Construction of shallow buried large-span metro stations using the small pipe roof-beam method

In relation to the Shifu Road Station project on Line 4 of the Shenyang Metro in China,a small-pipe roof-beam method for constructing subway stations is presented.First,a numerical simulation was performed to optimize the supporting parameters of the proposed method and determine the design scheme.Subsequently,the deformation of the pipe roof and surface settlement during the construction process were investigated.Finally,the surface settlement attributed to the excavation was studied through field monitoring,and the proposed method was compared with other methods.The results show that an increase in the pipe-roof spacing has little effect on the surface settlement and piperoof deformation.The bearing capacity of the pipe roof can be efficiently utilized once the flexural stiffness reaches 2EI,and the flexural stiffness is not the dominant factor controlling the deformation.The essential stages in controlling surface settlement are the excavations of the transverse pilot tunnels and the soil between them.The final settlement value of the ground was 24.1 mm,resulting in a reduction in the construction period by at least five months while satisfying the control requirements.

Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.