# Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Machine Learning Modelling

#### 2.1. Data Processing

#### 2.2. Machine Learning Algorithms

#### 2.3. Hyperparameter Tuning

#### 2.4. Evaluation Metrics

^{2}) and variance account for (VAF) represent the proportion of the variance in the dependent values between 0 and 1, where a larger value indicates a higher accuracy between predicted and measured values, and vice versa.

## 3. Application in TBM Tunnelling

#### 3.1. TBM Performance

#### 3.2. Surface Settlement

_{max}) for the Bangkok subway project, considering tunnel geometry, geological conditions, and operational parameters. Pourtaghi and Lotfollahi-Yaghin [33] improved the ANN model by adopting wavelets as activation functions, resulting in higher accuracy than traditional ANN models. In contrast, Goh et al. [77] utilised MARS and Zhang et al. [78] utilised XGBoost to predict S

_{max}for Singapore mass rapid transport lines with 148 samples. Interestingly, the mean standard penetration test showed opposite sensitivities in these two models. It further highlights the unreliability and unrobustness of ML models with limited samples, which may lead to overfitting or lack of generalisability. A comprehensive dataset from Changsha metro line 4, including geometry, geological conditions, and real-time operational parameters, has been used to compare the performance of various ML models such as ANN, SVM, RF, and LSTM [22,24,34,47].

#### 3.3. Time Series Forecasting

_{max}in the next step based on another RF model.

## 4. Summary and Perspectives

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

TBM | Tunnel boring machine |

ML | Machine learning |

PR | Penetration rate |

AR | Advance rate |

FPI | Field penetration index |

TH | Thrust force |

TO | Cutterhead torque |

S_{max} | Maximum surface settlement |

PCA | Principal component analysis |

PCC | Pearson correlation coefficient |

ANN | Artificial neural network |

CNN | Convolutional neural network |

RNN | Recurrent neural network |

LSTM | Long short-term memory |

GRU | Gated recurrent unit |

FL | Fuzzy logic |

ANIFS | Adaptive neuro-fuzzy inference system |

SVM | Support vector machine |

DT | Decision tree |

RF | Random forest |

CART | Classification and regression tree |

XGBoost | Extreme gradient boosting |

PSO | Particle swarm optimisation |

BO | Bayesian optimisation |

ICA | Imperialism competitive algorithm |

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**Figure 3.**Summary of ML algorithms in TBM performance, surface settlement and time series forecasting.

**Figure 4.**Comparison optimisation techniques (

**a**) MR model based on dataset from Queen water tunnel; (

**b**) XGBoost model based on dataset from Pahang–Selangor raw water transfer.

Literature | Data Processing ^{a} | Algorithms ^{b} | Hyperparameter Tuning ^{c} | Targets ^{d} | Data Size and Data Set |
---|---|---|---|---|---|

Grima et al. [26] | PCA | MR, ANN, ANFIS | - | PR, AR | 640 tunnel project |

Benardos and Kaliampakos [31] | - | ANN | - | AR | 11-Athens metro |

Tiryaki [28] | PCA | MR, ANN | - | specific energy | 44-Three tunnel projects |

Mikaeil et al. [41] | - | FL | - | Penetrability | 151-Queens water tunnel |

Yagiz [59] | PCC | MR, ANN | - | PR | 151-Queens water tunnel |

Javad and Narges [60] | - | ANN | - | PR | 185-Three tunnel projects |

Mahdevari et al. [43] | - | MR, SVM | - | PR | 151-Queens water tunnel |

Salimi et al. [27] | PCA | MR, SVM, ANFIS | - | FPI | 75-Zagros lot 1B and 2 |

Armaghani et al. [30] | - | ANN | PSO, ICA | PR | 1286-Pahang-Selangor raw water transfer |

Armaghani et al. [61] | - | MR, GEP | - | PR | 1286-Pahang-Selangor raw water transfer |

Sun et al. [18] | Kriging interpolation, rate of change | RF | - | TH, TO, PR | 88-Shenzhen metro |

Armaghani et al. [55] | - | ANN | PSO, ICA | AR | 1286-Pahang-Selangor raw water transfer |

Koopialipoor et al. [62] | - | ANN, DNN | - | PR | 1286-Pahang-Selangor raw water transfer |

Salimi et al. [48] | PCA | MR, CART, GP | - | FPI | 580-Seven tunnel projects |

Zhang et al. [47] | PCC | RF | PSO | TO, TH, PR, FP | 294-Changsha metro line 4 |

Koopialipoor et al. [63] | - | ANN | firefly algorithm | PR | 1200-Pahang-Selangor raw water transfer |

Mokhtari and Mooney [44] | PCC, Relief | SVM | BO | PR | Northgate Link tunnel |

Wang et al. [64] | - | ANN, LSTM, RF, SVM | - | AR | 806-Nanning metro line 1 |

Zhang et al. [49] | - | SVM, CART, RF, bagging, Ada boosting | BO | PR | 151-Queens water tunnel |

