Open Access

A digital twin for decision making during the life cycle of civil infrastructure is a promising concept combining simulation models with corresponding structure-specific sensor data to support maintenance decisions or to investigate the reliability. The sensor data quality as well as the model quality of the digital twin, comprising on modeling assumptions and correct model parameters strongly influences the prognosis results.

Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.

Wind energy has become an important player in the energy transition in Germany. Towers of onshore wind turbines are often designed as hybrid structures: the lower part is made of prestressed concrete whereas the upper part is made of steel tubes. The tall structures are permanently subjected to cyclic loads. A research project of BAM as part of the joint project WinConFat - Structure focusses on the evelopment of techniques to monitor fatigue damage evolution in the concrete part. Results of a previous project show that a combination of ultrasonic and acoustic emission testing can give an indication for critical conditions near the end of the fatigue life of the concrete. In the ongoing project acoustic emission sensors have been installed at the base and at the transition piece between concrete and steel of a hybrid wind turbine tower. Beside of acoustic emission measurement the sensor spacing allows for measuring the concrete ultrasonic velocity along the circumference in both levels. Additional measurements like strain, temperature, inclination, or acceleration allow for comparison of environmental loads and change of acoustic properties of the concrete. The paper focusses on first acoustic measurements recorded since December 2023 in comparison to operating data of the wind turbine.

For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved.

Orientation dependence of stress-induced martensitic transformation under compression and the influence of a corrosion attack on superelastic properties were investigated for Fe42.7Mn34.7Al13.4Ni7.7Cr1.5 (at.−%) single crystals. The results of incremental strain tests show that the crystallographic orientation has a considerable impact on the superelastic performance, eventually resulting from the formation of twinned or detwinned martensite to accommodate strain as well martensite variant interaction. In order to investigate the effect of a corrosive environment on the mechanical performance and martensitic transformation, compression specimens were immersed in a 5.0 wt.−% NaCl solution for 24 h before tested in incremental strain tests. The immersion of the compression specimens revealed a partial surface corrosion attack including localized pitting corrosion. The localized corrosion attack increased the number of active martensite plates, most probably due to an induced multiaxial stress state. Further investigations on specimens subjected to −6% compressive strain revealed that areas with retransformed martensite serve as nucleation zones for corrosion damage. Stress-induced corrosion cracks developed, which eventually deteriorate functional response.