A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

The Impact of Rate of Feeding Advancement after Early Initiation of Enteral Nutrition in Critically Ill, Underweight Patients: A Single-Center Retrospective Chart Review

Background: The optimal rate of feeding advancement after initiation of early enteral nutrition (EEN) for underweight, critically ill patients is unknown. Methods: We conducted a retrospective chart review in intensive care unit (ICU) patients with a body mass index (BMI) < 20.0 kg/m2. Patients were categorized into Group R, which reached the energy target within 3 days of EEN initiation, and Group S, which reached the energy target 4 or more days after EEN initiation. Results: A total of 65 patients with a median age of 73 years were included in the study. No significant differences were observed between the two groups for all-cause mortality, ICU-free days, or length of hospital stay. Ventilator-free days (VFDs) were significantly fewer in Group R than in Group S (18.0 [0.0 - 22.0] vs. 21.0 [16.3 - 24.8] days;P = 0.046). A significantly higher number of patients requiring mechanical ventilation (MV) at hospital discharge were observed in Group R than in Group S (29% vs. 8%;P = 0.030). Multivariable analyses with adjustment for confounders found that days required to reach target energy intake after EEN initiation were significantly and independently associated with the requirement for MV at hospital discharge, but not with VFDs. Conclusion: A slow rate of feeding advancement after initiation of EEN in critically ill patients having a BMI of <20.0 kg/m2 might be associated with a reduced requirement for MV at hospital discharge. These results require confirmation in a large multicenter trial of underweight, critically ill patients.

Patients 60 Years of Age and Older Should Have the Same Chance for Heart Transplantation or Not?

Heart transplantation is considered the best treatment modality for advanced heart disease. While old age has conventionally been considered a contraindication for heart transplantation due to the reported adverse effect of advanced age, however donor hearts＇ shortage continues to stimulate the discussion about the recipient＇s upper age limit. Our study was based on a retrospective analysis for the results of 52（18%） patients aged 60 years and older undergoing heart transplantation between May 2008 and December 2015, and these patients were compared with 236（82%） adult recipients who were younger than 60 years at the time of transplantation and during the same period. In older group, 71% were males with the mean age of 63.38±3.55 years, and in younger group, 83.4% were males with a mean age of 43.72±11.41 years（P=0.27）. Dilated cardiomyopathy was the most common indication for transplantation among patients in both groups（P=0.147）. In older group, the 3-month survival rate was higher than that in younger group（P=0.587）, however the 6-month survival rate showed no significant difference（P=0.225）. Although the 1-year survival rate was higher in older group（P=0.56）, yet the 3-year survival rate between the two groups showed no statistically significant difference（P=0.48）. According to our experience among older heart transplant candidates who were 60 years and older, we believe that advanced age should not be an excluding criterion to cardiac transplantation.

Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.

Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

TBM performance prediction using LSTM-based hybrid neural network model:Case study of Baimang River tunnel project in Shenzhen,China

Accurately predicting tunnel boring machine(TBM)performance is beneficial for excavation efficiency enhancement and risk miti-gation of TBM tunneling.In this paper,we develop a long short-term memory(LSTM)based hybrid intelligent model to predict two key TBM performance parameters(advance rate and cutterhead torque).The model combines the LSTM,BN,Dropout and Dense layers to process the raw data and improve the fitting quality.The features,including the ground formation properties,tunnel route cur-vature,tunnel location and TBM operational parameters,are divided into historical/real-time time-varying parameters,time-invariant parameters and historical/real-time output prediction data.The effectiveness of the proposed model is verified based on a large moni-toring database of the Baimang River Tunnel Project in Shenzhen,south China.We then discuss the influence of the prediction mode,neural network structure and time division interval length of historical data on the prediction accuracy.The significance evaluation of input features shows that the historical output prediction has the largest influence on the prediction accuracy,and the influence of ground properties is secondary.It is also found that the correlations between input features and the output prediction are coincident with their interrelationships with the ground properties and ease of TBM excavation.Finally,it is found that the prediction results are most affected by the total propulsion force followed by the rotation speed of the cutterhead.The established model can provide useful guidance for construction personnel to roughly grasp the possible TBM status from the prediction results when adjusting the operational parameters.