RANDOM MICROSTRUCTURE FINITE ELEMENT METHOD FOR EFFECTIVE NONLINEAR PROPERTIES OF COMPOSITE MATERIALS

Some theoretical methods have been reported to deal with nonlinear problems of composite materials but the accuracy is not so good. In the meantime, a lot of linear problems are difficult to be managed by the theoretical methods. The present study aims to use the developed method, the random microstructure finite element method, to deal with these nonlinear problems. In this paper, the random microstructure finite element method is used to deal with all three kinds of nonlinear property problems of composite materials. The analyzed results suggest the influences of the nonlinear phenomena on the effective properties of composite materials are significant and the random microstructure finite element method is an effective tool to investigate the nonlinear problems.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Stability and reliability of pit slopes in surface mining combined with underground mining in Tonglushan mine

Slope stability is of critical importance in the process of surface-underground mining combination. The influence of underground mining on pit slope stability was mainly discussed, and the self-stabilization of underground stopes was also studied. The random finite element method was used to analyze the probability of the rock mass stability degree of both pit slopes and underground stopes. Meanwhile, 3D elasto-plastic finite element method was used to research into the stress, strain and rock mass failure resulting from mining. The results of numerical simulation indicate that the mining of the underground test stope has certain influence on the stability of the pit slope, but the influence is not great. The safety factor of pit slope is decreased by 0.06, and the failure probability of the pit slope is increased by 1.84%. In addition, the strata yielding zone exists around the underground test stope. The results basically conform to the information coming from the field monitoring.

Spatially variable soils affecting geotechnical strip foundation design

Natural soil variability is a well-known issue in geotechnical design,although not frequently managed in practice.When subsoil must be characterized in terms of mechanical properties for infrastructure design,random finite element method(RFEM)can be effectively adopted for shallow foundation design to gain a twofold purpose:(1)understanding how much the bearing capacity is affected by the spatial variability structure of soils,and(2)optimisation of the foundation dimension(i.e.width B).The present study focuses on calculating the bearing capacity of shallow foundations by RFEM in terms of undrained and drained conditions.The spatial variability structure of soil is characterized by the autocorrelation function and the scale of fluctuation(δ).The latter has been derived by geostatistical tools such as the ordinary Kriging(OK)approach based on 182 cone penetration tests(CPTs)performed in the alluvial plain in Bologna Province,Italy.Results show that the increase of the B/δratio not only reduces the bearing capacity uncertainty but also increases its mean value under drained conditions.Conversely,under the undrained condition,the autocorrelation function strongly affects the mean values of bearing capacity.Therefore,the authors advise caution when selecting the autocorrelation function model for describing the soil spatial variability structure and point out that undrained conditions are more affected by soil variability compared to the drained ones.

Deformation and stability of the seawall,considering the strength uncertainty of cement mixing piles

The cement mixing (CM) pile is a common method of improving soft offshore ground. The strength growth of CM piles under complex conditions is affected by many factors, especially the cement and moisture contents, and shows significant uncertainty. To investigate the stochasticity of the early strength of CM piles and its impact on the displacement and stability of a seawall, a series of laboratory tests and numerical analyses were carried out in this study. Vane shear tests were conducted on the cement-solidified soil to determine the relationships between the undrained shear strength s_(u) of the cement soil curing in the seawater and the cement content a_(c), as well as the in situ soil moisture content w. It can be inferred that the 24 h undrained shear strength follows a normal distribution. A numerical model considering the random CM pile strength was established to investigate the deformation of the seawall. Due to the uncertainty of CM pile strength, the displacement of the seawall demonstrates a certain discreteness. The decrease of the mean undrained shear strength of CM piles causes a corresponding increase in the average displacement of the seawall. When the mean strength of CM piles is lower than a certain threshold, there is a risk of instability. Furthermore, the heterogeneity of the strength within an individual CM pile also has an impact on seawall displacement. Attention should be paid to the uncertainty of CM pile strength to control displacement and stability.

Probability-based method using RFEM for predicting wall deflection caused by excavation

This study employs the random finite element method (RFEM) to analyze the wall deflection caused by excavation. The RFEM combined random fields of material properties with the FEM through the Monte Carlo simulation. A well-documented excavation case history is employed to evaluate the influence of uncertainty of analysis parameters. This study shows that RFEM can provide reasonable estimations of the exceedance probability of wall deflection caused by excavation, and has the potential to be a useful tool to account for the uncertainties of material and model parameters in the numerical analysis.