Contactless Deformation Monitoring of Bridges with Spatio-Temporal Resolution: Profile Scanning and Microwave Interferometry.

Against the background of an aging infrastructure, the condition assessment process of existing bridges is becoming an ever more challenging task for structural engineers. Short-term measurements and structural monitoring are valuable tools that can lead to a more accurate assessment of the remaining service life of structures. In this context, contactless sensors have great potential, as a wide range of applications can already be covered with relatively little effort and without having to interrupt traffic. In particular, profile scanning and microwave interferometry, have become increasingly important in the research field of bridge measurement and monitoring in recent years. In contrast to other contactless displacement sensors, both technologies enable a spatially distributed detection of absolute structural displacements. In addition, their high sampling rate enables the detection of the dynamic structural behaviour. This paper analyses the two sensor types in detail and discusses their advantages and disadvantages for the deformation monitoring of bridges. It focuses on a conceptual comparison between the two technologies and then discusses the main challenges related to their application in real-world structures in operation, highlighting the respective limitations of both sensors. The findings are illustrated with measurement results at a railway bridge in operation.

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.