A site in Boissy-le-Chatel, France, has been instrumented with a meteorological station, piezometers, thermocouples and time domain reflectometry equipment, in order to monitor solar radiation energy, precipitation, runoff, wind speed, air temperature, air humidity, soil temperature, soil volumetric water content, and underground water table levels. Data recorded in April 1999 were used to validate predictions of soil water content changes based on the works of Blight and Wilson et al. In the elaborated approach, Penman's equation is employed to evaluate the potential evapotranspiration, and Darcy and Fick's laws are used to describe the flows of liquid and vapour water respectively. The soil water content and hydraulic conductivity are presumed to be functions of soil suction, and the coefficient of latent heat of vaporisation of water is assumed to be a linear function of temperature. The formula proposed by De Vries is used for calculation of the thermal conductivity coefficient. The results of Wilson et al. for laboratory conditions were used to validate the model used. To simulate field conditions, climatic data were used to calculate the volumetric water content and temperature profiles in the soil, and the calculated values were compared with direct measurements. Satisfactory results were obtained.

Please cite this article as: Lu Y, Deformation and failure mechanism of slope in three dimensions, Abstract: Understanding three-dimensional (3D) slope deformation and failure mechanism and corresponding stability analyses are crucially important issues in geotechnical engineering. In this paper, the mechanisms of progressive failure with thrust-type and pull-type landslides are described in detail. It is considered that the post-failure stress state and the pre-peak stress state may occur at different regions of a landslide body with deformation development, and a critical stress state element (or the soil slice block) exists between the post-failure stress state and the pre-peak stress state regions. In this regard, two sorts of failure modes are suggested for the thrust-type and three sorts for pull-type landslides, based on the characteristics of shear stress and strain (or tensile stress and strain). Accordingly, a new joint constitutive model (JCM) is proposed based on the current stability analytical theories, and it can be used to describe the mechanical behaviors of geo-materials with softening properties. Five methods, i.e. CSRM (comprehensive sliding resistance method), MTM (main thrust method), CDM (comprehensive displacement method), SDM (surplus displacement method), and MPM (main pull method), for slope stability calculation are proposed. The S-shaped curve of monitored displacement vs. time is presented for different points on the sliding surface during progressive failure process of landslide, and the relationship between the displacement of different points on the sliding surface and height of landslide body is regarded as the parabolic curve. The comparisons between the predicted and observed load-displacement and displacement-time relations of the points on the sliding surface are conducted. The classification of stable/unstable displacement-time curves is proposed. The definition of the main sliding direction of a landslide is also suggested in such a way that the failure body of landslide (simplified as "collapse body") is only involved in the main sliding direction, and the strike and the dip are the same as the collapse body. The rake angle is taken as the direction of the sum of sliding forces or the sum of displacements in collapse body, in which the main slip direction is dependent on progressive deformation. The reason of non-convergence with finite element method (FEM) in calculating the stability of slope is also numerically analyzed, in which a new method considering the slip surface associated with the boundary condition is proposed. It is known that the boundary condition of sliding surface can be described by perfect elasto-plastic model (PEPM) and JCM, and that the stress and strain of a landslide can be described properly with the JCM.

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Conventional neural networks tend to fall into local extremum on large datasets, while the research on the strength of rubber concrete using intelligent algorithms to optimize artificial neural networks is limited. Therefore, to improve the prediction accuracy of rubber concrete strength, an artificial neural network model with hybrid algorithm optimization was developed in this study. The main strategy is to mix the simulated annealing (SA) algorithm with the particle swarm optimization (PSO) algorithm, using the SA algorithm to compensate for the weak global search capability of the PSO algorithm at a later stage while changing the inertia factor of the PSO algorithm to an adaptive state. For this purpose, data were first collected from the published literature to create a database. Next, ANN and PSO-ANN models are also built for comparison while four evaluation metrics, MSE, RMSE, MAE, and R2, were used to assess the model performance. Finally, compared with empirical formulations and other neural network models, the result shows that the proposed optimized artificial neural network model successfully improves the accuracy of predicting the strength of rubber concrete. This provides a new option for predicting the strength of rubber concrete.