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International audience; Evaluating natural frequencies for damage detection and localization is a standard procedure in non-destructive testing (NDT) and structural health monitoring (SHM). In particular, for cables and other prestressed tendons, natural frequencies exhibit high sensitivities to structural changes, which is often demonstrated by comparing the frequencies before and after damage occurred, e.g., using probability of detection (POD) or probability of localization (POL) curves. In this paper, an approach is developed to predict POD/POL curves without any data from the damaged state. The method is based on data from the undamaged state and a numerical model, which is utilized to obtain sensitivity vectors that linearize the relation between frequency changes and changes in structural parameters. At the same time, statistical uncertainties in the frequency estimation process are considered. Based on this method, a cable monitoring strategy is suggested, where sensitivity vectors are first used to calibrate decisive cable properties using model updating, followed by a performance assessment through POD/POL curves, and a continuous damage detection and localization based on statistical tests. The synergic effects of the presented methods are demonstrated by means of a numerical case study on a prestressed cable, where data from a single uni-axial vibration sensor is evaluated. The results indicate that changes in stiffness and axial forces can be distinguished using localization tests, but additional sensors and features are necessary for damage localization along the cable.

International audience; Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson's method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliabilitybased approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data and a model from the undamaged structure, in an unsupervised learning mode, making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.

International audience; Damage detection and damage localization constitute two pillars of Structural Health Monitoring that are highly relevant for applications to large-scale structures. Damage detection is usually achieved through statistical tests of data-driven residuals that monitor changes of a structure from its baseline behaviour. Damage localization investigates changes in damage residuals with respect to parameterized structural models through sensitivity vectors. Among the classic damage-sensitive features used for residual generation are subspace angles and principal components obtained from data spaces, whose evaluation for a decision about damage often boils down to novelty analysis, or statistical likelihood ratio tests. Modal parameter estimates are also employed for this purpose; however, most of the existing approaches appear to neglect the uncertainties related to their estimation. This paper fills this gap and presents a residual for damage detection and damage localization that is based on the difference of modal parameters obtained from data collected in some baseline and some test state of the structural system. The proposed scheme is evaluated in numerical simulations validating its robustness for damage detection and damage localization.

International audience; The first phase of each structural monitoring project is the operational evaluation. Its purpose is to define relevant damage mechanisms and environmental conditions, to consider the data acquisition limitations on site, and to justify the investment. Subsequently, relevant measurement quantities and damagesensitive features are selected, but very few systematic approaches exist in the literature on how to select the most appropriate features. The presented paper fills this gap and develops an approach to select damage-sensitive features based on probability of detection (POD) curves. The POD curves are generated based on a novel method for statistical damage detection tests that requires a finite element model and vibration data from the undamaged structure. However, no data is required from the damaged state, making it particularly suited for unique or large and complex engineering structures. The approach explicitly considers the uncertainties in the features due to unknown loads, measurement noise, and short measurement durations. Although global damage-sensitive features are considered, such as modal parameters and subspace-based residuals, the detectability is evaluated for local structural components. The paper includes a proof of concept study on a laboratory structure. The results demonstrate that the developed method successfully finds the feature with the highest damage detectability for a chosen damage scenario, and that the detectability varies depending on the monitored local component.

International audience; This article describes an approach to evaluate the minimum localizable damage for stochastic subspace-based damage diagnosis. Localizability is defined as the sensitivity to small and local damages (detectability), the ability to narrow down the exact damage location (localization resolution) and the test response of undamaged parameters (false localization alarms). For the analysis, damage is defined as a change in model-based design parameters, for example, material constants or cross-sectional values in a finite element model. Subsequently, the parameter changes are linked to changes in the global damage-sensitive features using sensitivity vectors, and inherent uncertainties (due to stochastic loads and measurement noise) are quantified. This way, local structural parameters can be tested for changes using statistical hypothesis tests, such as the general likelihood ratio and the statistical minmax localization test. Due to the numerical conditioning of the damage localization problem, the sensitivity vectors have to be clustered before damage can be localized. Sensitivity clustering corresponds to a substructuring of the finite element model, where the number of clusters (the localization resolution) is a user-defined input parameter. The main results of this paper are mathematical criteria to calculate the damage detectability and the false alarm susceptibility for different localization resolutions. Moreover, an automated substructuring routine is described that finds the optimal substructure arrangement as a compromise between high damage detectability, high localization resolution, and low false alarm susceptibility. For proof of concept, a numerical case study is presented, where the damage localizability is determined and validated for a cable-stayed bridge.

