Sylvia Keßler

1. The central task of civil engineers is to design, construct, and maintain structures so that they withstand expected actions and environmental influences throughout their intended service life, ...

Civil infrastructures are increasingly aging, and reliable condition assessment is essential to ensure their safe and continuous operation. Structural Health Monitoring (SHM) systems have been developed to support this task, with automatic damage detection being one of their primary objectives. However, effective detection is often hindered by measurement uncertainties and low sensitivity of the monitored responses to damage. Since installing SHM systems is costly, it is crucial to assess their detection capability before deployment and prior to the occurrence of damage. To address this need, several approaches have been proposed to predict SHM reliability and estimate the minimum detectable damage using prior information about the structure and the SHM system. A major limitation of existing methods is that they provide accurate predictions only when the damage detection problem is linear or mildly nonlinear. This contribution presents a solution to overcome this limitation. We introduce a method capable of predicting the minimum detectable damage even for nonlinear problems. The approach is based on a fully probabilistic formulation of the damage detection process, enabling not only the detection but also the quantification of damage. As a proof of concept, the method is applied to a nonlinear case study, and its ability to predict damage detection reliability is evaluated and compared against existing approaches.

Zusammenfassung: Die satellitengestützte Radarinterferometrie (InSAR) ermöglicht die berührungslose Erfassung von Bauwerksverformungen mit millimetergenauer Präzision und bietet ein hohes Potenzial für ein netzweites, skalierbares Brückenmonitoring. Während InSAR bislang vor allem zur Beobachtung großflächiger Bodenbewegungen eingesetzt wurde, rücken zunehmend Infrastrukturbauwerke in den Fokus. Am Beispiel einer Autobahnbrücke in Deutschland wird die Eignung frei verfügbarer Sentinel‐1‐Daten in Kombination mit einer bauwerksspezifischen Temperaturkompensation („Virtual Sensing“) untersucht. Die Ergebnisse zeigen, dass temperaturbedingte Längsausdehnungen zuverlässig erfasst und von langfristigen Verformungstrends getrennt werden können. Nach Temperaturkompensation lassen sich verbleibende Langzeittrends im Millimeterbereich identifizieren. Darüber hinaus erfolgt eine Potenzialanalyse von 560 Autobahnbrücken innerhalb eines 100‐km‐Radius. Rund 94 % dieser Bauwerke weisen eine ausreichende Anzahl an Persistent‐Scatterer‐Punkten auf und sind damit grundsätzlich für ein satellitengestütztes Monitoring geeignet. Ein automatisiertes Flagging‐System ermöglicht die netzweite Identifikation potenzieller „Hotspots“. Die Methodik zeigt, dass InSAR als skalierbares Instrument in bestehende Überwachungskonzepte integriert werden kann und flächendeckendes Infrastrukturmonitoring unterstützt.

The assessment of grouting quality is one of the most important inspection tasks for post-tensioned bridges since insufficient grouting can lead to corrosion of the tensioning strands inside the duct. Ultrasonic testing (UT) is an NDT-method, which is capable of solving this task. However, due to many factors affecting the outcome of the inspection (e.g., bonding quality, depth of the duct, concrete quality, etc.) the interpretation of measurements results is very difficult, and results can be ambiguous. In this study, we conduct large-scale 3D UT-simulations to better assess and quantify the challenges during the inspection. A heterogeneous concrete model with explicit discretization of aggregates and pores is used to simulate noise generated during measurements. The amplitude and phase of the reflection signal are identified as the primary indicators for the grouting condition. A large set of simulations is carried out to assess the dependence of both indicators with varying grouting and bonding (between duct and grout) conditions. It was found that grouting conditions exhibit a strongly nonlinear influence on the indicators, while a linear trend could be observed for bonding conditions. Also, a strong dependency on the position from which the inspection is carried out was observed.

