The Structural Health Monitoring (SHM) in civil engineering faces several challenges. The main issue lies in defining a reliable and precise methodology of damage detection and localization in order to allow preventive maintenance or to enable the definition of repair actions. In this paper, a new methodology of SHM is proposed. Using Vibration-Based Damage Detection Methods (VBDDM), a damage detection and localization algorithm is elaborated and tested on a Finite Element Model (FEM) of an existing building. In a first case, the damage is introduced artificially by a local reduction of stiffness, while in the second case, the damage is calculated according to a real seismic signal from the italian L'Aquila earthquake. The advantages and disadvantages of each dynamic monitoring technique are discussed and the usefulness of the algorithm is highlighted.

End-of-life management of complex systems is increasingly important for industry because of growing environmental concerns and associated regulations. In many areas, lack of hindsight and significant statistical information restricts the efficiency of end-of-life management processes and additional expert knowledge is required. In this context and to promote the reuse of secondhand components, a methodology supported by risk assessment tools is proposed. The proposal consists of an approach to combine expert and statistical knowledge to improve risk assessment. The theory of belief functions provides a common framework to facilitate fusion of multisource knowledge, and a directed evidential network is used to compute a measure of the risk level. An additional indicator is proposed to determine the result quality. Finally, the approach is applied to a scenario in aircraft deconstruction. In order to support the scientific contribution, a software prototype has been developed and used to illustrate the processing of directed evidential networks.

This paper presents a methodology for anticipating failures in a component up to the end of its life cycle. Often, feedback data is not sufficient and must be complemented by the analysis of expert judgment. The methodology developed aims at anticipating the degradation mechanisms responsible for aging, and evaluating their relevance and related uncertainties. This is necessary information for risk analysis related to the operating of a component up to the end of its life cycle. Lastly, the methodology is applied to a nuclear component.

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