Les structures en béton armé exposées à des environnements agressifs subissent des dégradations qui affectent leur intégrité. La corrosion des armatures est l’un des mécanismes de dégradation les plus répandus et les coûteux en terme de maintenance et de réparation. Ce processus est dû à la pénétration des agents agressifs dans le béton, notamment les ions chlorures et le gaz carbonique. Les chlorures induisent une corrosion localisée ou par piqûre, alors que le gaz carbonique engendre une corrosion généralisée ou uniforme. Le déclenchement et la propagation de la corrosion dépendent de plusieurs facteurs liés aux matériaux, aux chargements, à la géométrie et à l’environnement. Ces facteurs présentent de grandes incertitudes qui doivent être prise en comptes à travers une approche probabiliste. Dans ce travail de recherche, nous nous intéressons au mécanisme de corrosion en général. Un intérêt particulier est porté à la prise en compte de l’impact des facteurs climatiques sur ce processus, notamment dans le contexte libanais. Ainsi, nous proposons une modélisation physique de la corrosion des aciers dans les poutres en béton armé qui se déroule en deux phases : - une phase d’initiation durant laquelle les agents agressifs (chlorures et gaz carbonique) pénètrent dans le béton et atteignent des concentrations critiques provoquant la dépassivation de l’acier ; - une phase de propagation durant laquelle il y a corrosion active des aciers et diminution de la résistance de la poutre jusqu’à la défaillance. Les facteurs présentant des incertitudes sont traités comme des variables aléatoires. Pour les modéliser, nous avons étudié, pour les différentes variables aléatoires, de nombreux modèles probabilistes proposés dans la littérature. Nous avons vérifié leur compatibilité vis-à-vis de notre problématique et la possibilité d’assurer les données nécessaires à leur bonne utilisation (notamment la cohérence entre les hypothèses). Ensuite, nous avons retenu les modèles probabilistes les plus adaptés à notre cas. Par ailleurs, l’application des principes fiabilistes nous permet d’évaluer la fiabilité des poutres sujettes à la corrosion vis-à-vis des deux états-limites (ELU et ELS). En effet, la perte de la section d’acier due à la corrosion induit d’une part, une diminution de la capacité portante de la poutre, et d’autre part une augmentation de la contrainte au niveau du béton tendu (provoquant un accroissement des ouvertures des fissures). Ainsi, pour l’état limite de service, la marge de sûreté s’annule lorsque l’ouverture des fissures dépasse la valeur limite préconisée par l’Eurocode 2. Quant à l’état limite ultime, la fonction d’état limite est la résistance en flexion : la défaillance a lieu lorsque le moment résistant équivaut au moment sollicitant. Le calcul fiabiliste est effectué au moyen de simulations de Monte-Carlo. Finalement, nous avons réalisé plusieurs applications aux modèles de corrosions proposées dans ce travail. La première application porte sur l’analyse des sensibilités des modèles de corrosion aux différents paramètres. L’effet des moyennes des paramètres aléatoires ainsi que leurs variabilités sur la réponse du modèle est examiné. Une attention particulière est accordée à l’impact des facteurs climatiques. Ainsi une application du modèle de corrosion induite par les chlorures avec des données réelles de température et d’humidité relatives à trois villes côtières ayant des caractéristiques climatiques différentes est présentée. Ensuite une étude comparative de l’effet du choix des diamètres des armatures et des épaisseurs des enrobages sur la fiabilité à l’état limite ultime et à l’état limite de service est effectuée. Les résultats obtenus ont permis de mettre en évidence l’aspect agressif des facteurs climatiques : un climat chaud et humide est très agressif vis-à-vis de la corrosion induite par les chlorures alors qu’un climat à humidité relative variable favorise la corrosion par carbonatation. (...) / When exposed to aggressive environment, reinforced concrete structures are subject to a degradation mechanism that affects their integrity. Among various environmental attacks, the corrosion of RC structures is considered the most dangerous. The process is launched by the penetration of aggressive agents, precisely the chlorides and carbon dioxide into the concrete. The chlorides induce a localized corrosion, also called pitting corrosion, while on the other hand the carbon dioxide leads to a general corrosion called uniform corrosion. This corrosion phenomenon depends on several factors such as the materials characteristics,loadings, geometry and the environment. All these components include different levels of uncertainties that are taken into account throughout a probabilistic approach. In this work, we propose two models for the corrosion mechanisms induced separately by the chlorides and the carbon dioxide. These models take into account the effect of the climatic condition that is mainly described by the temperature and the relative humidity. In addition to that, as a study case we have treated in details the Lebanese climatic context. We have proposed a physical model of steel corrosion in reinforced concrete beams that occurs in two phases : - An initiation phase where aggressive agents like the chlorides and carbon dioxide penetrate into the concrete and reach a critical concentration values causing the depassivation of the steel ; - A propagation phase in which the active corrosion of steel decreases the strength of the beam leading to its failure. All the factors that have uncertainties are treated as random variables. Several probabilistic models are listed and discussed in the literature while only the models that match with our context are selected. The reliability analysis allowed us to assess the reliability of beams subjected to corrosion in ULS and SLS. The loss of steel section due to the corrosion mechanism induces a decrease of the bearing beam capacity, and an increase in the tension stress in the concrete.This causes an increase of the width of cracks openings. Thus, taking into account the serviceability limit state, the safety margin goes to zero when the width of crack opening exceeds the acceptable width as recommended by the Eurocode 2. The limit state function in ULS is the bending strength. The failure occurs when the applied moment equals or surpasses the resisting moment. The reliability calculations are carried out using Monte-Carlo simulations. Finally, several applications to the corrosion model are proposed via this work. The first application concerns the sensitivity analysis of the corrosion models for the different parameters. The effects of the mean values and the variability of the random variables on the model response are also examined. The impact of climatic factors on the corrosion phenomenon took the biggest part of this work. We have applied the chloride’s corrosion model with the real temperatures and relative humidity of three coastal cities having different climatic characteristics. Then a comparative study showing the effect of the ba rdiameters and the cover thickness on the reliability of the RC beam subjected to aggressive environment is carried out. (...)