Zhang et al. [22] | WT, MD, GRG | LSTM, RF | PSO | TH, TO, PR, RPM, CP | 3549-Changsha metro line 4 and Zhengzhou metro line 2 |

Zhou et al. [65] | - | ANN, GP | - | AR | 1286-Pahang-Selangor raw water transfer |

Bai et al. [45] | PCC, Seasonal-trend decomposition | MR, SVM, DT, GBoost | - | TO, TH, FP | 450-Xi’an metro |

Bardhan et al. [66] | - | hybrid ensemble model | - | PR | 185-Three tunnel project |

Harandizadeh et al. [56] | - | ANFIS-PNN | ICA | PR | 209-Pahang-Selangor raw water transfer |

Lin et al. [67] | - | MR, ANN, SVM, LSTM, GRU, EML | PR | 1000-Shenzhen railway | |

Parsajoo et al. [42] | - | ANFIS | artificial bee colony | FPI | 150-Queens water tunnel |

Zeng et al. [35] | - | EML | PSO | AR | 1286-Pahang-Selangor raw water transfer |

Zhou et al. [54] | - | XGBoost | BO | AR | 1286-Pahang-Selangor raw water transfer |

Zhou et al. [50] | - | ANN, RF, XGBoost, SVM | GWO, PSO, SCA, SSO, MVO, MFO | PR | 1286-Pahang-Selangor raw water transfer |

Lin et al. [25] | - | LSTM | PSO | TH | 1500-Shenzhen railway |

Lin et al. [68] | - | GRU | PSO | TO | 1500-Shenzhen railway |

Salimi et al. [69] | - | MR, CART | - | FPI | 666-Eight tunnel projects |

Yang et al. [3] | - | SVM | GWO, biogeography-based optimisation | PR | 503-Shenzhen metro line |

^{a}WT, wavelet transform; MD, Mahalanobis distance; GRG, grey rational grade.

^{b}MR, multiple regression (linear/non-linear); GP, genetic programming; GBoost, gradient boosting; GEP, gene expression programming; EML, extreme machine learning; PNN, polynomial neural network; DNN, deep neural network.

^{c}GWO, grey wolf optimiser; SCA, sine cosine algorithm; SSO, social spider optimisation; MVO, multi-verse optimisation; MFO, moth flame optimization.

^{d}FP, face pressure; RPM, revolutions per minute; CP, chamber pressure.

Model Type | Dataset | Data Size | Parameters | Open Access | Limitations |
---|---|---|---|---|---|

Model A | 640 tunnel projects | - | Geological conditions, Operational parameters, TBM type and size | No | hard to access |

Model B | Queen water tunnel | 151 | Geological conditions | Yes | overfitting or lack of generalisability |

Model C | Pahang-Selangor raw water transfer | 1286 | Geological conditions, Operational parameters | Yes | hard to apply in practice |

Literature | Data Processing | Algorithms ^{a} | Hyperparameter Tuning | Targets | Data Size and Data Set |
---|---|---|---|---|---|

Suwansawat and Einstein [32] | - | ANN | - | S_{max} | 49-Bangkok subway project |

Boubou et al. [75] | - | ANN | - | S(X) | 432-Toulouse subway line B |

Pourtaghi and Lotfollahi-Yaghin [33] | - | Wavelet-ANN | - | S_{max} | 49-Bangkok subway project |

Dindarloo and Siami-Irdemoosa [76] | PCC | CART | - | S_{max} | 34-Various tunnel projects |

Goh et al. [77] | - | MARS | - | S_{max} | 148-Three Singapore MRT projects |

Chen et al. [24] | PCC | ANN, RBF, GRNN | - | S_{max} | 200-Changsha metro line 4 |

Zhang et al. [47] | PCC | RF | PSO | S_{max} | 294-Changsha metro line 4 |

Zhang et al. [34] | PCC | ANN, SVM, RF, EML, GRNN | PSO | S_{max} | 294-Changsha metro line 4 |

Zhang et al. [22] | WT, MD, GRG | LSTM, RF | PSO | S_{max} | 423-Changsha metro line 4 |

Zhang et al. [78] | PCC | XGBoost, ANN, SVM, MARS | - | S_{max} | 148-Three Singapore MRT projects |

Kannangara et al. [46] | PCC, sequential feature selection, Boruta algorithm | RF | - | S_{max} | 264-Hangzhou metro line 2 and line 6 |

**MARS, multivariate adaptive regression spline; RBF, radial basis function; GRNN, general regression neural network.**

^{a}Literature | Data Processing ^{a} | Algorithms ^{b} | Hyperparameter Tuning | Targets ^{c} | Data Size and Data Set |
---|---|---|---|---|---|

