Alexander Mendler

Static and dynamic load tests were conducted on an anonymized in-service prestressed concrete box girder bridge constructed in 1972 and designed for Bridge Class 30.The bridge is instrumented as part of the Digitalization and Technology Research Center of the Bundeswehr-Structural Health Monitoring (DTEC-SHM) research initiative with a permanent structural health monitoring system comprising 128 sensors, including accelerometers, strain sensors, inclinometers, displacement transducers, embedded temperature sensors, and weather stations. The test program includes long-term static loading with stepwise added masses of 680, 1,420, and 2,160 kg; short-term static truck parking positions; and repeated dynamic truck crossings under loaded and unloaded vehicle configurations. The short-term static tests used a loaded truck of 21,250 kg parked at three bridge positions, while the dynamic tests used loaded and unloaded truck configurations of 21,250 and 12,340 kg at speeds up to 13.9 m/s. The published load-test data are available on Zenodo in HDF5 format and include synchronized measurements from the monitoring system, temporary laser sensors used for vehicle-position and passage-time validation, and derived tracked eigenfrequency time series from the reference and long-term static load-test period. The bridge geometry, sensor layout, acquisition system, load cases, truck geometry, and data hierarchy are documented to support reuse of the data. Plausibility checks are presented for the main response quantities, including laser-based validation of parking positions and passage windows, vibration spectra and tracked modal parameters, and repeatable strain and displacement response profiles under crawl-speed truck passages. The data set addresses the scarcity of publicly available full-scale bridge load-test data and supports applications such as finite-element model calibration, neutral-axis tracking, influence-line extraction, and data-driven anomaly detection.

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.

Accumulated deposits in pipelines, such as scaling, are a major concern across industries. Numerous approaches are used to monitor scale buildup and to verify the effectiveness of descaling measures. This is particularly challenging in geothermal plants, where testing techniques capable of enduring high-temperature conditions are required. And while developing appropriate non-destructive inspection techniques is crucial, suitable tools to assess the reliability of these techniques are just as important. Probability of detection (POD) curves are standard tools for reliability assessment, but they would require a substantial amount of data from pipes with different scaling degrees. The so-called predictive probability of detection (P-POD) curves are a suitable alternative, as they consider the statistical variability in the measurements and can be generated using only information from a single measurement record from the undamaged state. This paper is a proof-of-concept study, where P-POD is applied to scaling monitoring for the first time. The dataset was recorded using ultrasonic testing in a pulse-echo configuration on a heavily scaled pipeline section from the geothermal power plant in Sauerlach, Germany. The analysis was based on time-of-flight considerations. P-POD curves and confidence intervals were constructed for both increasing (i.e., scaling) and decreasing (i.e., descaling) thickness changes, using a single reference measurement for each case. Subsequently, the predictions were validated with experimental data. The results demonstrate the accuracy of the predicted POD. The successful application of the P-POD method translates into a significant contribution to pipeline monitoring in general, where reliable fouling inspection is key for minimizing operational difficulties.

Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.