A maior parte dos mapas empregados em tarefas de navegação por robôs móveis representam apenas informações espaciais do ambiente. Outros tipos de informações, que poderiam ser obtidos dos sensores do robô e incorporados à representação, são desprezados. Hoje em dia é comum um robô móvel conter sensores de distância e um sistema de visão, o que permitiria a princípio usá-lo na realização de tarefas complexas e gerais de maneira autônoma, dada uma representação adequada e um meio de extrair diretamente dos sensores o conhecimento necessário. Uma representação possível nesse contexto consiste no acréscimo de informação semântica aos mapas métricos, como por exemplo a segmentação do ambiente seguida da rotulação de cada uma de suas partes. O presente trabalho propõe uma maneira de estruturar a informação espacial criando um mapa semântico do ambiente que representa, além de obstáculos, um vínculo entre estes e as imagens segmentadas correspondentes obtidas por um sistema de visão omnidirecional. A representação é implementada por uma descrição relacional do domínio, que quando instanciada gera um campo aleatório condicionado, onde são realizadas as inferências. Modelos que combinam probabilidade e lógica de primeira ordem são mais expressivos e adequados para estruturar informações espaciais em semânticas. / Most maps used in navigational tasks by mobile robots represent only environmental spatial information. Other kinds of information, that might be obtained from the sensors of the robot and incorporated in the representation, are negleted. Nowadays it is common for mobile robots to have distance sensors and a vision system, which could in principle be used to accomplish complex and general tasks in an autonomously manner, given an adequate representation and a way to extract directly from the sensors the necessary knowledge. A possible representation in this context consists of the addition of semantic information to metric maps, as for example the environment segmentation followed by an attribution of labels to them. This work proposes a way to structure the spatial information in order to create a semantic map representing, beyond obstacles, an anchoring between them and the correspondent segmented images obtained by an omnidirectional vision system. The representation is implemented by a domains relational description that, when instantiated, produces a conditional random field, which supports the inferences. Models that combine probability and firstorder logic are more expressive and adequate to structure spatial in semantic information.

