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.

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a b s t r a c tThis study focuses on the identification of concrete behavior under severe triaxial loading in order to better evaluate the vulnerability of sensitive infrastructure to near-field detonations or ballistic impacts. For the purpose of reproducing high stress levels with well-controlled loading paths, static tests have been conducted on concrete samples using a triaxial press offering very high capacities (stress levels of around 1 GPa). It is a well-known fact that the concrete drying process is a slow phenomenon. Massive concrete structures, such as bridge piers, dams and nuclear reactors, could retain a quasi-saturated core throughout most of their lifetime, even though their facing dries very quickly. The objective of this article is to evaluate the effect of the saturation ratio on concrete behavior under high confinement; this article will present triaxial test results on concrete samples over a saturation ratio range extending from dried to quasi-saturated concretes. The subsequent analysis of results will show that the saturation ratio exerts a major influence on concrete behavior, particularly on both the concrete strength capacity and shape of the limit state curve for saturation ratios above 50%. This analysis also highlights that while the strength of dried concrete strongly increases with confining pressure, it remains constant over a given confining pressure range for either wet or saturated samples.

Hot wire measurements of longitudinal and transverse increments are performed in three different types of flows on a large range of Reynolds numbers (100≲Rλ≲3000). An improved technique based on cumulant expansion of velocity structure functions is used to estimate the spreading of the pdfs and to study their scaling properties in the inertial range. Thus, the rate of intermittency depth through the scales of flow, called here β(Rλ), is experimentally introduced, and it is shown that β(Rλ) has a universal behavior on a very large Reynolds numbers range.

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.

We study the experimental dependence of the third-order velocity structure function on the Taylor based Reynolds number, obtained in different flow types over the range 72рR р2260. As expected, when the Reynolds number is increasing, the third-order velocity structure functions ͑plotted in a compensated way͒ converge very slowly to a possible Ϫ4/5 plateau value according to the Kolmogorov 41 theory. Actually, each of these normalized third-order functions exhibits a maximum, at a scale close to the Taylor microscale . In this Brief Communication, we show that experimental data are in good agreement with the recent predictions of Qian and Lundgren. We also suggest that, from an experimental point of view, a log-similar plot suits very well to study carefully the behavior of the third-order velocity structure functions with the flow Reynolds number.Considerable attention has been given to the famous ''Ϫ4/5'' law, 1,2 characteristic of inertial scales in fully developed turbulence which is written as ͗͑␦u͑r͒͒ 3 ͘ӍϪ4/5͗⑀͘r ͑1͒ ͑r and ⑀ are the inertial range separation and the mean dissipation rate, respectively͒. Even though the previous relation is strictly valid for Reynolds number tending to the infinity, experimental data seemed to verify it as soon as a conspicuous power-law scaling range exists ͑i.e., for R у500), whatever the flow type. To emphasize this feature, the third-order longitudinal velocity structure functions are usually compensated by the opposite of the Kolmogorov scaling term ͓de-fined as Ϫ͗(␦u(r)) 3 ͘/(͗⑀͘r) and hereafter noted S 3 (r)], and are plotted in log-log coordinates ͑cf. Refs. 3, 4, and references therein͒. Actually, the difference between S 3 (r) and 4/5 is experimentally very difficult to determine. Indeed, the identification of S 3 (r) with 4/5 is very often the most accurate way to experimentally determine ͗⑀͘ at large R .However, some experiments have shown a very slow convergence towards the above infinite Reynolds result when R is raised. For instance, in a ''Von Karman'' flow, Moisy 5 showed that S 3 (r) tends to 4/5 like R Ϫ6/5 . By another way, Mydlarski and Warhaft, 6 found a slow convergence of the slope of the energy spectrum measured in a grid turbulence towards Ϫ5/3 as R increases. For the peculiar scale rϭ, Pearson and Antonia 7 found that S 3 () does not exactly scale as the Kolmogorov prediction for Reynolds number up to R Ӎ1000, reflecting the large scale anisotropy effects on the inertial range.On the theoretical side, in some recent papers, 8,9 Qian examines this problem, dicussing systematically the influence of the energy input. Qian predicts the slowness of the convergence, but points out noticeable differences between various types of power injection. Lundgren 10,11 goes back to the Karman-Howarth equation to propose a solution in the decaying self-similar case which seems not easily compatible with the possibility of intermittency.In the present state, it is difficult to decide if an experimental discrepancy with the Lundgren prediction is due to ͑i͒ a peculi...

Abstract. Politano and Pouquet's law, a generalization of Kolmogorov's four-fifths law to incompressible MHD, makes it possible to measure the energy cascade rate in incompressible MHD turbulence by means of third-order moments. In hydrodynamics, accurate measurement of thirdorder moments requires large amounts of data because the probability distributions of velocity-differences are nearly symmetric and the third-order moments are relatively small. Measurements of the energy cascade rate in solar wind turbulence have recently been performed for the first time, but without careful consideration of the accuracy or statistical uncertainty of the required third-order moments. This paper investigates the statistical convergence of third-order moments as a function of the sample size N. It is shown that the accuracy of the third-moment (δv ) 3 depends on the number of correlation lengths spanned by the data set and a method of estimating the statistical uncertainty of the thirdmoment is developed. The technique is illustrated using both wind tunnel data and solar wind data.

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