Compression failure conditions of concrete-granite combined body with different roughness interface

Bedrock and concrete lining are typical composite structures in the engineering field and the stability of the geological body and engineering body is directly connected to the mechanical properties of the composite body.Under this background,the study provides the transverse isotropic equivalent model of concrete-granite double-layer composite based on the notion of strain energy equivalence.Assuming that the strength failure of concrete and granite meets the Mohr-Coulomb criterion,then the strength failure model of the combined body considering the joint roughness coefficient(JRC)is derived,and the influences of JRC,the height ratio of concrete to granite,and confining pressure on the strength failure characteristics of the combined body are emphatically analyzed.Finally,the model applicability is illustrated by the uniaxial and triaxial compression tests on concrete monomer,granite monomer and concretegranite composite samples(CGCSs)with different JRCs.The results revealed that the compressive strength of the composite is closer to the concrete with lower strength in the combined body under different confining pressures.Adding interface roughness causes to raise the compressive strength of the composite due to interfacial adhesion between concrete and granite,and a slowing growth trend is observed in compressive strength as roughness.The model can provide a certain reference for the stability design and evaluation of engineering rock mass.

Effect of Deformation Condition on Axial Compressive Precision Forming Process of Tube with Curling Die

The effect of the deformation condition on the axial compressive precision forming process of tube with curling die was investigated by using a rigid-plastic FEM. The results show that the forming accuracy depends mainly on geometric condition rp/d0, little on tube material properties and friction condition; the relative gap △/2rp of double-walled tubes obtained decreases with Increasing rp/d0, and there is a parameter k for a given to/do or rp/t0, when rp/d0 >k, △/2rp< 1, otherwise △/2rp>1.

Compressed Least Squares Algorithm of Continuous-Time Linear Stochastic Regression Model Using Sampling Data

In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter sparsity.At each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter.Then,the original high-dimensional sparse unknown parameter is recovered by a reconstruction method.By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals.At last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.

Stability and performance analysis of the compressed Kalman filter algorithm for sparse stochastic systems

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.