The ability to recognize handshapes in signing video is essential in algorithms for sign recognition and retrieval. Handshape recognition from isolated images is, however, an insufficiently constrained problem. Many handshapes share similar 3D configurations and are indistinguishable for some hand orientations in 2D image projections. Additionally, significant differences in handshape appearance are induced by the articulated structure of the hand and variants produced by different signers. Linguistic rules involved in the production of signs impose strong constraints on the articulations of the hands, yet, little attention has been paid towards exploiting these constraints in previous works on sign recognition. Among the different classes of signs in any signed language, lexical signs constitute the prevalent class. Morphemes (or, meaningful units) for signs in this class involve a combination of particular handshapes, palm orientations, locations for articulation, and movement type. These are thus analyzed by many sign linguists as analogues of phonemes in spoken languages. Phonological constraints govern the ways in which phonemes combine in American Sign Language (ASL), as in other signed and spoken languages; utilizing these constraints for handshape recognition in ASL is the focus of the proposed thesis. Handshapes in monomorphemic lexical signs are specified at the start and end of the sign. The handshape transition within a sign are constrained to involve either closing or opening of the hand (i.e., constrained to exclusively use either folding or unfolding of the palm and one or more fingers). Furthermore, akin to allophonic variations in spoken languages, both inter- and intra- signer variations in the production of specific handshapes are observed. We propose a Bayesian network formulation to exploit handshape co-occurrence constraints also utilizing information about allophonic variations to aid in handshape recognition. We propose a fast non-rigid image alignment method to gain improved robustness to handshape appearance variations during computation of observation likelihoods in the Bayesian network. We evaluate our handshape recognition approach on a large dataset of monomorphemic lexical signs. We demonstrate that leveraging linguistic constraints on handshapes results in improved handshape recognition accuracy. As part of the overall project, we are collecting and preparing for dissemination a large corpus (three thousand signs from three native signers) of ASL video annotated with linguistic information such as glosses, morphological properties and variations, and start/end handshapes associated with each ASL sign.

Nous proposons une approche probabiliste afin de déterminer l’impact des changements dans les programmes à objets. Cette approche sert à prédire, pour un changement donné dans une classe du système, l’ensemble des autres classes potentiellement affectées par ce changement. Cette prédiction est donnée sous la forme d’une probabilité qui dépend d’une part, des interactions entre les classes exprimées en termes de nombre d’invocations et d’autre part, des relations extraites à partir du code source. Ces relations sont extraites automatiquement par rétro-ingénierie. Pour la mise en oeuvre de notre approche, nous proposons une approche basée sur les réseaux bayésiens. Après une phase d’apprentissage, ces réseaux prédisent l’ensemble des classes affectées par un changement. L’approche probabiliste proposée est évaluée avec deux scénarios distincts mettant en oeuvre plusieurs types de changements effectués sur différents systèmes. Pour les systèmes qui possèdent des données historiques, l’apprentissage a été réalisé à partir des anciennes versions. Pour les systèmes dont on ne possède pas assez de données relatives aux changements de ses versions antécédentes, l’apprentissage a été réalisé à l’aide des données extraites d’autres systèmes. / We study the possibility of predicting the impact of changes in object-oriented code using bayesian networks. For each change type, we produce a bayesian network that determines the probability that a class is impacted given that another class is changed. Each network takes as input a set of possible relationships between classes. We train our networks using historical data. The proposed impact-prediction approach is evaluated with two different scenarios, various types of changes, and five systems. In the first scenario, we use as training data, the changes performed in the previous versions of the same system. In the second scenario training data is borrowed from systems that are different from the changed one. Our evaluation showed that, in both cases, we obtain very good predictions, even though they are better in the first scenario.

