The parameters of the constitutive model, the creep model, and the wetting model of materials of the Nuozhadu high earth-rockfill dam were back-analyzed together based on field monitoring displacement data by employing an intelligent back-analysis method. In this method, an artificial neural network is used as a substitute for time-consuming finite element analysis, and an evolutionary algorithm is applied for both network training and parameter optimization. To avoid simultaneous back-analysis of many parameters, the model parameters of the three main dam materials are decoupled and back-analyzed separately in a particular order. Displacement back-analyses were performed at different stages of the construction period, with and without considering the creep and wetting deformations. Good agreement between the numerical results and the monitoring data was obtained for most observation points, which implies that the back-analysis method and decoupling method are effective for solving complex problems with multiple models and parameters. The comparison of calculation results based on different sets of back-analyzed model parameters indicates the necessity of taking the effects of creep and wetting into consideration in the numerical analyses of high earth-rockfill dams. With the resulting model parameters, the stress and deformation distributions at completion are predicted and analyzed.

The safety issues of geotechnical structures are often related to tension-induced cracking of geomaterials. Conventional finite element analysis often experiences difficulties in re-meshing and adaptively analyzing the crack propagation region. In this work, the Radial Point Interpolation Method (RPIM) is adaptively coupled with the Finite Element Method (FEM) to simulate tensile crack initiation and propagation in earth structures. In the algorithm, FEM mesh in identified crack regions is automatically converted to RPIM nodes and then refined for crack analysis. The behavior of soil under tension and the brittle and quasi-brittle failure types are investigated based on typical tensile test results. Especially, the crack-induced anisotropic and softening behaviors of soils are appropriately simulated using the smeared crack approach. Two types of soil cracking experiments, the four-point bending test and centrifuge model test, were conducted in the laboratory and simulated by the proposed approach. Good agreements between the simulated and experimental results have been obtained, which demonstrates the effectiveness of this coupled method for 2D and 3D simulations of tensile cracking in earth structures.

This study concerns the development and preliminary experimental verification of a topological sensitivity–based platform for the material characterization of tissue anomalies exposed by vibroacoustography-type imaging techniques. Vibroacoustography (VA) is a high-resolution imaging method that has been applied to the detection of pathological changes in soft tissues. Although the data provided by this method is related to the mechanical properties of tissue, the viscoelastic parameters of the object cannot be estimated by this imaging method itself. Topological sensitivity (TS) method is a data processing methodology that can be used to estimate the viscoelastic parameters of an object from vibration data. In this study, the concept of topological sensitivity is applied to interpret the vibroacoustography measurements for the purpose of lesion characterization. In the proposed approach, the topological sensitivity function, which signifies the variation of a given cost functional when an infinitesimal inclusion with trial material parameters is placed at the location of a point force, is formulated in terms of the adjoint field. The effectiveness of the resulting formula as a material indicator for lesion characterization is demonstrated by estimating the relative elastic parameters of a well-controlled neoprene sphere embedded in a tissue-mimicking phantom specimen.