Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach

The presented research introduces a novel hybrid deep learning approach for the dynamic prediction of the attitude and position of super-large diameter shields-a critical consideration for construction safety and tunnel lining quality.This study proposes a hybrid deep learning approach for predicting dynamic attitude and position prediction of super-large diameter shield.The approach consists of principal component analysis(PCA)and temporal convolutional network(TCN).The former is used for employing feature level fusion based on features of the shield data to reduce uncertainty,improve accuracy and the data effect,and 9 sets of required principal component characteristic data are obtained.The latter is adopted to process sequence data in predicting the dynamic attitude and position for the advantages and potential of convolution network.The approach’s effectiveness is exemplified using data from a tunnel construction project in China.The obtained results show remarkable accuracy in predicting the global attitude and position,with an average error ratio of less than 2 mm on four shield outputs in 97.30%of cases.Moreover,the approach displays strong performance in accurately predicting sudden fluctuations in shield attitude and position,with an average prediction accuracy of 89.68%.The proposed hybrid model demonstrates superiority over TCN,long short-term memory(LSTM),and recurrent neural network(RNN)in multiple indexes.Shapley additive exPlanations(SHAP)analysis is also performed to investigate the significance of different data features in the prediction process.This study provides a real-time warning for the shield driver to adjust the attitude and position of super-large diameter shields.

Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling

Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation.This study therefore introduced a framework to predict the attitude and position of shield machine by combining long short-term memory(LSTM)model with attention mechanism.The data obtained from the Wuhan Rail Transit Line 6 project were utilized to verify the feasibility of the proposed method.By adding the attention mechanism into the LSTM model,the proposed model can focus more on parameters with higher weights.Sensitivity analysis based on Pearson correlation coefficient was conducted to improve the prediction efficiency and reduce the irrelevant input parameters.Compared with LSTM model,LSTM-attention model has higher accuracy.The mean value of coefficient of determination(R^(2))increases from 0.625 to 0.736,and the mean value of root mean square error(RMSE)decreases from 3.31 to 2.24.The proposed LSTM-attention model can provide an effective prediction for attitude and position of shield machine in practical tunneling engineering.

Autonomous flight control with different strategies applied during the complete flight cycle for flapping-wing flying robots

Flapping-wing flying robots(FWFRs),especially large-scale robots,have unique advantages in flight efficiency,load capacity,and bionic hiding.Therefore,they have significant potential in environmental detection,disaster rescue,and anti-terrorism explosion monitoring.However,at present,most FWFRs are operated manually.Some have a certain autonomous ability limited to the cruise stage but not the complete flight cycle.These factors make an FWFR unable to give full play to the advantages of flapping-wing flight to perform autonomous flight tasks.This paper proposed an autonomous flight control method for FWFRs covering the complete process,including the takeoff,cruise,and landing stages.First,the flight characteristics of the mechanical structure of the robot are analyzed.Then,dedicated control strategies are designed following the different control requirements of the defined stages.Furthermore,a hybrid control law is presented by combining different control strategies and objectives.Finally,the proposed method and system are validated through outdoor flight experiments of the HIT-Hawk with a wingspan of 2.3 m,in which the control algorithm is integrated with an onboard embedded controller.The experimental results show that this robot can fly autonomously during the complete flight cycle.The mean value and root mean square(RMS)of the control error are less than 0.8409 and 3.054 m,respectively,when it flies around a circle in an annular area with a radius of 25 m and a width of 10 m.