En écotoxicologie, les effets des substances toxiques sur les organismes vivants sont classiquement mesurés au niveau individuel, en laboratoire et selon des normes, ce qui assure la reproductibilité des bioessais et le contrôle des facteurs environnementaux. Ces tests standardisés, en toxicité aiguë ou chronique, portent généralement sur la survie, la reproduction et la croissance d'organismes modèles de laboratoire ; leur analyse statistique conduit à l'estimation de concentrations critiques d'effet. Ce sont ces concentrations qui sont utilisées en analyse quantitative du risque en écotoxicologie. Cependant, pour l'estimation d'un même type de concentration critique d'effet, différentes méthodes/modèles peuvent être utilisés qui sont plus ou moins adaptés en fonction du type de jeux de données. Le premier objectif de ce travail de thèse est donc de sélectionner les méthodes/modèles les plus adaptés afin d'améliorer l'analyse des données issus des tests de toxicité et donc l'estimation des concentrations critiques d'effet. Habituellement, les jeux de données sont construits à partir de tests standards en fonction de l'organisme étudié : la durée du test est généralement fixée et des recommandations sont faites, par exemple sur le nombre minimal d'organismes à exposer à un nombre minimal de concentrations. Il est donc légitime de penser que ces recommandations ne sont pas forcément les plus adaptées pour toutes les concentrations critiques d'effet et les méthodes/modèles les plus adaptés. C'est pourquoi, le deuxième objectif de cette thèse est d'optimiser les plans d'expérience afin d'aller soit vers une amélioration des estimations des concentrations critiques d'effet à coût constant soit pour un même niveau de qualité des estimations, d'éviter le gaspillage en temps et en organismes / In ecotoxicology, the effects of toxic compounds on living organisms are usually measured at the individual level, in the laboratory and according to standards. This ensures the reproducibility of bioassays and the control of environmental factors. Bioassays, in acute or chronic toxicity, generally apply to survival, reproduction and growth of organisms. The statistical analysis of standardized bioassays classically leads to the estimation of critical effect concentrations used in risk assessment. Nevertheless, several methods/models are used to determine a critical effect concentration. These methods/models are more and less adapted to the data type. The first aim of this work is to select the most adapted methods/models to improve data analysis and so the critical effect concentration estimation. Usually, data sets are built from standard bioassays and so follow recommendations about exposure duration, number and range of tested concentrations and number of individuals per concentration. We can think that these recommendations are not the most adapted for each critical effect concentration and each method/model. That’s why, the second aim of this work is to optimize the experimental design in order to improve the critical effect concentration estimations for a fixed cost or at least to reduce the waste of time and organisms

Thermophysical characterization of anisotropic composite materials is extremely important in the control of today fabrication processes and in the prediction of structure failure due to thermal stresses. Accuracy in the estimation of the thermal properties can be improved if the experiments are designed carefully. However, on one hand, the typically used parametric study for the design optimization is tedious and time intensive. On the other hand, commonly used gradient-based estimation methods show instabilities resulting in nonconvergence when used with models that contain correlated or nearly correlated parameters. The objectives of this research were to develop systematic and reliable methodologies for both Experimental Design Optimization (EDO) used for the determination of thermal properties, and Simultaneous Parameter Estimation (SPE). Because of their advantageous features, Genetic Algorithms (GAs) were investigated for use as a strategy for both EDO and SPE. The EDO and SPE approaches used involved the maximization of an optimality criterion associated with the sensitivity matrix of the unknown parameters, and the minimization of the ordinary least squares error, respectively. Two versions of a general-purpose genetic-based program were developed: one is designed for the analysis of any EDO / SPE problems for which a mathematical model can be provided, while the other incorporates a control-volume finite difference scheme allowing for the practical analysis of complex problems. The former version was used to illustrate the genetic performance on the optimization of a difficult mathematical test function. Two test cases previously solved in the literature were first analyzed to demonstrate and assess the GA-based {EDO/SPE} methodology. These problems included the optimization of one and two dimensional designs for the estimation at ambient temperature of two and three thermal properties, respectively (effective thermal conductivity parallel and perpendicular to the fibers plane and effective volumetric heat capacity), of anisotropic carbon/epoxy composite materials. The two dimensional case was further investigated to evaluate the effects of the optimality criterion used for the experimental design on the accuracy of the estimated properties. The general-purpose GA-based program was then successively applied to three advanced studies involving the thermal characterization of carbon/epoxy anisotropic composites. These studies included the SPE of successively three, seven and nine thermophysical parameters, with for the latter case, a two dimensional EDO with seven experimental key parameters. In two of the three studies, the parameters were defined to represent the dependence of the thermal properties with temperature. Finally, the kinetic characterization of the curing of three thermosetting materials (an epoxy, a polyester and a rubber compound) was accomplished resulting in the SPE of six kinetic parameters. Overall, the GA method was found to perform extremely well despite the high degree of correlation and low sensitivity of many parameters in all cases studied. This work therefore validates the use of GAs for the thermophysical characterization of anisotropic composite materials. The significance in using such algorithms is not only the solution to ill-conditioned problems but also, a drastically cost savings in both experimental and time expenses as they allow for the EDO and SPE of several parameters at once. / Ph. D.