SS26: Artificial Intelligence Techniques for Structural Health Monitoring
Summary: Recent advances in the field of Structural Health Monitoring (SHM) have shown that employing Artificial Intelligence algorithms appears to be a promising solution towards the improvement of the accuracy and robustness of SHM frameworks. In this context, many methods have been employed to post-process the information gathered through a network of sensors installed on the structure, to improve damage detection, localisation, quantification and prognosis. Most of these have belonged to the field of Machine Learning.
This special session of SEMC 2022 aims at collecting recent advances in the field of Artificial Intelligence techniques for Structural Health Monitoring. Both theoretical contributions and applications based on experimental and/or numerical case studies are welcome in this session. Hot topics include, but are not limited to: (i) deep learning techniques for damage identification, diagnosis and/or prognosis; (ii) interpretability of intelligent algorithms; (iii) shifting from supervised to unsupervised approaches. The most recent scientific contributions from key players at the intersection SHM and Artificial Intelligence will be featured.