Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets.
arXiv:2607. 02545v1 Announce Type: cross Abstract: Structural health monitoring (SHM) has emerged as an essential tool for ensuring the integrity and reliability of critical engineering structures, particularly in aerospace applications.
By Xin Yang, Morteza Moradi, Tongtong Yan, Jinbo Du, Yunlai Liao, Dimitrios Zarouchas, Dimitrios Chronopoulos
arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.
By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga
The paper introduces a Physics‑Informed Neural Network (PINN) tailored for predicting Ground‑Penetrating Radar (GPR) data, integrating electromagnetic wave propagation physics into a deep learning framework. The architecture combines a CNN, spatial feature channel attention, ConvLSTM, and temporal feature frame attention to extract relevant visual and temporal features. Results show improved accuracy in forecasting GPR data, aiding assessments of bridge deck conditions and other civil infrastructure evaluations.
By Mehrdad Shafiei Dizaji, Hoda Azari
The paper presents a method for detecting and classifying damage in wind turbine blades using aerodynamic pressure measurements from the Aerosense system. A convolutional neural network was trained on data from a NACA 633418 airfoil mounted on a vibrating cantilever, where damage was introduced via saw cuts. The study also incorporates physics-based insights and explainable ML techniques to interpret how damage affects dynamic response and pressure fields, enhancing transparency and robustness of the monitoring pipeline.
By Philip Franz, Max von Danwitz, Gregory Duth\'e, Alexander Popp, Eleni Chatzi
The paper introduces FreqCondNorm, a Transformer-based architecture that adds a frequency-conditioned normalization layer to unify heterogeneous time-series data for predictive maintenance. The model is pretrained on five public datasets using masked auto‑encoding and contrastive learning, achieving 99.2% accuracy on CWRU and 82.1% zero‑shot accuracy on MFPT, showing strong transfer across sampling frequencies. However, it does not improve remaining useful life prediction, indicating a mismatch between pretraining and RUL objectives that requires further study.
By Zaynab Raounak, Camille LHermine, Zhiguo Zeng