O desafio de encontrar corretamente genes eucarioticos codificadores de proteinas nas sequencias genomicas e um problema em aberto. Neste trabalho, implementamos uma plata- forma, com o objetivo de melhorar a forma com que preditores de genes sao implementados e avaliados. Tres novas ferramentas foram implementadas: ToPS (Toolkit of Probabilistic Models of Sequences) foi o primeiro arcabouco orientado a objetos que fornece ferramentas para implementacao, manipulacao, e combinacao de modelos probabilisticos para representar sequencias de simbolos; MYOP (Make Your Own Predictor) e um sistema que tem como objetivo facilitar a construcao de preditores de genes; e SGEval utiliza grafos de splicing para comparar diferente anotacoes com eventos de splicing alternativos. Utilizamos nossas ferramentas para o desenvolvimentos de preditores de genes em onze genomas distintos: A. thaliana, C. elegans, Z. mays, P. falciparum, D. melanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. Com esse desenvolvimento, estabelecemos um protocolo para implementacao de novos preditores. Alem disso, utilizando a nossa plata- forma, desenvolvemos um fluxo de trabalho para predicao de genes no projeto do genoma da cana de acucar, que ja foi utilizado em 109 sequencias de BAC geradas pelo BIOEN (FAPESP Bioenergy Program). / The challenge of correctly identify eukaryotic protein-coding genes in the genomic se- quences is an open problem. In this work, we implemented a plataform with the aim of improving the way that gene predictors are implemented and evaluated. ToPS (Toolkit of Probabilistic Models of Sequence) was the first object-oriented framework that provides tools for implementation, manipulation, and combination of probabilistic models that represent sequences of symbols. MYOP (Make Your Own Predictor) facilitates the construction of gene predictors. SGEval (Splicing Graph Evaluation) uses splicing graphs to compare dif- ferent annotations with alternative splicing events. We used our plataform to develop gene finders in eleven distinct genomes: A. thaliana, C. elegans, Z. mays, P. falciparum, D. me- lanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. With this development, we established a protocol for implementing new gene predictors. In addi- tion, using our platform, we developed a pipeline to find genes in the 109 sugarcane BAC sequences produced by BIOEN (FAPESP Bioenergy Program).

Tephra deposits are one of the possible outcomes of explosive volcanic eruptions and are the result of vertical settling of volcanic particles that have been expelled from the volcanic vent into the atmosphere, following magma fragmentation within the volcanic conduit. Tephra fallout represents the main volcanic hazard to populated areas and critical facilities. Therefore, it is crucial to better understand processes that lead to tephra transport, sedimentation and hazards. In this study, and based on detailed mapping and sampling of the tephra deposit of the 2450 BP Plinian eruption of Pululagua volcano (Ecuador), I investigate tephra deposits through a variety of approaches, including empirical and analytical modeling of tephra thickness and grain size data to infer important eruption source parameters (e.g. column height, total mass ejected, total grain size distribution of the deposit). I also use a statistical approach (smoothed bootstrap with replacement method) to assess the uncertainty in the eruptive parameters. The 2450 BP Pululagua volcanic plume dynamics were also explored through detailed grain size analysis and 1D modeling of tephra accumulation. Finally, I investigate the influence of particle shape on tephra accumulation on the ground through a quantitative and comprehensive study of the shape of volcanic ash. As the global need for energy is expected to grow in the future, many future natural hazard studies will likely involve the assessment of volcanic hazards at critical facilities, including nuclear power plants. I address the potential hazards from tephra fallout, pyroclastic flows and lahars for the Bataan Nuclear Power Plant (Philippines) posed by three nearby volcanoes capable of impacting the site during an explosive eruption. I stress the need for good constraints (stratigraphic analysis and events dating) on past eruptive events to better quantify the probability of future events at potentially active volcanoes, the need for probabilistic approaches in such volcanic hazard assessments to address a broad range of potential eruption scenarios, and the importance of considering coupled volcanic processes (e.g. tephra fallout leading to lahars) in volcanic hazard assessments.

This thesis studies grammar-based approaches in the application of Estimation of Distribution Algorithms (EDA) to the tree representation widely used in Genetic Programming (GP). Although EDA is becoming one of the most active fields in Evolutionary computation (EC), the solution representation in most EDA is a Genetic Algorithms (GA) style linear representation. The more complex tree representations, resembling GP, have received only limited exploration. This is unfortunate, because tree representations provide a natural and expressive way of representing solutions for many problems. This thesis aims to help fill this gap, exploring grammar-based approaches to extending EDA to GP-style tree representations. This thesis firstly provides a comprehensive survey of current research on EDA with emphasis on EDA with GP-style tree representation. The thesis attempts to clarify the relationship between EDA with conventional linear representations and those with a GP-style tree representation, and to reveal the unique difficulties which face this research. Secondly, the thesis identifies desirable properties of probabilistic models for EDA with GP-style tree representation, and derives the PRODIGY framework as a consequence. Thirdly, following the PRODIGY framework, three methods are proposed. The first method is Program Evolution with Explicit Learning (PEEL). Its incremental general-to-specific grammar learning method balances the effectiveness and efficiency of the grammar learning. The second method is Grammar Model-based Program Evolution (GMPE). GMPE realises the PRODIGY framework by introducing elegant inference methods from the formal grammar field. GMPE provides good performance on some problems, but also provides a means to better understand some aspects of conventional GP, especially the building block hypothesis. The third method is Swift GMPE (sGMPE), which is an extension of GMPE, aiming at reducing the computational cost. Fourthly, a more accurate Minimum Message Length metric for grammar learning in PRODIGY is derived in this thesis. This metric leads to improved performance in the GMPE system, but may also be useful in grammar learning in general. It is also relevant to the learning of other probabilistic graphical models.

