Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in
various industries, especially medicine. AI describes computational programs that mimic and …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …

Unsupervised learning is an effective and practical methodology for structural health
monitoring when the preparation of labeled training data regarding damaged states is …

Abstract Structural Health Monitoring (SHM) is an emerging field of engineering with a wide
range of applications. The most common SHM strategies operate on structural responses …

Early damage detection is an initial step of structural health monitoring. Thanks to recent
advances in sensing technology, the application of data-driven methods based on the …

Continuous dynamic monitoring brings an important opportunity to evaluate the health and
integrity of civil structures in a long-term manner. However, high dimensionality and sparsity …

Semi-supervised learning has become a hot learning framework, where large amounts of
unlabeled data and small amounts of labeled data are available during the training process …

This study proposes an unsupervised, online structural health monitoring framework robust
to the sensor configuration, that is, the number and placement of sensors. The proposed …