SUMMARYThis paper is dedicated to the identification of constitutive parameters of the Mohr-Coulomb constitutive model from in situ geotechnical measurements. A pressuremeter curve and the horizontal displacements of a sheet pile wall retaining an excavation are successively used as measurements. Two kinds of optimization algorithms are used to minimize the error function, the first one based on a gradient method and the second one based on a genetic algorithm. The efficiency of each algorithm related to the error function topology is discussed. Finally, it is shown that the use of a genetic algorithm to identify the soil parameters seems particularly suitable when the topology of the error function is complex.

The permeability of intact Barre granite is determined by conducting transient hydraulic pulse tests that induce radial flow in saturated granite cylinders containing a water-filled co-axial cavity. Saturated specimens of granite are also subjected to uniform heating on the outer cylindrical surface and the permeability of the granite is measured at the termination of a heating sequence. The theoretical basis for the use of simplified analytical solutions for evaluating the decay of the pressure pulse is examined and the permeability of the granite in the pre-heating and post-heating states is inferred from the results of pulse tests. The reported investigations document one of the limited numbers of large-scale laboratory tests conducted to determine the permeability characteristics of intact granite. The results of the permeability measurements indicate the absence of any appreciable influences of micro structural alterations in the fabric of the granite due to its heating in a saturated condition, at an average temperature of 140°Celsius.

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SUMMARYThis study concerns the identification of constitutive models from geotechnical measurements by inverse analysis. Soil parameters are identified from measured horizontal displacements of sheet pile walls and from a measured pressuremeter curve. An optimization method based on a genetic algorithm (GA) and a principal component analysis (PCA), developed and tested on synthetic data in a previous paper, is applied. These applications show that the conclusions deduced from synthetic problems can be extrapolated to real problems. The GA is a robust optimization method that is able to deal with the non-uniqueness of the solution in identifying a set of solutions for a given uncertainty on the measurements. This set is then characterized by a PCA that gives a first-order approximation of the solution as an ellipsoid. When the solution set is not too curved in the research space, this ellipsoid characterizes the soil properties considering the measured data and the tolerate margins for the response of the numerical model. Besides, optimizations from different measurements provide solution sets with a common area in the research space. This intersection gives a more relevant and accurate identification of parameters. Finally, we show that these identified parameters permit to reproduce geotechnical measurements not used in the identification process.