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The building sector currently accounts for nearly 45% of the national energy bill, and the existing buildings are slowly renewed. Energy audit is a tool for decision support to improve building performance by providing suitable renovation strategies. However, actual energy audit methods fail to completely determine the building thermal properties, and the proposed retrofit strategies may be inappropriate. We propose an innovative energy audit method which significantly reduces the uncertainties on the building thermal parameters and the thermal state. It differs from conventional methods by its operating protocol and its numerical resolution. In addition to the collection of use scenarios, thermal characteristics of built and systems, and weather conditions, it exploits the information embedded in the measurement of the thermal state, composed of the temperatures of the rooms, and the surface temperature of walls and heating devices. This data feed an inverse problem of parameters identification. Using a model for EDP gives us a direct access to the tools of the optimal control theory, including the adjoint state method. With this formalism, the inverse problem can be rewritten as a nonlinear optimization problem of the calibration of the thermal model. The unknown parameters consist of the most influential parameters of the building thermal model: the heat capacity and the thermal conductivity of the walls, the heaters characteristics, the windows transmittance and the mean rate of air renewal. We propose two alternatives to the method. The first requires the data of each individual power provided to heating device, and the second uses a global measure of the heat production. We apply them on a numerical case study: the thermal model is used for both the measures generation and the parameters identification. Once the thermal state measurements are synthesized, the selected parameters are disturbed by conventional uncertainties of the building. We investigate the method on a randomly generated sample, which gives us as many starting values for the algorithm. This allows to compute the uncertainty of the method. Reducing the operating protocol for the second alternative results in a decrease of the identification accuracy. We analyze the robustness of the method when the weather conditions of the audit change, when the sensor noise of the thermal state increases, and when we deal with strong initial parameter uncertainties. We show that the method is stable compared to the sensor noise and the thermal response of the rooms is generally well reconstructed. In order to improve the ventilation consideration, we then propose a technique to distinguish two thermal sources whose dynamics differ: one piecewise constant, and one smooth in time. The method is to solve an inverse problem, taking into account the temporal regularity of functions by a projection step. This tool has been validated on an experimental study, and the results demonstrate robustness to measurement noise. This result is a milestone in the will to overcome the constraint of precisely describing to use scenarios in the audit methodology. Finally, we propose an inverse formalism to characterize the thermal properties of a 3D structure from temperature measurements on a part of its boundary. The tool allows the reconstruction of various types and forms of internal defects. He could find opportunities in the thermal building diagnostics for characterizing insulation defects or thermal bridges, Le secteur du bâtiment représente aujourd'hui près de 45% de la facture énergétique nationale, et le parc des bâtiments existants se renouvelle lentement. L'audit énergétique est un outil d'aide à la décision permettant d'améliorer la performance des bâtiments en proposant des scénarios de rénovation adaptés. Cependant, les méthodes d'audit actuelles ne parviennent pas à saisir réellement les propriétés thermiques des bâtiments et les propositions de rénovation peuvent être inadaptées. Nous proposons une méthode innovante d'audit énergétique des bâtiments qui permet de réduire fortement les incertitudes sur les paramètres et la réponse thermique du modèle. Elle diffère des méthodes classiques par son protocole opératoire et ses outils de résolution numérique. En plus du recueil des scénarios d'usage, des caractéristiques thermiques des systèmes et du bâti, et des conditions climatiques, elle exploite l'information contenue dans la mesure de l'état thermique, composé des températures des pièces et des températures de surfaces de certaines parois et des émetteurs. Ces données alimentent un problème inverse d'identification de paramètres. L'utilisation d'un modèle aux EDP nous donne accès aux outils du contrôle optimal, et notamment à la méthode de l'état adjoint. Avec ce formalisme, le problème inverse se réécrit comme un problème d'optimisation non linéaire de calibration du modèle thermique. Les paramètres inconnus sont constitués des paramètres les plus influents : la capacité et la conductivité thermiques des parois, les caractéristiques des émetteurs, la transmittance des fenêtres et le débit de renouvellement d'air moyen. Nous proposons deux variantes de la méthode, que nous appliquons sur un cas d'étude numérique. La première nécessite les données de chaque puissance individuelle fournie aux émetteurs, et la seconde utilise une mesure globale de production de chaleur. L'approche est étudiée numériquement : le modèle thermique sert à la fois pour la génération des mesures et pour l'identification. Une fois les mesures de l'état thermique synthétisées, les paramètres retenus sont perturbés suivant les incertitudes