Estimating Dynamic Properties from Static Tests

The applicability of various types of constitutive models to estimating dynamic material properties for soils from the results of static shear tests is briefly reviewed. The primary obstacle to making such predictions is the limiting resolution of conventional static tests. A simple procedure using empirical relationships to interpolate beyond the limit of the static shear tests is suggested for use in preliminary analysis and in cases where cyclic test data is not available

One- and Two-Dimensional Analysis of Earth Dams

Earth dams may experience reduction in shear strength due to seismically induced pore pressures. Such reduction may be large enough to result in large deformations and eventual loss of the reservoir. While the analysis of embankment dams subject to earthquake loading is a complicated process, it is required for evaluation of seismic stability. In particular, the possibility of liquefaction in older, hydraulically-filled or otherwise poorly compacted dams during earthquakes presents a threat that must be addressed. This paper compares two methods of calculating the peak dynamic shear stress (critical to liquefaction evaluation) that occurs in an embankment during an earthquake. The first method is a one-dimensional analysis, which is simple, rapid and inexpensive. The second method is a two-dimensional finite element analysis, which is complicated, long and expensive. Because it is more desirable to use the simpler one-dimensional analysis, the results from the two analyses were compared and indicated that for slopes up to 35° the stresses were comparable

Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline

International audienceWe propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metric mapping (LDDMM). We first exhibit the limited predictive abilities of geodesic regression extrapolation on this group. Building on the recent concept of parallel curves in shape manifolds, we then introduce a second predictive protocol which personalizes previously learned trajectories to new subjects, and investigate the relative performances of two parallel shifting paradigms. This design only requires the baseline imaging data. Finally, coefficients encoding the disease dynamics are obtained from longitudinal cognitive measurements for each subject, and exploited to refine our methodology which is demonstrated to successfully predict the follow-up visits