Guo et al. [20] | WT | Elman RNN | PSO | longitudinal settlement | Jiangji subway tunnel |

Zhang et al. [79] | WT | ANN, SVM | - | daily settlement | 60-Wuhan metro line 2 |

Gao et al. [37] | - | RNN, LSTM, GRU, SVM. RF, Lasso | - | TO, TH, AR, CP | 3000-Shenzhen metro |

Zhou et al. [23] | WT | ARIMA, LSTM, CNN-LSTM | - | HDSH, HDST, VDSH, VDST, roll, pitch | 5005-Sanyang Road Tunnel |

Gao et al. [80] | 3-sigma rule, MA, GRG | GRU | genetic algorithm | earth pressure | 1538-Luoyang metro line 2 |

Erharter and Marcher [81] | PCC | LSTM, RF, SVM | - | TO | 200,000-Brenner base tunnel |

Feng et al. [13] | 3-sigma rule, WT | DBN | - | FPI | 8915-Yingsong water diversion project |

Gao et al. [82] | - | ARIMA, RNN, LSTM | - | PR | Hangzhou second water source project |

Li et al. [2] | PCC | LSTM | - | TO, TH | 4650-Yingsong water diversion project |

Qin et al. [36] | cosine similarity | CNN-LSTM, XGBoost, RF, SVM, LSTM, RNN, CNN | - | TO | 150,000-Singapore metro T225 project |

Shi et al. [39] | WT, variational mode decomposition | LSTM, CNN, RNN, SVM, RF | - | TO | 60,000-Singapore metro T225 project |

Wang et al. [21] | WT, light gradient boosting machine | LSTM | - | PR, TO | 25,543-Sutong gas transmission line |

Xu et al. [14] | 3-sigma rule, MA, PCC | SVM, RF, CNN, LSTM, GBoost, KNN, Bayesian ridge regression | - | PR, TO, TH, RPM | 7000-Yingsong water diversion project |

Zhang et al. [83] | - | RF | - | S_{max} | 386-Changsha Metro Line 4 |

Huang et al. [53] | SelectKBest | LSTM | BO | TO | Yingsong water diversion project |

Shan et al. [19] | MA | RNN, LSTM | - | PR | 463-Changsha metro line 4 and Zhengzhou metro line 2 |

Shen et al. [17] | WT, Kriging interpolation | LSTM, SVM, RNN | - | HDSH, HDST, VDSH, VDST, roll, pitch | 1200-Shenzhen intercity railway |

Zhang et al. [29] | PCA, PCC | GRU, RNN, SVM | - | HDSH, HDST, VDSH, VDST, | 22,010-Guang-Fo intercity railway |

^{a}MA, moving average.

^{b}DBN, deep belief network; KNN, k-nearest neighbours.

**HDSH, horizontal deviation of shield head; HDST, horizontal deviation of shield tail; VDSH, vertical deviation of shield head; VDST, vertical deviation of shield tail.**

^{c}Literature | Category | Historical Data | Forecast Horizon | ||
---|---|---|---|---|---|

Step behind | Distance behind | Step ahead | Distance ahead | ||

Gao et al. [37] | high-frequency | 5 steps | 1.25 mm ^{a} | 1 step | 0.25 mm ^{a} |

Qin et al. [36] | 10 steps | - | 1 step | - | |

Huang et al. [53] | 6 steps | 22.4 mm ^{a} | 1 step | 3.73 mm ^{a} | |

Erharter and Marcher [81] | 50 steps | 2.75 m | 1 or 100 steps | 0.055 or 5.5 m | |

Shi et al. [39] | 10 steps | - | 1–5 steps | - | |

Gao et al. [80] | low-frequency | 5 steps | 7.5 m | 1 step | 1.5 m |

Feng et al. [13] | 7 steps | 7 m | 1 step | 1 m | |

Shan et al. [19] | 5 steps | 7.5 m | 1–5 steps | 1.5–7.5 m |

^{a}The distance is estimated based on the time step, sampling period, and average penetration rate.

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## Share and Cite

**MDPI and ACS Style**

Shan, F.; He, X.; Xu, H.; Armaghani, D.J.; Sheng, D.
Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review. *Eng* **2023**, *4*, 1516-1535.
https://doi.org/10.3390/eng4020087

**AMA Style**

Shan F, He X, Xu H, Armaghani DJ, Sheng D.
Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review. *Eng*. 2023; 4(2):1516-1535.
https://doi.org/10.3390/eng4020087

**Chicago/Turabian Style**

Shan, Feng, Xuzhen He, Haoding Xu, Danial Jahed Armaghani, and Daichao Sheng.
2023. "Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review" *Eng* 4, no. 2: 1516-1535.
https://doi.org/10.3390/eng4020087