Prediction of tunnel boring machine operating parameters using various machine learning algorithms

Chen, Xu; Liu, Xiao Li; Wang, Enzhi; Wang, Sijing

doi:10.1016/j.tust.2020.103699

Cited by 69 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model, combining variational mode decomposition (VMD), empirical wavelet transform (EWT) and long shortterm memory (LSTM) network, can accurately predict cutterhead torque of shield tunneling machine in multiple time steps. Xu et al [26] successfully predicted the shield thrust using five different statistical and ensemble machine learning methods.…”

Section: B Intelligent Prediction For Shield Loadmentioning

confidence: 99%

A Novel Hybrid Deep Neural Network Prediction Model for Shield Tunneling Machine Thrust

Chen

Jia

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: B Intelligent Prediction For Shield Loadmentioning

confidence: 99%

A Novel Hybrid Deep Neural Network Prediction Model for Shield Tunneling Machine Thrust

Chen

Jia

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…After determining the basic parameters of the AI algorithms, it was also necessary to determine other hyperparameters, such as the number of trees in RF algorithm, the hidden layer nodes in BPNN, CNN, LSTM, and GRU algorithms, etc. For these hyperparameters, we used grid search to determine specific values [13,61]. First, on the training set, we provided a series of priori candidate values of various model parameters, then all parameter combinations were searched through the grid.…”

Section: Parameters Selectionmentioning

confidence: 99%

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

Xing²,

Wang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.

show abstract

“…Overall, although there is no specific way to say which the two algorithms would show better performance, it can be said that bagging is more useful for a dataset that is prone to high variance and the target is to decrease the model prediction variance (Grandvalet, 2004). In contrast, the gradientboosting approaches have shown acceptable outcomes in problems interacting with a dataset having features with complex and non-linear relationships, especially in regression ones (Xu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of TBM Penetration rate using Gradient Boosting-based Algorithms

Ghorbani

Adoko

Yağız

2023

Preprint

View full text Add to dashboard Cite

TBM performance prediction from the rate of penetration (ROP) point of view has yet to draw a lot of attention since it is one of the main challenges for mechanized excavation with tunnel boring machines (TBMs). In this study, five algorithms, Gradient Boosting (GB), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), AdaBoost (AB), and CatBoost (CB) have been conducted to predict the ROP based on the Gradient Boosting theory. Six tunnel cases from different projects were examined to obtain the aim of the research. Dataset developed using those tunnel datasets includes Uniaxial compressive strength (UCS), Rock Type, Distance between Plane of Weakness (DPW), and TBM-related parameter of thrust force (TF). Mentioned Gradient Boosting algorithms were performed to obtain the most accurate results for the study. The developed models showed that XGBoost outperformed the other models, followed by the CatBoost model according to seven different evaluation metrics used to rank the models. After parameter tuning, the GB model outperformed others while those were not improved very much. By using the overall ranking according to the metrics and considering the parameter tuning time, XGBoost and CatBoost presented the first two best performances. Through SHAP values and dependency plots, the features and importance of the inputs showed the TF has the highest impact on the ROP, followed by UCS, Rock Type, and DPW. It is concluded that the XGBoost and CatBoost algorithms could be used for modeling to obtain the TBM penetration for similar rock types.

show abstract

Prediction of tunnel boring machine operating parameters using various machine learning algorithms

Cited by 69 publications

References 33 publications

A Novel Hybrid Deep Neural Network Prediction Model for Shield Tunneling Machine Thrust

A Novel Hybrid Deep Neural Network Prediction Model for Shield Tunneling Machine Thrust

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

Estimation of TBM Penetration rate using Gradient Boosting-based Algorithms

Contact Info

Product

Resources

About