Ref130841087|[16]]] Li X.,  Gong G. Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization. Automation in Construction, 107, 102928, 2019.
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 [17] Li Q., Li J. Chamber earth pressure balance control for shield machine based on PSO-BP prediction model (in Chinese). Information Technology and Network Security, 37(04):94-99, 2018. DOI:10.19358/j.issn.2096-5133.2018.04.022.
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+[19] Yang X. Intelligent prediction of in-chamber earth pressure for EPB shield and optimization of tunneling parameters based on machine learning (in Chinese). Shandong University, Master's Theses, 2022. DOI:10.27272/d.cnki.gshdu.2022.002796.
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@@ Line 178: / Line 178: @@
 |}
-In the equation, <math>\omega </math> is the weight coefficient: <math>d = 1 , 2, 3... N </math>; <math>k</math> is the current iteration number; <math>c_1</math>, <math>c_2</math>, <math>r_1</math>, <math>r_2</math> are non-negative constants randomly distributed in <math>[0,1]</math>; to avoid large-scale blind search, the range of particle positions and velocities are limited to <math>[-X_{\max}, X_{\max}]</math>  and <math>[-V_{\max}, V_{\max}]</math>, respectively.
+where, <math>\omega </math> is the weight coefficient: <math>d = 1 , 2, 3... N </math>; <math>k</math> is the current iteration number; <math>c_1</math>, <math>c_2</math>, <math>r_1</math>, <math>r_2</math> are non-negative constants randomly distributed in <math>[0,1]</math>; to avoid large-scale blind search, the range of particle positions and velocities are limited to <math>[-X_{\max}, X_{\max}]</math>  and <math>[-V_{\max}, V_{\max}]</math>, respectively.
 In this paper, PSO algorithm is mainly used to optimize the hyperparameters in the soil pressure prediction model. Therefore, the error between the predicted values obtained by the prediction model and the actual values is used as a reference value for the fitness function, and the optimization goal is to find the best combination of hyperparameters for the deep neural network. The relationship between the model to be optimized and the optimization goal can be expressed as Eq.(11):

@@ Line 73: / Line 73: @@
 |}
-In Eqs.(1) and (2), <math>c_t</math> represents, at each time step <math> t </math>, the internal state <math>c_t</math> stores the historical information from the beginning up to the current time step; ⊙denotes element-wise multiplication of vectors; <math>c_{t-1}</math> is the memory cell from the previous time step; <math>h_t</math> is both the current time step <math>t</math>'s output of the hidden layer and the input of the hidden layer for the next time step <math>t+1</math>; <math>\tanh(x)</math> is the activation function, defined by Eq.(3), with a range of <math>(-1,1)</math>；
+In Eqs.(1) and (2), <math>c_t</math> represents, at each time step <math> t </math>, the internal state <math>c_t</math> stores the historical information from the beginning up to the current time step; ⊙denotes element-wise multiplication of vectors; <math>c_{t-1}</math> is the memory cell from the previous time step; <math>h_t</math> is both the current time step <math>t</math>'s output of the hidden layer and the input of the hidden layer for the next time step <math>t+1</math>; <math>\tanh(x)</math> is the activation function, defined by Eq.(3), with a range of <math>(-1,1)</math>
 {| class="formulaSCP" style="width: 100%; text-align: left;"

@@ Line 516: / Line 516: @@
 |  style="text-align: center;vertical-align: top;"|Unrelated
 |}
-<div class="center" style="font-size: 85%;">**Exhibits significant correlation at the 0.01 level (two-tailed)</div>
+<div class="center" style="text-align: left;font-size: 85%;">**Exhibits significant correlation at the 0.01 level (two-tailed)</div>