Monitoring structures such as bridges or wind turbines using Structural Health Monitoring (SHM) methods allows continuous assessment of their condition and considerably faster action compared to conventional on-site inspection. Inevitably, SHM systems suffer from the same problems as the structure itself: Not only does the structure age over time, but the measurement system itself is also subject to environmental aging. To ensure robust SHM, this paper presents a concept that combines optimal sensor placement with a hardware redundancy approach to provide the most efficient, durable, and reliable SHM for structures. For this purpose, a small-scale experiment is conducted in a climate chamber equipped with various sensor types (accelerometers and strain gauges). The different levels of sensor integration allow reliable detection of sensor errors in the raw data signal. Correlation matrices are used to decide which sensor functions are most reliable at what point in time and whether sensor replacement in the SHM system is required. Furthermore, data normalisation is applied to the raw data signal to compensate for the influence of environmental conditions (ECs) such as temperature and humidity. The proposed concept is validated on the small-scale dynamically excited experiment and shows promising results to operate reliable SHM systems for several decades in the future.

Acceleration sensors are vital for assessing engineering structures by measuring properties like natural frequencies. In practice, engineering structures often have low natural frequencies and face harsh environmental conditions. Understanding sensor behavior on such structures is crucial for reliable measurements. The research focus is on understanding the behavior of acceleration sensors in harsh environmental conditions within the low-frequency acceleration range. The main question is how to distinguish sensor behavior from structural influences to minimize errors in assessing engineering structure conditions. To investigate this, the sensors are tested using a long-stroke calibration unit under varying temperature and humidity conditions. Additionally, a mini-monitoring system configured with four IEPE sensors is applied to a small-scale support structure within a climate chamber. For the evaluation, a signal-energy approach is employed to distinguish sensor behavior from structural behavior. The findings show that IEPE sensors display temperature-dependent nonlinear transmission behavior within the low-frequency acceleration range, with humidity having negligible impact. To ensure accurate engineering structure assessment, it is crucial to separate sensor behavior from structural influences using signal energy in the time domain. This study underscores the need to compensate for systematic effects, preventing the underestimation of vibration energy at low temperatures and overestimation at higher temperatures when using IEPE sensors for engineering structure monitoring.

The construction industry is a significant consumer of natural resources and a major contributor to CO2 emissions in Germany, accounting for an annual waste volume of 54%. Therefore, preserving existing structures can play a crucial role in environmental protection and resource conservation. To achieve this goal, effective monitoring and maintenance concepts are essential to extend the service life of structures while ensuring their stability and functionality. In 2022, the German Research Foundation launched a priority program called 'SPP 100+' with the objective of fundamentally redefining the current maintenance strategy for infrastructure structures through intelligent digitization. This paper focuses on the Nibelungen Bridge in Worms, which serves as a validation case in this program. The paper begins by presenting the Nibelungen Bridge and its maintenance history. It then outlines key aspects, including geometric modeling, structural health monitoring (SHM), digital twinning, level 4 recalculation considering complex nonlinearities, and the assessment of detected structural damages. In the end, conclusions and outlooks are given.