classiques du bâtiment. Nous éprouvons la méthode sur un échantillon de tirages aléatoires, engendrant autant d'initialisations pour l'algorithme, afin de calculer l'incertitude de la méthode. L'allègement du protocole opératoire pour la seconde variante se traduit toutefois par une baisse de la précision de l'identification. Nous analysons la robustesse de la méthode lorsque les conditions climatiques de l'audit changent, que le bruit des capteurs de l'état thermique augmente, et pour de fortes incertitudes initiales des paramètres. Nous montrons que la méthode est stable par rapport au bruit des capteurs et que l'incertitude sur l'état thermique du bâtiment est globalement réduite. Afin d'améliorer la prise en compte de l'aéraulique, nous proposons une technique permettant de discriminer deux sources ayant des dynamiques différentes : l'une constante par morceaux, l'autre régulière dans le temps. La méthode résout un problème inverse prenant en compte la régularité des fonctions par projection. Cet outil est validé sur une étude expérimentale, dont les résultats démontrent la robustesse au bruit de mesure. Ce résultat constitue un jalon dans la volonté de s'affranchir de la contrainte de maîtrise de scénarios d'usage de la méthode d'audit. Enfin, nous proposons un formalisme inverse pour caractériser les propriétés thermiques d'une structure 3D à partir de mesures de température sur une partie de sa frontière. L'outil permet de reconstruire différentes formes et natures de défauts. Il pourrait trouver des débouchés dans la caractérisation de défauts d'isolation ou de ponts thermiques

This thesis focuses on research and development of inverse identification methods of material parameters. A particular attention is attributed to the viscoelastic response of collagen-reinforced soft tissues (artery, intervertebral disc, skin, tendon, ligament, etc) submitted to large displacements and large deformations (hyperelasticity). Highly non-linear and anisotropic, biomechanical constitutive laws account for a large number of material parameters. The inverse problem that allows their identification is of high non-linearity and of large dimension. By reason of numerical difficulties related to its resolution with gradient-based methods, we developed two new identification methods labelled GAO (Genetic algorithms & Analytical Optimization) and MMIM (Maximum-Minimum Identification Method).GAO advantageously combines deterministic methods of gradient type with genetic algorithms. Its originality consists in introducing analytical computations for the deterministic part leading to a gain in the speed up and in the convergence of genetic algorithms. This strategy is used in the context of anisotropic hyperelasticity.Regarding MMIM method, it operates according to an identification criterion that is expressed with the infinite norm and uses genetic algorithms. MMIM method identifies parameters of quasi-linear viscoelastic laws. It guarantees a constant viscous response that characterises the insensitivity of soft tissues to strain rate. GAO and MMIM methods successfully identified parameters of arterial wall and intervertebral disc tissues. The properties of these tissues are described in a more general context that exhibits the anatomy, histology and deformation mechanism at different hierarchical levels (nano-scale to milli-scale) of collagen-reinforced soft tissues. This gives understanding of the role of forces in relating structure to function in biology., Les travaux de cette thèse s'inscrivent dans le cadre du développement de méthodes d'identification inverse de paramètres matériau. On porte un intérêt particulier à la biomécanique des tissus souples renforcés par des fibres de collagène (artère, disque intervertébral, peau, tendon, ligament, etc.), dans le cadre de leurs réponses viscoélastiques et en grandes déformations et en grands déplacements (hyperélasticité). Fortement non-linéaires et anisotropes, les lois constitutives en biomécanique contiennent un nombre important de paramètres matériau. Le problème inverse qui permet de les identifier est de grande dimension et fortement non linéaire. En raison de difficultés numériques liées à sa résolution avec des méthodes à base de gradient, nous avons développé deux nouvelles méthodes d’identification inverse de paramètres nommées GAO (Genetic algorithms & Analytical Optimization) et MMIM (Maximum-Minimum Identification Method).La méthode GAO combine de manière avantageuse les méthodes déterministes de type gradient avec les algorithmes génétiques. Son originalité consiste à introduire des calculs analytiques pour la partie déterministe, ce qui permet d’accélérer et d’améliorer la convergence des algorithmes génétiques. Cette stratégie est appliquée dans le cadre de l’hyperélasticité anisotrope.En ce qui concerne la méthode MMIM, elle opère selon un critère d’identification basé sur la norme infinie et elle utilise les algorithmes génétiques. Elle permet d’identifier les paramètres de lois viscoélastiques quasi-linéaires. Elle garantit une réponse visqueuse constante qui est caractéristique des tissus souples qui sont insensibles à la vitesse de chargement.Les méthodes GAO et MMIM ont identifié avec succès des paramètres de tissus artériels et de tissus du disque intervertébral. Les propriétés de ces tissus sont décrits par ailleurs dans le mémoire dans un contexte plus général où on expose l'anatomie, l'histologie et le mécanisme de déformation aux différents niveaux hiérarchiques (nano-échelle à milli-échelle) d’un tissu souple renforcé par des fibres de collagène. Ceci permet de comprendre le rôle des efforts dans la relation liant la structure à la fonction en biologie.

