Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

Hu, Xiao; Xing, Bo; Wang, Yujie; Yu, Peng; Liu, Lipeng; Cao, Rihong

doi:10.3390/app112110264

Cited by 31 publications

(9 citation statements)

References 56 publications

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“…Kong Xiangxun [12] proposed a prediction method based on random forest and selected geological conditions and shield operation data as features to establish a prediction model to achieve thrust prediction. Xiao Haohan [13] established eight simple moving average (SMA) prediction models K-nearest neighbors (KNN), Support vector regression (SVR), Random Forest (RF), Adaptive Boosting (AdaBoost), Backpropagation Neural Network (BPNN), CNN, LSTM, and GRU for comparison, and indirectly realized thrust prediction. At present, predictive control is also widely used in ground settlement and possible risks during shield machine construction [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The prediction models built by the above methods can all give the prediction parameters for the next moment, but they are based on a large amount of data-driven, which leads to a long training time for the model due to a large amount of data and does not satisfy the requirement of real-time prediction. In addition, the structure and input characteristics of the thrust prediction model in the literature [12,13]are relatively simple and do not consider the input of other relevant tunneling parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

et al. 2023

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During the tunneling process of shield machine, the accurate determination of tunneling parameters is the guarantee of safe operation of shield machine. Through the real-time construction data analysis and mining of shield machine, the tunneling parameters at the next moment are obtained in real-time. Based on this, this paper proposes an intelligent real-time prediction method for multi-region thrust of EPB shield machine based on SSA-LSTM (Sparrow Search algorithm-Long and Short-term Memory). By correlation analysis of the construction big data, the data features with a great correlation of shield machine thrust are obtained. And it is used as the input of LSTM prediction model to explore the nonlinear relationship between inputs and output. SSA is used to optimize LSTM prediction model to establish a more accurate nonlinear relationship, and then the multi-region thrust of shield machine at the next moment is accurately obtained. The simulation results show that SSA-LSTM model can accurately predict the thrust of each region of shield machine at the next moment, and the prediction performance is better than other models. The method provides a reference basis for shield machine to implement accurate thrust regulation and provides a guarantee for effective control of earth pressure balance (EPB) in sealed cabin to ensure construction safety, which has engineering application value.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

et al. 2023

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“…To ensure the advance in accordance with the established route and control the deformation of tunnel excavation, it is important to adjust the shield attitude in time 1 . The control of shield propulsion attitude should be paid more attention to 2 . The unreasonable control of shield attitude may seriously affect the safety and quality of tunnel construction 3 .…”

Section: Introductionmentioning

confidence: 99%

Attitude prediction of shield machine based on BO-CatBoost

Li,

Zeng,

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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To effectively control the shield attitude to avoid excessive influence on the tunnel caused by forward deformation, serpentine shape, axis deviation and correction, it is necessary to effectively control the shield attitude. Based on BO-CatBoost machine learning algorithm, an intelligent prediction framework for shield construction propulsion attitude is proposed to maintain construction safety and quality. 23 factors affecting the shield attitude are selected as input variables. The CatBoost hyperparameters are optimized by Bayesian optimization (BO) algorithm and the importance of the influencing factors is evaluated by BO-CatBoost. And the attitude prediction model is established. The applicability and effectiveness of this method are verified by an engineering example: (1) The BO-CatBoost model has an excellent prediction effect for shield attitude. The goodness of fit 𝑅 2 is all above 0.9, and the values of 𝑅𝑀𝑆𝐸 and 𝑀𝐴𝐸 are small. (2) The BO-CatBoost algorithm can effectively identify important construction parameters and provide guarantee for the safety control of shield construction.

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“…Therefore, many scholars have conducted research on TBMAP prediction. Xiao et al [16] established eight prediction models of shield machine attitude based on the data from five earth pressure balance shield machines and obtained two best algorithms, the LSTM and GRU with EVS > 0.9 and RMSE < 1.5. Fu et al [17] proposed the deep learning model with a graph convolutional network and long short-term memory, which can predict the vertical and horizontal deviations at the articulation and tail of TBM with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning

Jia,

Huo,

Yang

et al. 2023

Applied Sciences

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Efficient control of tunnel boring machine (TBM) tunneling along the designed tunnel axis in an unknown variable geological environment is a difficult and significant task. At present, the TBM attitude during tunneling is mostly manually controlled based on the deviation between the tunneling axis and the designed tunnel axis and their experiences. The tunneling axis from manual control is often the snakelike motion around the designed tunnel axis, even exceeding the deviation limit, for which this paper analyzed three reasons, the unknown geological environment, the hysteresis of TBM position response, and the unsolved overall optimization of tunneling axis. For these reasons, this paper proposed a real-time optimal control framework of TBM attitude based on reinforcement learning, which contains the geological information predictive model, TBM attitude and position (TBMAP) predictive model, and optimal attitude control policy (OACP). This framework can predict the current geological information in real-time and provide the corresponding real-time optimal attitude control that simultaneously considers the hysteresis of TBM position response and the overall optimization of the tunneling axis. This attitude control framework can be directly deployed to TBM without increasing costs and excessive modifications to the equipment. To verify the effectiveness of this attitude control framework, the Xinjiang Yiner Water Supply Phase II Project, using the TBM method, was adopted as a case study. The results revealed that the accuracy of geological environment recognition reached 94%, and OACP can significantly reduce the accumulated deviation of the tunneling axis from the designed tunnel axis by over 80% compared with the manual control and easily provide real-time decision support for attitude control in actual engineering.

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Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

Cited by 31 publications

References 56 publications

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

Attitude prediction of shield machine based on BO-CatBoost

The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning

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