Structural health monitoring (SHM) can extend the operation of bridges beyond their original life span, increase the safety between scheduled inspections, and allow for a prioritized inspection after extreme events. One of the major challenges is to assess which damages can or cannot be diagnosed (i.e., detected or localized), which is essential to evaluate the value of a SHM system before it is installed, and to optimize the sensor placement accordingly.This work develops a framework to predict the minimum detectable damage, i.e., the minimum change in local structural design parameters that can reliably be detected based on changes in global damage-sensitive features. The diagnosis is considered "reliable" if the probability of false alarms is low and the probability of detection is high. Equivalently, a damage is "detectable" if it is significant under consideration of typical uncertainties related to ambient excitation and measurement noise and empirical safety thresholds. The approach requires vibration data from the undamaged structure in combination with a numerical model, and is universally applicable to a wide range of structures and damage-sensitive features. Secondly, a method is proposed to analyze the minimum localizable damage. The results show that optimal localizability is a compromise between high localization resolution, high detectability, and few false localization alarms. Thirdly, a sensor placement strategy is devised that takes as input the desired minimum diagnosable damage and optimizes the sensor layout and the number of sensors accordingly. The method allows one to focus the global damage diagnosis on local structural components. Ultimately, the monitoring of prestressing forces and support displacements is incorporated into the diagnostic framework, so that they can be analyzed and distinguished from changes in material properties or cross-sectional values.Besides the performance evaluation, the framework is suitable for quality control of existing instrumentation on real structures. Therefore, self-validation strategies are implemented to verify the input parameters, to validate the theoretical assumptions, and to check its effectiveness based on non-invasive tests using extra masses. The proof of concept studies based on a laboratory steel beam and a cable-stayed bridge show promising results regarding the practical application of the theoretical contributions.; La surveillance de l’intégrité des structures (structural health monitoring, SHM) peut prolonger l'utilisation des ponts au-delà de leur durée de vie initialement prévue, accroître la sécurité entre les inspections programmées et permettre une inspection prioritaire après des événements extrêmes. L'un des principaux défis consiste à déterminer quels endommagements peuvent ou non être diagnostiqués (c'est-à-dire détectés ou localisés), ce qui est essentiel pour évaluer la valeur d'un système de SHM avant son installation et également en conséquence pour optimiser le placement des capteurs.Cette thèse développe un cadre pour prédire les endommagements détectables, c'est-à-dire le changement minimum dans des paramètres structurels locaux qui peut être détecté de manière fiable sur la base des caractéristiques calculées à partir des données vibratoires. Le diagnostic est considéré comme "fiable" si la probabilité de fausses alertes est faible et la probabilité de détection élevée. De même, un endommagement est "détectable" si le changement dans les caractéristiques est important compte tenu des incertitudes typiques liées à l'excitation ambiante, le bruit de mesure et les seuils de sécurité empiriques. L'approche requiert des données vibratoires de la structure intacte et un modèle numérique, et est universellement applicable à un large éventail de structures et de caractéristiques. Par la suite, une méthode est proposée pour analyser les endommagements localisables. Les résultats montrent que la localisation optimale est un compromis entre une haute résolution de localisation, une grande détectabilité et peu de fausses alarmes de localisation. Avec ces résultats, une stratégie de placement de capteurs est élaborée en tenant compte de la diagnosticabilité d’endommagements souhaitée et optimise le placement et le nombre de capteurs en conséquence. La méthode permet d’axer le diagnostic global d’endommagements sur les composantes structurelles locales. Enfin, le suivi des forces de précontrainte et des déplacements d’appui est intégré dans les méthodes de diagnostic, afin que cela puisse être analysé et distingué des changements de propriétés des composantes structurelles.Outre l'évaluation des performances, les méthodes développées conviennent au contrôle de la qualité de l’instrumentation existante sur des structures réelles. Par conséquent, des stratégies d'auto-validation sont mises en œuvre pour vérifier les paramètres d'entrée, valider les hypothèses théoriques et vérifier l’efficacité sur des tests non invasifs utilisant des masses supplémentaires. Les études de preuve de concept basées sur une poutre en laboratoire et un pont à haubans montrent des résultats prometteurs en ce qui concerne l'application pratique des contributions théoriques.

The development of a methodology for accurate and reliable condition assessment of civil struc-tures has become very important. The finite element (FE) model updating method provides an efficient, non-destructive, global damage identification technique, which is based on the fact that the modal parameters (e.g. natural frequencies and mode shapes) of the structure are af-fected by structural damage. In the FE model, the damage is represented by a change of the structural parameters and can be identified by updating the FE model to the measured modal parameters. This thesis describes an iterative sensitivity-based FE model updating method in which the discrepancies between the natural frequencies of the numerical model and the real structure are minimized. Furthermore, the updating procedure is applied to the model of the University of the Federal Armed Forces (UniBw) in Munich. For this purpose, it was necessary to design the model and develop an interface between the finite element software (ANSYS) and a computing software (MATLAB), which can apply the model updating technique to the finite element model.