This thesis focuses on research and development of inverse identification methods of material parameters. A particular attention is attributed to the viscoelastic response of collagen-reinforced soft tissues (artery, intervertebral disc, skin, tendon, ligament, etc) submitted to large displacements and large deformations (hyperelasticity). Highly non-linear and anisotropic, biomechanical constitutive laws account for a large number of material parameters. The inverse problem that allows their identification is of high non-linearity and of large dimension. By reason of numerical difficulties related to its resolution with gradient-based methods, we developed two new identification methods labelled GAO (Genetic algorithms & Analytical Optimization) and MMIM (Maximum-Minimum Identification Method).GAO advantageously combines deterministic methods of gradient type with genetic algorithms. Its originality consists in introducing analytical computations for the deterministic part leading to a gain in the speed up and in the convergence of genetic algorithms. This strategy is used in the context of anisotropic hyperelasticity.Regarding MMIM method, it operates according to an identification criterion that is expressed with the infinite norm and uses genetic algorithms. MMIM method identifies parameters of quasi-linear viscoelastic laws. It guarantees a constant viscous response that characterises the insensitivity of soft tissues to strain rate. GAO and MMIM methods successfully identified parameters of arterial wall and intervertebral disc tissues. The properties of these tissues are described in a more general context that exhibits the anatomy, histology and deformation mechanism at different hierarchical levels (nano-scale to milli-scale) of collagen-reinforced soft tissues. This gives understanding of the role of forces in relating structure to function in biology., Les travaux de cette thèse s'inscrivent dans le cadre du développement de méthodes d'identification inverse de paramètres matériau. On porte un intérêt particulier à la biomécanique des tissus souples renforcés par des fibres de collagène (artère, disque intervertébral, peau, tendon, ligament, etc.), dans le cadre de leurs réponses viscoélastiques et en grandes déformations et en grands déplacements (hyperélasticité). Fortement non-linéaires et anisotropes, les lois constitutives en biomécanique contiennent un nombre important de paramètres matériau. Le problème inverse qui permet de les identifier est de grande dimension et fortement non linéaire. En raison de difficultés numériques liées à sa résolution avec des méthodes à base de gradient, nous avons développé deux nouvelles méthodes d’identification inverse de paramètres nommées GAO (Genetic algorithms & Analytical Optimization) et MMIM (Maximum-Minimum Identification Method).La méthode GAO combine de manière avantageuse les méthodes déterministes de type gradient avec les algorithmes génétiques. Son originalité consiste à introduire des calculs analytiques pour la partie déterministe, ce qui permet d’accélérer et d’améliorer la convergence des algorithmes génétiques. Cette stratégie est appliquée dans le cadre de l’hyperélasticité anisotrope.En ce qui concerne la méthode MMIM, elle opère selon un critère d’identification basé sur la norme infinie et elle utilise les algorithmes génétiques. Elle permet d’identifier les paramètres de lois viscoélastiques quasi-linéaires. Elle garantit une réponse visqueuse constante qui est caractéristique des tissus souples qui sont insensibles à la vitesse de chargement.Les méthodes GAO et MMIM ont identifié avec succès des paramètres de tissus artériels et de tissus du disque intervertébral. Les propriétés de ces tissus sont décrits par ailleurs dans le mémoire dans un contexte plus général où on expose l'anatomie, l'histologie et le mécanisme de déformation aux différents niveaux hiérarchiques (nano-échelle à milli-échelle) d’un tissu souple renforcé par des fibres de collagène. Ceci permet de comprendre le rôle des efforts dans la relation liant la structure à la fonction en biologie.

