Prediction of Axis Attitude Deviation and Deviation Correction Method Based on Data Driven During Shield Tunneling

Wang, Pei; Kong, Xianwen; Guo, Zekun; Hu, Lei

doi:10.1109/access.2019.2952649

Cited by 37 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SAKAI and HOSHIYA applied the Kalman filter theory to the attitude control of the shield and established an autoregressive predictive control model 22 . Xia et al and Wang et al used the XGBoost model to predict the deviation of the shield 23 , 24 . The model's input is composed of control parameters such as cutter speed, cutter torque, total thrust, and advance speed.…”

Section: Literature Reviewmentioning

confidence: 99%

Interpretable predictive model for shield attitude control performance based on XGboost and SHAP

Zhang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The sudden decline in the attitude control performance is a common abnormal situation during shield tunneling. When the problem happens, the shield driver will have difficulty controlling the shield's attitude, which will cause the shield to deviate from its design axis and affect the quality of the tunnel. The causes behind poor control performance are usually complicated, so how to choose appropriate countermeasures is a challenging problem. Based on the above issues, this paper proposes the Interpretable Predictive Model for Shield attitude Control Performance (IPM_SCP). The model first predicts the current shield control performance through the extreme gradient boosting (XGBoost) sub-model and then uses the Shapley additive explanation sub-model to interpret the model output. The model was tested on the left-line tunnel of the Hangzhou–Shaoxing railway project in the Ke-Feng section. The results reveal that the model could effectively predict the control performance of the shield and give the most influential parameter and the direction in adjusting the parameter to improve the shield's attitude control performance when the control performance decreases. Therefore, IPM_SCP gives the correct parameter adjustment instructions when the shield’s attitude control performance declines, and eventually improves tunnel construction quality and efficiency.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Interpretable predictive model for shield attitude control performance based on XGboost and SHAP

Zhang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Propelling pressure (PA~PF) is the main execution parameter of SMA correction for EPB operators [60]. Some of the above parameters were also used in the prediction models of SMA in Wang et al [18] and Zhou et al [19].…”

Section: Parameters Selectionmentioning

confidence: 99%

“…Based on the shield data, Zhang et al [17] established a hybrid trajectory deviation prediction model combining principal component analysis (PCA) and gated recurrent unit (GRU). For the SMA prediction, similar yields include the extreme gradient boosting (XGBoost) model established by Wang et al [18] and a hybrid deep learning model proposed by Zhou et al [19]. These methods made significant attempts in the development of SMA prediction models, but so far as we know, there is still a lack of a universal approach.…”

Section: Introductionmentioning

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

“…Based on a Turkish project [24], big data research was carried out on the four important parameters of cutter head speed, cutter head torque, thrust, and advancement rate during the TBM advancement process, which can comprehensively predict the tunnel geological conditions. After processing the advancement data, it can accurately predict the axial attitude deviation of the shield and provide a correction method [25]. Taking the Shenzhen Metro as an example [26], based on five popular artificial intelligence methods, starting from parameter correlation analysis, inputting geological parameters and operational parameters, an accurate and flexible prediction model is obtained.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Model of Shield Advancement Rate in Sandy Cobble Ground of Beijing

Li²,

Yang

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

The long distance and tight construction period of the earth pressure balance shield tunneling in Beijing sandy cobble ground have a very positive impact on grasping the construction progress and improving the organization efficiency. Based on the real-time monitoring data of on-site earth pressure, cutter head speed, total thrust, screw conveyor speed, and cutter head torque, this study investigated the correlation between the shield advancement rate and the above parameters based on multiple nonlinear regression analysis method, which lay down the foundation of shield advancement rate prediction. The prediction model achieved satisfactory results and was further applied to an airport project. The results show that it is of guiding significance to use multiple regression method to establish shield prediction model and put forward the best advancement parameters to ensure the construction progress. The order in which the four operational parameters affect the advancement rate is total thrust, cutter head torque, screw conveyor speed, and cutter head speed. When the advancement time is greater than 57 min, the average advancement rate and daily progress are highly positively correlated. The research provides a reliable basis for the advancement rate prediction, safety control, and parameter optimization of shield advancement in similar ground.

show abstract

Prediction of Axis Attitude Deviation and Deviation Correction Method Based on Data Driven During Shield Tunneling

Cited by 37 publications

References 32 publications

Interpretable predictive model for shield attitude control performance based on XGboost and SHAP

Interpretable predictive model for shield attitude control performance based on XGboost and SHAP

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

Research on the Prediction Model of Shield Advancement Rate in Sandy Cobble Ground of Beijing

Contact Info

Product

Resources

About