Stochastic User Equilibrium (SUE) models allow the representation of the perceptual and preferential differences that exist when drivers compare alternative routes through a transportation network. However, as an effect of the used choice models, conventional applications of SUE are based on the assumption that all available routes have a positive probability of being chosen, however unattractive. In this paper, a novel choice model, the Bounded Choice Model (BCM), is presented along with network conditions for a corresponding Bounded SUE. The model integrates an exogenously-defined bound on the random utility of the set of paths that are used at equilibrium, within a Random Utility Theory (RUT) framework. The model predicts which routes are used and unused (the choice sets are equilibrated), while still ensuring that the distribution of flows on used routes accords to a Discrete Choice Model. Importantly, conditions to guarantee existence and uniqueness of the Bounded SUE are shown. Also, a corresponding solution algorithm is proposed and numerical results are reported by applying this to the Sioux Falls network.

When the finite element method is used to solve boundary value problems, the corresponding finite element mesh is appropriate if it is reflects the behavior of the true solution. A posteriori error estimators are suited to construct adequate meshes. They are useful to measure the quality of an approximate solution and to design adaptive solution algorithms. Singularly perturbed problems yield in general solutions with anisotropic features, e.g. strong boundary or interior layers. For such problems it is useful to use anisotropic meshes in order to reach maximal order of convergence. Moreover, the quality of the numerical solution rests on the robustness of the a posteriori error estimation with respect to both the anisotropy of the mesh and the perturbation parameters. There exist different possibilities to measure the a posteriori error in the energy norm for the singularly perturbed reaction-diffusion equation. One of them is the equilibrated residual method which is known to be robust as long as one solves auxiliary local Neumann problems exactly on each element. We provide a basis for an approximate solution of the aforementioned auxiliary problem and show that this approximation does not affect the quality of the error estimation. Another approach that we develope for the a posteriori error estimation is the hierarchical error estimator. The robustness proof for this estimator involves some stages including the strengthened Cauchy-Schwarz inequality and the error reduction property for the chosen space enrichment. In the rest of the work we deal with adaptive algorithms. We provide an overview of the existing methods for the isotropic meshes and then generalize the ideas for the anisotropic case. For the resulting algorithm the error reduction estimates are proven for the Poisson equation and for the singularly perturbed reaction-difussion equation. The convergence for the Poisson equation is also shown. Numerical experiments for the equilibrated residual method, for the hierarchical error estimator and for the adaptive algorithm confirm the theory. The adaptive algorithm shows its potential by creating the anisotropic mesh for the problem with the boundary layer starting with a very coarse isotropic mesh.

When the finite element method is used to solve boundary value problems, the corresponding finite element mesh is appropriate if it is reflects the behavior of the true solution. A posteriori error estimators are suited to construct adequate meshes. They are useful to measure the quality of an approximate solution and to design adaptive solution algorithms. Singularly perturbed problems yield in general solutions with anisotropic features, e.g. strong boundary or interior layers. For such problems it is useful to use anisotropic meshes in order to reach maximal order of convergence. Moreover, the quality of the numerical solution rests on the robustness of the a posteriori error estimation with respect to both the anisotropy of the mesh and the perturbation parameters. There exist different possibilities to measure the a posteriori error in the energy norm for the singularly perturbed reaction-diffusion equation. One of them is the equilibrated residual method which is known to be robust as long as one solves auxiliary local Neumann problems exactly on each element. We provide a basis for an approximate solution of the aforementioned auxiliary problem and show that this approximation does not affect the quality of the error estimation. Another approach that we develope for the a posteriori error estimation is the hierarchical error estimator. The robustness proof for this estimator involves some stages including the strengthened Cauchy-Schwarz inequality and the error reduction property for the chosen space enrichment. In the rest of the work we deal with adaptive algorithms. We provide an overview of the existing methods for the isotropic meshes and then generalize the ideas for the anisotropic case. For the resulting algorithm the error reduction estimates are proven for the Poisson equation and for the singularly perturbed reaction-difussion equation. The convergence for the Poisson equation is also shown. Numerical experiments for the equilibrated residual method, for the hierarchical error estimator and for the adaptive algorithm confirm the theory. The adaptive algorithm shows its potential by creating the anisotropic mesh for the problem with the boundary layer starting with a very coarse isotropic mesh.

Dans cette thèse nous développons des estimations d'erreur a posteriori par équilibrage de flux pour la poro-mécanique et la poro-plasticité.En se basant sur ces estimations, nous proposons des algorithmes adaptatifs pour la résolution numérique de problèmes en mécanique des sols.Le premier chapitre traite des problèmes en poro-élasticité linéaire.Nous obtenons une borne garantie sur l'erreur en utilisant des reconstructions équilibrées et $H({rm div})$-conformes de la vitesse de Darcy et du tenseur de contraintes mécaniques.Nous appliquons cette estimation dans un algorithme adaptif pour équilibrer les composantes de l'erreur provenant de la discrétisation en espace et en temps pour des simulations en deux dimensions.La contribution principale du chapitre porte sur la reconstruction symétrique du tenseur de contraintes.Dans le deuxième chapitre nous proposons une deuxième technique de reconstruction du tenseur de contraintes dans le cadre de l'élasticité nonlinéaire.En imposant la symétrie faiblement, cette technique améliore les temps de calcul et facilite l'implémentation.Nous démontrons l'éfficacité locale et globale des estimateurs obtenus avec cette reconstruction pour une grande classe de lois en hyperélasticité.En ajoutant un estimateur de l'erreur de linéarisation, nous introduisons des critères d'arrêt adaptatifs pour le solveur de linéarisation.Le troisième chapitre est consacré à l'application industrielle des résultats obtenus. Nous appliquons un algorithme adaptatif à des problèmes poro-mécaniques en trois dimensions avec des lois de comportement mécanique élasto-plastiques. / In this Ph.D. thesis we develop equilibrated flux a posteriori error estimates for poro-mechanical and poro-plasticity problems.Based on these estimations we propose adaptive algorithms for the numerical solution of problems in soil mechanics.The first chapter deals with linear poro-elasticity problems.Using equilibrated $H({rm div})$-conforming flux reconstructions of the Darcy velocity and the mechanical stress tensor, we obtain a guaranteed upper bound on the error.We apply this estimate in an adaptive algorithm balancing the space and time discretisation error components in simulations in two space dimensions.The main contribution of this chapter is the symmetric reconstruction of the stress tensor.In the second chapter we propose another reconstruction technique for the stress tensor, while considering nonlinear elasticity problems.By imposing the symmetry of the tensor only weakly, we reduce computation time and simplify the implementation.We prove that the estimate obtained using this stress reconstuction is locally and globally efficient for a wide range of hyperelasticity problems.We add a linearization error estimator, enabling us to introduce adaptive stopping criteria for the linearization solver.The third chapter adresses the industrial application of the obtained results.We apply an adaptive algorithm to three-dimensional poro-mechanical problems involving elasto-plastic mechanical behavior laws.