One challenge in structural health monitoring (SHM) is to make reliable statements about the condition of the structure using only a few sensors, which is desirable from both technical and economic perspectives, as wind turbines often already have one acceleration sensor installed at the nacelle. Virtual sensing methods are used for this purpose, enabling the estimation of the structural response at non-instrumented locations using a structural model and measurement data. Knowledge of the structural response of wind turbines is desirable, as it provides the foundation for lifetime extension or load-optimised operation.
The method applied here uses only one DC-capable acceleration sensor to estimate the full-field strain across the entire support structures of wind turbines. It utilises the tilt error present in the measured acceleration of tower structures to estimate the displacement in the frequency range below 0.05~Hz. The strains are subsequently estimated from the displacements using a transfer function based on a multi-band modal decomposition and expansion.
For this contribution, the proposed method is applied to measurement data from an operating offshore wind turbine. The focus lies on the influence of uncertainties in the structural model on the accuracy of the strain estimation. The investigated modelling errors include parameters that are usually known to the operators with high levels of uncertainty, such as modelling of the scour protection, the soil stiffness, and the rotor. The influence of the selected parameters on the accuracy of strain estimation is compared using the damage-equivalent strains.
The results showcase which parameters have a significant influence on the accuracy of strain estimation and indicate whether measures should be taken to reduce the uncertainty in the modelling to increase the strain estimation accuracy.

The detection of rail defects is a crucial topic for companies operating railway networks to efficiently drive the rail maintenance. For this, the equipment of trains with measuring devices has the advantage of covering the complete network with a reduced number of sensors, compared with fixing sensors on the tracks.

In this context, Vibratec and RATP have developed a methodology to process the measurements of accelerometers fixed on the axle boxes, in order to detect rail defects and to classify them in terms of defect nature (squats and other deteriorations of the tread surface, matted insulation joints, unstable sleepers, broken rails), and of defect severity. In this methodology, the vertical acceleration signals are double integrated to obtain displacement signals. A wavelet decomposition of the vertical displacement signals is then performed, providing a cartography indicating the amplitude of the displacement at each spatial position, for wavelengths between 0 and 5 meters. Unstable sleepers generate high displacement amplitudes in high wavelengths, broken rail in medium wavelengths, and squats and joint matting generate high displacement amplitudes in low wavelengths.

The methodology was first developed using simulations results of a multi-body bogie model running on a finite-element straight track with several defects. It was then applied on measurements performed on a test track with a broken rail, and on measurements collected on the RER A network. More recently, the suitability of the method for the detection of insulation joints and the classification of their deterioration level was investigated. The main outcomes of these development steps will be presented at the EWSHM conference.

This study investigates the feasibility of using low-cost smartphone accelerometers for drive-by bridge monitoring, with a focus on dynamic identification under realistic conditions. Laboratory and field experiments were conducted on a scaled reinforced concrete bridge and a full-scale reinforced concrete bridge using vehicle-mounted smartphones, with performance evaluated under partially and fully drive-by configurations across different vehicle types and excitation scenarios. Results show that partially drive-by monitoring provides reliable and consistent frequency identification, comparable to bridge-mounted measurements. In contrast, fully drive-by monitoring is highly dependent on excitation: low excitation leads to poor identification due to weak vehicle–bridge interaction and noise, whereas higher excitation significantly improves performance. Differences between vehicle types further highlight the influence of stability on signal quality. Overall, smartphone-based sensing offers a flexible, low-cost, and scalable solution for bridge monitoring, although its effectiveness is governed by excitation level, signal quality, and vehicle–bridge interaction, supporting the development of mobile and crowd-sourced structural health monitoring systems.