Most standard geotechnical in situ tests do not allow the direct identification of the constitutive parameters of the soil layers. The use of the finite element method for the designing geotechnical structures is consequently strongly limited by the rough knowledge of the mechanical properties of soil. This fact leads to set the problematics of inverse analysis in geotechnical engineering: What information concerning the constitutive parameters of the soil is it possible to get from in situ measurement ?This study is dedicated to the identification of parameters of soil constitutive models by inverse analysis from in situ geotechnical measurements. The inverse analysis is based on a genetic algorithm optimization process. Furthermore, to describe the set of solutions identified from a genetic algorithm optimization, a statistical analysis based on Principle Component Analysis is introduced. This analysis provides a set of solutions of the inverse problem. If all solutions are not directly identify by genetic algorithm, a principle component analysis on these solutions permits to define an envelop in which all the parameter values satisfy the inverse problem. These developments are based on some synthetic excavation problems and pressuremeter tests., La plupart des essais géotechniques in situ ne permettent pas d'identifier directement les paramètres constitutifs des couches de sol. L'utilisation de calculs par éléments finis pour dimensionner les ouvrages est ainsi limitée par une mauvaise connaissance des propriétés mécaniques des sols. C'est dans ce contexte que se pose la problématique d'analyse inverse en géotechnique : quelles informations concernant les paramètres constitutifs du sol est-il possible d'obtenir à partir de mesures in situ ? L'objectif de ce travail est de développer une méthode d'analyse inverse permettant d'identifier l'ensemble des paramètres constitutifs d'un sol à partir de mesures géotechniques in situ. La procédure est basée sur un processus d'optimisation par algorithme génétique. L'ensemble des solutions identifiées sont ensuite décrites mathématiquement par une étude statistique de type analyse en composantes principales. Cette étude montre que ce type d'optimisation par algorithme génétique permet d'estimer différentes solutions approchées pour les problèmes inverses de géotechnique. Si toutes les solutions d'un problème ne sont pas identifiées directement par l'algorithme génétique, leur exploitation par une analyse en composantes principales permet d'estimer l'ensemble des solutions du problème inverse. Cette méthode est développée sur des exemples d'ouvrages de soutènement et d'essais pressiométriques.

Most standard geotechnical in situ tests do not allow the direct identification of the constitutive parameters of the soil layers. The use of the finite element method for the designing geotechnical structures is consequently strongly limited by the rough knowledge of the mechanical properties of soil. This fact leads to set the problematics of inverse analysis in geotechnical engineering: What information concerning the constitutive parameters of the soil is it possible to get from in situ measurement ?This study is dedicated to the identification of parameters of soil constitutive models by inverse analysis from in situ geotechnical measurements. The inverse analysis is based on a genetic algorithm optimization process. Furthermore, to describe the set of solutions identified from a genetic algorithm optimization, a statistical analysis based on Principle Component Analysis is introduced. This analysis provides a set of solutions of the inverse problem. If all solutions are not directly identify by genetic algorithm, a principle component analysis on these solutions permits to define an envelop in which all the parameter values satisfy the inverse problem. These developments are based on some synthetic excavation problems and pressuremeter tests.; La plupart des essais géotechniques in situ ne permettent pas d'identifier directement les paramètres constitutifs des couches de sol. L'utilisation de calculs par éléments finis pour dimensionner les ouvrages est ainsi limitée par une mauvaise connaissance des propriétés mécaniques des sols. C'est dans ce contexte que se pose la problématique d'analyse inverse en géotechnique : quelles informations concernant les paramètres constitutifs du sol est-il possible d'obtenir à partir de mesures in situ ? L'objectif de ce travail est de développer une méthode d'analyse inverse permettant d'identifier l'ensemble des paramètres constitutifs d'un sol à partir de mesures géotechniques in situ. La procédure est basée sur un processus d'optimisation par algorithme génétique. L'ensemble des solutions identifiées sont ensuite décrites mathématiquement par une étude statistique de type analyse en composantes principales. Cette étude montre que ce type d'optimisation par algorithme génétique permet d'estimer différentes solutions approchées pour les problèmes inverses de géotechnique. Si toutes les solutions d'un problème ne sont pas identifiées directement par l'algorithme génétique, leur exploitation par une analyse en composantes principales permet d'estimer l'ensemble des solutions du problème inverse. Cette méthode est développée sur des exemples d'ouvrages de soutènement et d'essais pressiométriques.