Zusammenfassung: Bauwerksmonitoring (SHM) entwickelt sich zu einem zentralen Baustein für die Instandhaltung von Bauwerken. Mit intelligenter Sensorik lassen sich Tragreserven sichtbar machen, Schäden frühzeitig erkennen und Instandhaltungsprozesse neu denken. Gleichzeitig stellen Qualifikation und Zertifizierung des beauftragten Fachpersonals eine große Herausforderung für Vergabestellen dar. Da es keine standardisierten Ausbildungs‐ und Zertifizierungswege gibt, werden Referenzprojekte häufig als Qualifikationsnachweis herangezogen. Diese Praxis begünstigt eine Marktkonzentration auf wenige etablierte Anbieter, erschwert den Markteintritt neuer Akteure und wirkt kostentreibend – mit negativen Folgen für die flächendeckende Umsetzung von SHM. Dieses Positionspapier schlägt einen Sechs‐Punkte‐Plan zur Qualifizierung und Zertifizierung von Anwärtern ohne einschlägige Referenzprojekte vor. Ziel ist es, Transparenz, Vergleichbarkeit und Wettbewerb im Vergabeprozess zu stärken, ohne bestehende Qualitätsstandards abzusenken. Zentrale Elemente sind eine branchenübliche Arbeitsteilung, nachvollziehbare Qualifikationsnachweise über Ausbildung und Berufserfahrung, ein modulares Zertifizierungsmodell sowie die Einführung eines neuen Akteurs, des Kommunikationsdienstleisters, zur Standardisierung der Datenverarbeitung und ‐bereitstellung. Der vorgeschlagene Ansatz schafft einen breitenwirksamen Zugang zum Bauwerksmonitoring und fördert gleichzeitig Innovation und Wettbewerb bei Marktführern.

Bayesian model updating is widely used in engineering for the identification of unknown parameters of computational models based on noisy experimental measurements. In practical applications, however, the repeated evaluation of computational models may be demanding, which renders direct Bayesian inference prohibitive as it relies often on extensive sampling procedures. Surrogate models, particularly Gaussian process regression, are therefore frequently employed due to their capability to provide both predictions of the model response and a probabilistic characterization of the approximation uncertainty. Nevertheless, the uncertainty introduced by the surrogate model is not always treated consistently within the Bayesian inference procedure. This contribution proposes a framework for Bayesian model updating that explicitly propagates the predictive uncertainty induced by Gaussian process surrogates into the likelihood function via analytical marginalization. In addition, the proposed framework allows the simultaneous identification of the measurement noise characteristics. Moreover, an active learning strategy is developed in order to adaptively refine the surrogate model while controlling the uncertainty in the estimation of the model evidence. The resulting formulation provides a unified framework in which surrogate uncertainty, measurement noise, and adaptive learning are treated in a consistent probabilistic manner within Bayesian inference. The approach is illustrated by means of a simple dynamical system.

Early‐age concrete exhibits limited mechanical strength, making it highly susceptible to external disturbances such as vibrations from construction activities, traffic, or nearby machinery which can disrupt internal structure. This paper presents a state‐of‐the‐art review of this underexplored and critical topic, drawing primarily data from laboratory experiments and field active construction sites. Reported results indicate that intensities exceeding approximately 0.2 g acceleration paved a reduced compressive strength. Some of the control groups in the literature exhibited the highest average tensile strength, while the weakest group showed only a 7% decrease. These inconsistencies are due to alteration/overlap in the chemical activity, particle distribution, air bubble formation. The literature reveals inconsistent findings regarding the thresholds of vibrations, as well as conflicting conclusions on vibration parameters and mechanical properties. Furthermore, limited research exists on key factors such as the role of admixtures, curing regimes, material composition, vibration screening methods, and the anisotropy of damage. Fiber reinforced specimens studied at early ages had improved vibration resistance, with strength ratio falling between 0.8–1.0. By consolidating current knowledge and identifying research gaps, this review aims to inform best practices and propose future directions to optimize construction processes and mitigate the adverse effects of early‐age vibrations on concrete performance.