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach.

Nous proposons une approche probabiliste afin de déterminer l’impact des changements dans les programmes à objets. Cette approche sert à prédire, pour un changement donné dans une classe du système, l’ensemble des autres classes potentiellement affectées par ce changement. Cette prédiction est donnée sous la forme d’une probabilité qui dépend d’une part, des interactions entre les classes exprimées en termes de nombre d’invocations et d’autre part, des relations extraites à partir du code source. Ces relations sont extraites automatiquement par rétro-ingénierie. Pour la mise en oeuvre de notre approche, nous proposons une approche basée sur les réseaux bayésiens. Après une phase d’apprentissage, ces réseaux prédisent l’ensemble des classes affectées par un changement. L’approche probabiliste proposée est évaluée avec deux scénarios distincts mettant en oeuvre plusieurs types de changements effectués sur différents systèmes. Pour les systèmes qui possèdent des données historiques, l’apprentissage a été réalisé à partir des anciennes versions. Pour les systèmes dont on ne possède pas assez de données relatives aux changements de ses versions antécédentes, l’apprentissage a été réalisé à l’aide des données extraites d’autres systèmes. / We study the possibility of predicting the impact of changes in object-oriented code using bayesian networks. For each change type, we produce a bayesian network that determines the probability that a class is impacted given that another class is changed. Each network takes as input a set of possible relationships between classes. We train our networks using historical data. The proposed impact-prediction approach is evaluated with two different scenarios, various types of changes, and five systems. In the first scenario, we use as training data, the changes performed in the previous versions of the same system. In the second scenario training data is borrowed from systems that are different from the changed one. Our evaluation showed that, in both cases, we obtain very good predictions, even though they are better in the first scenario.

O desafio de encontrar corretamente genes eucarioticos codificadores de proteinas nas sequencias genomicas e um problema em aberto. Neste trabalho, implementamos uma plata- forma, com o objetivo de melhorar a forma com que preditores de genes sao implementados e avaliados. Tres novas ferramentas foram implementadas: ToPS (Toolkit of Probabilistic Models of Sequences) foi o primeiro arcabouco orientado a objetos que fornece ferramentas para implementacao, manipulacao, e combinacao de modelos probabilisticos para representar sequencias de simbolos; MYOP (Make Your Own Predictor) e um sistema que tem como objetivo facilitar a construcao de preditores de genes; e SGEval utiliza grafos de splicing para comparar diferente anotacoes com eventos de splicing alternativos. Utilizamos nossas ferramentas para o desenvolvimentos de preditores de genes em onze genomas distintos: A. thaliana, C. elegans, Z. mays, P. falciparum, D. melanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. Com esse desenvolvimento, estabelecemos um protocolo para implementacao de novos preditores. Alem disso, utilizando a nossa plata- forma, desenvolvemos um fluxo de trabalho para predicao de genes no projeto do genoma da cana de acucar, que ja foi utilizado em 109 sequencias de BAC geradas pelo BIOEN (FAPESP Bioenergy Program). / The challenge of correctly identify eukaryotic protein-coding genes in the genomic se- quences is an open problem. In this work, we implemented a plataform with the aim of improving the way that gene predictors are implemented and evaluated. ToPS (Toolkit of Probabilistic Models of Sequence) was the first object-oriented framework that provides tools for implementation, manipulation, and combination of probabilistic models that represent sequences of symbols. MYOP (Make Your Own Predictor) facilitates the construction of gene predictors. SGEval (Splicing Graph Evaluation) uses splicing graphs to compare dif- ferent annotations with alternative splicing events. We used our plataform to develop gene finders in eleven distinct genomes: A. thaliana, C. elegans, Z. mays, P. falciparum, D. me- lanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. With this development, we established a protocol for implementing new gene predictors. In addi- tion, using our platform, we developed a pipeline to find genes in the 109 sugarcane BAC sequences produced by BIOEN (FAPESP Bioenergy Program).