Matheus Dalcin

An Adaptive Unsupervised Structural Health Monitoring Framework Using Exclusion-Aware Convolutional Autoencoders

This paper proposes an anomaly detection framework that combines the sequential retraining of CAE networks with an exclusion logic strategy, and demonstrates good generalizability, seamless adaptability to varying monitoring scenarios, and robustness across both training and validation datasets.