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 $v_{\max }=56.3\mathrm{m}/\mathrm{s}$. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

Fracture morphology characterization in broken glass panes is crucial for designing laminated safety glass (LSG) in civil engineering. Verifying completely broken LSG systems requires destructive sampling, increasing costs and hindering development. Therefore, to determine the residual load-bearing capacity, the link between the pre-fracture characteristics and the fracture morphology must be known. However, when the crack propagation needs to be directly captured with high-speed imaging, conventional methods are no longer sufficient for detecting cracks. To enable such investigations, we propose a novel machine learning framework for crack segmentation in high-speed imaging that addresses the complexity of glass fracture and minimises the required labour costs. In this study, the crack propagation of a sample was recorded and analysed at 2,000,000 images per second. The results showcase accuracies surpassing 97% while requiring only two labeled images for training, thus streamlining practical implementation. Furthermore, we show the method's robustness to the extent that hyperparameter tuning becomes unnecessary. Instead, we offer guidelines for selecting the most crucial hyperparameters depending on the problem. Our method offers a promising approach for non-linear temporal interpolation of noisy images, with implications for various applications extending beyond glass fracture analysis.