Rotor-bearing systems take an important place in engineering applications and are used in many industrial systems: gas turbines, compressors, jet engines, machine-tool spindles, etc. The spindle is an essential component of manufacturing systems involving metal removal processes. Its performance largely influences the quality of machined parts. This importance has increased with the advent of High Speed Machining, High Productivity Machining and Hard Machining with low lubrication. In machining, manufacturers aim at higher cutting speeds and feed rates in order to increase productivity. These trends imply higher rotational speeds for spindles but also higher cutting forces. At the same time, manufacturers wish to improve surface roughness and tolerances of the machined parts. To meet those goals, it is essential to master the dynamic behavior of spindles. Stiffness is a fundamental parameter controlling the static and dynamic performances of the spindle. Modeling and controlling the dynamic behavior of spindles is a complex problem, because of the non-linear nature of the bearing stiffness, of its speed dependence and because of thermo mechanical effects associated with the heat dissipation in the bearings. In this dissertation, a mixed experimental-numerical method is presented for evaluating bearing stiffness of very high speed rotor-bearing systems. This method focuses on determining the stiffness properties of angular contact ball bearings used in the design of high-speed spindles for machine-tool applications. The goal of this method is to provide accurate and reliable stiffness data to improve dynamic predictive models of spindles. These models will then serve to improve and facilitate the design of high-speed spindles. A special attention is paid to the speed dependence of the bearing stiffness. The mixed identification method is based on the comparison of an experimental modal model with a numerical modal model of spindles. An optimization procedure based on a non-linear least square fit algorithm is used to estimate the bearing stiffness. The optimization criterion combines error functions based on natural frequencies and mode shapes. Based on the measurement of frequency response functions, the experimental modal parameters (natural frequencies and mode shapes) are extracted. In order to match numerical modal parameters with the experimental ones, the iterative optimization procedure updates the numerical parametric model. The model parameters are the bearing stiffness parameters to estimate. The procedure terminates once the error between the experimental and numerical parameter falls below a predefined threshold value. At this point, the bearing stiffness estimation is completed. The numerical model was developed to be easily implemented in a commercial finite element software. Moreover, the 3D finite element model allows to take into account all the surrounding structural elements which can influence the dynamic behavior of the spindle. The ball bearing is modeled as a stiffness matrix including radial, axial, tilting and coupling terms. On the other hand, a test rig using contact-free capacitive sensors to measure frequency response functions of motor-spindles was developed. Measurements can be performed either under non-rotating conditions or under rotation. Experimental modal parameters are extracted using a traditional curve fitting method. Several numerical applications were used to assess the performances of the identification method and validate it. As an example, the proposed identification procedure is applied to a mid-size (7kW) motor-spindle running at up to 70'000 rpm. Two test cases are presented: test spindle equipped with angular contact ball bearings with a contact angle of 15° and then 25°, commonly used in industrial high-speed spindles. For these two configurations, the goal is to estimate bearing stiffness and validate the method with experimental data. Several identifications were conducted at various rotational speeds. The rotational speed range was chosen to highlight the decrease in the bearing stiffness with rotational speed. In both test cases, we observed significant decrease in axial bearing stiffness with speed. With a contact angle of 15°, at 66'000 rpm, the axial stiffness can drop to 40% of its value at zero speed. With a contact angle of 25°, at 54'000 rpm, the axial stiffness can drop to 30% of its value at zero speed. The behavior of the radial stiffness is different in the two test cases. With a contact angle of 15°, the radial stiffness remains almost constant over the whole range of rotational speed. Conversely, with a contact angle of 25°, the behavior of the radial stiffness is similar to the behavior of the axial stiffness. At 54'000 rpm, its value can decrease to less than 40% of its value at zero speed. The obtained results are in good agreement with the literature and with numerically predicted stiffness values.

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques reduce soil disturbances to minimum, allowing investigating the response of virgin soil. Self-boring pressuremeter tests (SBPT) and standard piezocone tests (CPTU) are widely used to deduce properties of clayey soils through analytical and empirical correlations between soil properties and experimental measurements. Empirical correlations usually require some tuning based on reference laboratory data because first-order estimates for typical correlation coefficients may give unreliable evaluation of soil properties. Analytical correlations are mostly based on cavity expansion methods which are restricted to either fully drained or perfectly undrained problems, so that inverse closed-form solutions for relatively simple constitutive models can be derived. In practice, however, depending on physical and consolidation properties of the soil, partially drained conditions may occur during field testing, leading to an erroneous estimation of clay characteristics. Therefore, elaborating a generic parameter identification framework, which is based on the artificial neural network (NN) technique and which may improve the reliability of soil properties derived from in situ testing, is the main goal of this research. This study explores the possibility of using NNs to solve complex inverse problems including partially drained conditions. In other words, NNs are used to map experimental measurements onto set of soil properties. The development of NN-based inverse models is based on a training data sets which consists of pseudo-experimental measurements derived from numerical simulations of both the SBPT and the CPTU test in normally- and lightly overconsolidated clay type material. The study presents a generic two-level procedure designed for the calibration of constitutive models of soils. It is demonstrated that NN inverse models can be easily integrated into the classical back-analysis. At the first level, the NN approach is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient-based optimization (GBO) technique, considered here as a corrector that improves predicted parameters. Trained NNs as parallel operating systems can provide output variables instantly and without a costly GBO iterative scheme. The proposed framework is verified for the elasto-plastic Modified Cam Clay (MCC) model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the consolidated isotropic drained compression test. The study presents formulations of the input data for the NN predictors, enhanced by a dimensional reduction of experimental data using principal component analysis (PCA). The determination of model characteristics is demonstrated, first on numerical pseudo-experiments and then on the experimental data. Furthermore, the efficiency of the proposed approach in terms of accuracy and computational effort is also discussed. The verified two-level strategy is applied to a numerical procedure of parameter identification for the boundary value problem (BVP) of the SBPT. The coupled hydro-mechanical finite element (FE) formulation allows the generated excess pore water pressure to be dissipated during simulations of the expansion test, followed then by a holding test. Numerical simulations demonstrate that volume changes that may occur in clay during the expansion test due to partial drainage, can cause local soil hardening near the cavity wall and affect parameter interpretation for pressuremeter tests. Therefore, the NN technique is applied to obtain an initial guess for model parameters, taking into account the possible partially drained conditions during the expansion test. Parameter identification based on measurements obtained through the pressuremeter expansion test and two types of holding tests is illustrated on the MCC model. NNs are trained using a set of synthetic test samples, which are generated by means of FE simulations based on constrained random permutations of input model parameters. The measurements obtained through expansion and consolidation tests are normalized by the proposed normalizing formulas so that NN predictors operate independently of the testing depth. Examples of parameter determination are demonstrated on both numerical data and field measurements from the Fucino clay deposit. The efficiency of the combined parameter identification in terms of accuracy, effectiveness and computational effort is also discussed. Finally, an application of NN predictors as a stand-alone support for soil profiling is presented for the piezocone test. By similarity to the SBPT problem, a number of NN inverse models are developed based on the results derived from rigorous FE analyzes. The FE model of piezocone penetration involves numerical formulation for the two-phase material obeying the MCC law and including the large strain theory, as well as the large deformation formulation for contact interface. It is demonstrated that a considerable computational effort related to the generation of the training database can be reduced by optimizing the mesh size and "steady-state" depth in function of soil rigidity index. Due to a severe loss of measurement accuracy observed in the finite elements adhered to the "rough" interface, an equivalent semi-numerical approach is proposed to account for frictional effects in different drainage conditions which are delineated from a number of numerical simulations. The validity of the developed penetration model is verified in detail by means of comparisons with other theoretical solutions and parametric studies synthesized from literature, as well as experimental evidence for both undrained and partially drained scenarios. An extended parametric study including influence analyzes of strength and stress anisotropy, rigidity index and cone roughness on two cone factors provides new insight into the analysis of cone penetration. The shortcomings of the FE model due to the limitations of the applied constitutive model are also discussed. As regards NN models, different configurations of input variables, including standard normalized piezocone metrics and other available soil characteristics are investigated in terms of feasibility of effective NN training. The post-training regression analyzes are performed for numerical data allowing the assessment of the influence of specific input variables on accuracy of parameters predictions. Finally, the developed NN models are applied to predict parameters based on field measurements for a number of characterization sites. Provided examples demonstrate that NN inverse models may constitute an effective complementary support during the first-order quantification of the MCC parameters from piezocone measurements.