Initial forms of analytical models created to simulate real engineering structures may generally yield dynamic response predictions different than those obtained from experimental tests. Since testing a real structure under every possible excitation is not practical, it is essential to transform the initial mathematical model to a model which reflects the characteristics of the actual structure in a better way. By using structural model updating techniques, the initial mathematical model is adjusted so that it simulates the experimental measurements more closely. In this study, a sensitivity-based finite element (FE) model updating method using experimental frequency response (FRF) data is presented. This study bases on a technique developed in an earlier study on the computation of the so-called Mis-correlation Index (MCI) used for identifying the system matrices which require updating. MCI values are calculated for each required coordinate, and non-zero numerical values indicate coordinates carrying error. In this work a new model updating procedure based on the minimization of this index is developed. The method uses sensitivity approach. FE models are iteratively updated by minimizing MCI values using sensitivities. The validation of the method is realized through some case studies. In order to demonstrate the application of the method for real systems, a real test data obtained from the modal test of a scaled aircraft model (GARTEUR SM-AG19) is used. In the application, the FE model of the scaled aircraft is updated. In the case studies the generic software developed in this study is used along with some commercial programs.

Un vérificateur de modèle peut produire une trace de contreexemple, pour un programme erroné, qui est souvent difficile à exploiter pour localiser les erreurs dans le code source. Dans ma thèse, nous avons proposé un algorithme de localisation d'erreurs à partir de contreexemples, nommé LocFaults, combinant les approches de Bounded Model Checking (BMC) avec un problème de satisfaction de contraintes (CSP). Cet algorithme analyse les chemins du CFG (Control Flow Graph) du programme erroné pour calculer les sous-ensembles d'instructions suspectes permettant de corriger le programme. En effet, nous générons un système de contraintes pour les chemins du graphe de flot de contrôle pour lesquels au plus k instructions conditionnelles peuvent être erronées. Ensuite, nous calculons les MCSs (Minimal Correction Sets) de taille limitée sur chacun de ces chemins. La suppression de l'un de ces ensembles de contraintes donne un sous-ensemble satisfiable maximal, en d'autres termes, un sous-ensemble maximal de contraintes satisfaisant la postcondition. Pour calculer les MCSs, nous étendons l'algorithme générique proposé par Liffiton et Sakallah dans le but de traiter des programmes avec des instructions numériques plus efficacement. Cette approche a été évaluée expérimentalement sur des programmes académiques et réalistes. / A model checker can produce a trace of counter-example for erroneous program, which is often difficult to exploit to locate errors in source code. In my thesis, we proposed an error localization algorithm from counter-examples, named LocFaults, combining approaches of Bounded Model-Checking (BMC) with constraint satisfaction problem (CSP). This algorithm analyzes the paths of CFG (Control Flow Graph) of the erroneous program to calculate the subsets of suspicious instructions to correct the program. Indeed, we generate a system of constraints for paths of control flow graph for which at most k conditional statements can be wrong. Then we calculate the MCSs (Minimal Correction Sets) of limited size on each of these paths. Removal of one of these sets of constraints gives a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the postcondition. To calculate the MCSs, we extend the generic algorithm proposed by Liffiton and Sakallah in order to deal with programs with numerical instructions more efficiently. This approach has been experimentally evaluated on a set of academic and realistic programs.