# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. EPB Machine Specifications

## 4. Engineering Geology

#### 4.1. Grain Size Distribution and Characteristics

## 5. EPB Cutting Tool Wear and Machine Parameters

#### Machine Parameters and Tool Wear Evaluation

## 6. Explanation of CNN

## 7. Proposed 1D CNN Regression for Cutter Wear Prediction

#### 7.1. Data Preprocessing

#### 7.2. ReLU Function

#### 7.3. Leaky ReLU

#### 7.4. Mean Squared Error (MSE)

#### 7.5. Mean Absolute Error (MAE)

#### 7.6. Regularization

## 8. Model Comparison

## 9. Discussion

## 10. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Overview of the proposed disc cutter wear prediction model. The proposed 1D CNN model was trained and evaluated to obtain the best model for disc cutter wear prediction.

**Figure 5.**Triangular grain size distribution and characteristics: (

**a**) Triangular system, (

**b**) triangular coordinates for medium classification, (

**c**) subclassification of coarse soil and fine-grained soil.

**Figure 10.**(

**a**) Disc cutter, (

**b**) reamer bit, (

**c**) block bit, and (

**d**) leading bit wear measurement on site.

**Figure 15.**(

**a**) Actual and predicted data distribution with fit line; (

**b**) error distribution between actual and predicted data; (

**c**) 1D CNN learning curve.

Machine Specifications | |
---|---|

Shield outer diameter (m) | 3.12 |

Shield length (m) | 7.42 |

Thrust force (kN/m^{2}) | 1342 |

Shield jack speed (mm/min) | 63 |

Torque (kN.m) | 886 |

Borehole Number | Depth (m) | Soil Layers | Soil Layers | Test Points | Elastic Module (MN/m ^{2}) | Consolidated Undrained Triaxial Test | |
---|---|---|---|---|---|---|---|

Cohesion $(\mathbf{kN}/{\mathbf{m}}^{2})$ | Friction Angle | ||||||

No. 1 | 7.50 | Silty clay | Vcs | 6 | 15.97 | 16.4 | 31.04 |

10 | Sandy silt mixed with gravel | Vcs | 5 | 14.76 | 21.4 | 41.37 | |

No. 2 | 16 | Sandy clay | Vsg | 50 | 83.45 | 92.1 | 31.27 |

19.50 | Silty sand | Vsg | 18 | 45.20 | 60.5 | 23.33 | |

No. 3 | 11 | Silty clay | Vcs | 4 | 4.37 | 19.7 | 27.25 |

14.50 | Silty gravel | Vg | 34 | 62.37 | 2.7 | 40.44 |

Descriptive | Pressure Gauge (kPa) | Digging Velocity (mm/min) | Shield Jack Stroke (mm) | Propulsion Pressure (MPa) | Total Thrust (kN) | Cutter Torque (kN.m) | Cutter Rolling Velocity (rpm) | Screw Pressure (MPa) | Screw Rotation Speed (rpm) | Gate Opening (%) | Mud Injection Pressure (MPa) | Add MudFlow (L/min) | Back in injection rate (%) | E_{f} (m^{3}/mm) | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | Left | Right | Left | Right | ||||||||||||

Count | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 | 726 |

Mean | 64.95 | 60.85 | 58.51 | 67.22 | 13.74 | 13.27 | 694.17 | 696.96 | 15.75 | 3459 | 482 | 1.96 | 2.85 | 0.90 | 29.40 | 0.18 | 16.33 | 96.9 | 39.79 |

Std | 32.64 | 36.99 | 36.26 | 36.88 | 14.14 | 10.71 | 137.75 | 213.39 | 5.82 | 1268 | 69 | 0.27 | 5.42 | 2.96 | 12.16 | 0.08 | 8.24 | 37.71 | 22.6 |

Min | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | −114 | −1.21 | 0 | 0 | 0 | 0 | 0 |

25% | 42.43 | 35.90 | 31.63 | 39.75 | 8.18 | 8 | 692.27 | 689.31 | 11.36 | 2517 | 449 | 1.87 | 2.66 | 0.30 | 22.72 | 0.14 | 10.2 | 79.54 | 30.15 |

50% | 60.45 | 53.90 | 56.13 | 64.19 | 12 | 12 | 700.5 | 699 | 14.90 | 3290 | 478 | 2 | 2.93 | 0.55 | 27.09 | 0.17 | 15.56 | 91.36 | 39.81 |

75% | 80.79 | 76.81 | 84.38 | 88.43 | 17.34 | 16.90 | 706.63 | 707.15 | 19.97 | 4335 | 515 | 2.06 | 3.35 | 1.1 | 35.63 | 0.22 | 21.48 | 110.2 | 59.39 |

Max | 210.27 | 259 | 175.36 | 426.93 | 336 | 239 | 3504.2 | 5946 | 31.54 | 7160 | 694 | 7 | 7.69 | 78.25 | 67.27 | 1.6 | 58.31 | 560 | 78.7 |

Inspection Points | Total Wear (mm) |
---|---|

205 | 76 |

344 | 33 |

493 | 41 |

623 | 103 |

Total | 253 |

M (arithmetic average) | 63.3 |

Layer | Output Shape | Parameters |
---|---|---|

Conv_1 (1D) | (None, 1532) | 128 |

MaxPooling1D | (None, 1532) | 0 |

Conv_2 (1D) | (None, 1364) | 6208 |

MaxPooling1D | (None, 1364) | 0 |

Conv_3 (1D) | (None, 11,128) | 24,704 |

MaxPooling1D | (None, 11,128) | 0 |

Conv_4 (1D) | (None, 9256) | 98,560 |

MaxPooling1D | (None, 9256) | 0 |

Flatten | (None, 2304) | 0 |

Dense_1 | (None, 512) | 1,180,160 |

Dense_2 | (None, 128) | 65,664 |

Output (Dense) | (None, 1) | 129 |

Trainable Params | 1,375,553 |

Model | ${\mathit{R}}^{2}$ | MSE | MAE | Time |
---|---|---|---|---|

Proposed 1D CNN | 89.6 | 57.5 | 1.6 | 3 min 22 s |

MLPRegressor | 77.8 | 107 | 5.93 | 3 min 26 s |

LSTMRegressor | 39 | 326.3 | 11.12 | 12 min 38 s |

Extratree regression | 77.90 | 111.16 | 7.31 | 0.497 s |

Light gradient boosting machine | 73.98 | 130.19 | 8.04 | 0.101 s |

Gradient boosting machine | 71.81 | 141.01 | 8.71 | 0.182 s |

Random forest regression | 70.72 | 147.54 | 8.35 | 0.671 s |

AdaBoost regressor | 59.12 | 202.48 | 11.84 | 0.127 s |

k-NN regression | 19.04 | 452.03 | 15.47 | 0.062 s |

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## Share and Cite

**MDPI and ACS Style**

Kilic, K.; Toriya, H.; Kosugi, Y.; Adachi, T.; Kawamura, Y. One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction. *Appl. Sci.* **2022**, *12*, 2410.
https://doi.org/10.3390/app12052410

**AMA Style**

Kilic K, Toriya H, Kosugi Y, Adachi T, Kawamura Y. One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction. *Applied Sciences*. 2022; 12(5):2410.
https://doi.org/10.3390/app12052410

**Chicago/Turabian Style**

Kilic, Kursat, Hisatoshi Toriya, Yoshino Kosugi, Tsuyoshi Adachi, and Youhei Kawamura. 2022. "One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction" *Applied Sciences* 12, no. 5: 2410.
https://doi.org/10.3390/app12052410