Bearing capacity and failure mechanism of strip footings on anisotropic sand

Sand typically exhibits anisotropic internal structure (or fabric), and the fabric anisotropy has a dramatic influence on the mechanical behavior of sand. Meanwhile, the fabric evolves when sand is subjected to external loading. This eventually makes the response of strip footings on sand dependent on fabric anisotropy and fabric evolution. A numerical investigation on this effect is presented using a critical state sand model accounting for fabric evolution. The model parameters are determined based on plane strain and triaxial compression test data, and the model performance is validated by centrifuge tests for strip footings on dry Toyoura sand. The bearing capacity of strip footings is found to be dependent on the bedding plane orientation of dense sand. However, this effect vanishes as the sand density decreases, though the slope of the force-displacement curve is still lower for vertical bedding. Progressive failure is observed for all the simulations. General shear failure mode occurs in dense and medium dense sand, and the punching shear mode is the main failure mechanism for loose sand. In general shear failure, unsymmetrical slip lines develop for sand with an inclined bedding plane due to the noncoaxial sand behavior caused by fabric anisotropy. For strip footing on sand with horizontal bedding, the bearing capacity and failure mechanism are primarily affected by the sand density. The bearing capacity of a strip footing is higher when the sand fabric is more isotropic for the same soil density. An isotropic model can give significant overestimation on the bearing capacity of strip footings

Optimal Policies for Selling New and Remanufactured Products

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138248/1/poms12724.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138248/2/poms12724-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138248/3/poms12724_am.pd

Fractional elastoplastic constitutive model for soils based on a novel 3D fractional plastic flow rule

A novel three-dimensional (3D) fractional plastic flow rule that is not limited by the coordinate basis of the differentiable function is proposed based on the fractional derivative and the coordinate transformation. By introducing the 3D fractional plastic flow rule into the characteristic stress space, a 3D fractional elastoplastic model for soil is established for the first time. Only five material parameters with clear physical significance are required in the proposed model. The capability of the model in capturing the strength and deformation behaviour of soils under true 3D stress conditions is verified by comparing model predictions with test results

A novel transversely isotropic strength criterion for soils based on a mobilized plane approach

The peak shear strength rules of transversely isotropic soils are stress state dependent and dependent on relative orientation between bedding plane and principal stress. Accordingly, the shear strength of transversely isotropic soils exhibits two primary characteristics: (i) the strength curve on the deviatoric plane is asymmetrical with respect to three principal stress axes; (ii) the shear strength changes with the direction angle of the bedding plane when the intermediate principal stress coefficient is a constant. In this paper, the mobilized plane is introduced and used to reveal the failure mechanism of soils. By projecting the microstructure tensor of transversely isotropic soils onto the normal of the mobilized plane, the directionality of the transversely isotropic soils is introduced into the friction rules on the mobilized plane, and a transversely isotropic strength parameter is proposed. The proposed strength parameter can extend isotropic strength criteria into transversely isotropic strength criteria. This mobilized plane approach is used to establish a novel transversely isotropic nonlinear unified strength criterion (TI-NUSC). The difficulty to establish a unified description of the asymmetrical strength curve and its evolution with direction angle is overcome by the established criterion. Comparisons between available test results and the TI-NUSC shows that the TI-NUSC can successfully describe these two primary peak strength characteristics

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains signi�cant variables. �ut it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the �rst stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. e simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization

Water wave optimization (WWO) is a novel metaheuristic method that is based on shallow water wave theory, which has simple structure, easy realization, and good performance even with a small population. To improve the convergence speed and calculation precision even further, this paper on elite opposition-based strategy water wave optimization (EOBWWO) is proposed, and it has been applied for function optimization and structure engineering design problems. There are three major optimization strategies in the improvement: elite opposition-based (EOB) learning strategy enhances the diversity of population, local neighborhood search strategy is introduced to enhance local search in breaking operation, and improved propagation operator provides the improved algorithm with a better balance between exploration and exploitation. EOBWWO algorithm is verified by using 20 benchmark functions and two structure engineering design problems and the performance of EOBWWO is compared against those of the state-of-the-art algorithms. Experimental results show that the proposed algorithm has faster convergence speed, higher calculation precision, with the exact solution being even obtained on some benchmark functions, and a higher degree of stability than other comparative algorithms

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction