Proposition d’une approche multidisciplinaire pour la maintenance prédictive des chaussées

La dégradation d’une chaussée a pour origine de multiples facteurs tels que le trafic, les conditions climatiques et ses caractéristiques structurelles. Pour maintenir la qualité des infrastructures routières et prolonger leurs durées de vie tout en réduisant les coûts de maintenance, il devient essentiel de prédire et d'anticiper ces dégradations. Dans cette optique, l’utilisation de la maintenance prédictive, basée sur des moyens de surveillance in-situ, d'analyse statistique et d'intelligence artificielle, est donc nécessaire. Cependant, sa mise en œuvre fait face à de nombreux défis tels que la gestion de grandes quantités de données collectées par diverses sources mais aussi la modélisation dans un environnement incertain. Dans ce contexte, pour améliorer la surveillance des infrastructures routières, cette étude combine trois disciplines scientifiques pour démontrer la faisabilité d’un jumeau numérique d'une section de chaussée. La méthodologie proposée s’appuie sur l'optimisation des moyens d'instrumentation des routes, à l'aide de capteurs sans fil, pour alimenter des modèles mécaniques et issus des données destinés à prédire l'endommagement de la chaussée et ainsi anticiper son état de santé. Cette approche pluridisciplinaire est mise en œuvre sur un cas d'étude : une section d’autoroute instrumentée dans la région de Bordeaux en France.The pavement deterioration can be caused by multiple factors such as traffic, weather conditions and structural characteristics. To maintain the quality of roads and extend their life while reducing maintenance costs, it is essential to predict and anticipate deterioration. The use of predictive maintenance, based on in-situ monitoring, statistical analysis and artificial intelligence, is therefore necessary. Nevertheless, its implementation must deal with several challenges such as managing large amounts of data collected from different sources or modelling in an uncertain environment. In this context, to improve road infrastructure monitoring, this study combines three scientific fields to demonstrate the feasibility of a digital twin of a pavement section. The proposed methodology is based on the optimization of road instrumentation tools, using wireless sensors, to feed mechanical and data-driven models to predict pavement damage and thus anticipate its health. This multidisciplinary approach is implemented on a case study: an instrumented highway section in the Bordeaux region in France

Multitasking on Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a loose interconnection among distributed embedded devices called motes. Motes have constrained sensing, computing, and communicating resources and operate for a long period of time on a small energy supply. Although envisioned as a platform for facilitating and inspiring a new spectrum of applications, after a decade of research the WSN is limited to collecting data and sporadically updating system parameters. Programming other applications, including those that have real-time constraints, or designing WSNs operating with multiple applications require enhanced system architectures, new abstractions, and design methodologies. This dissertation introduces a system design methodology for multitasking on WSNs. It allows programmers to create an abstraction of a single, integrated system running with multiple tasks. Every task has a dedicated protocol stack. Thus, different tasks can have different computation logics and operate with different communication protocols. This facilitates the execution of heterogeneous applications on the same WSN and allows programmers to implement a variety of system services. The services that have been implemented provide energy-monitoring, tasks scheduling, and communication between the tasks. The experimental section evaluates implementations of the WSN software designed with the presented methodology. A new set of tools for testbed deployments is introduced and used to test examples of WSNs running with applications interacting with the physical world. Using remote testbeds with over 100 motes, the results show the feasibility of the proposed methodology in constructing a robust and scalable WSN system abstraction, which can improve the run-time performance of applications, such as data collection and point-to-point streaming