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

Traditionally, damage identification techniques in bridges have focused on monitoring
changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship …

Monitoring of modal frequencies under an unsupervised learning framework is a practical
strategy for damage assessment of civil structures, especially bridges. However, the key …

Clustering is a popular and useful unsupervised learning method with various algorithms for
applying to many engineering problems. However, some practical and technical issues such …

Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are
extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural …

We propose an outlier detection‐based statistical approach to identify and locate a defect in
composite plates using far fewer number of sensing points compared to conventional …

Modal frequencies are widely used for vibration‐based structural health monitoring (SHM)
and for capturing the dynamics of a monitored structure to reveal possible failures. However …

While many techniques for outlier detection have been proposed in the literature, the
interpretation of detected outliers is often left to users. As a result, it is difficult for users to …

In the data-based approach to structural health monitoring (SHM), the absence of data from
damaged structures in many cases forces a dependence on novelty detection as a means of …

Distance-based anomaly detectors are among the most efficient unsupervised learning
methods due to their non-parametric properties, inexpensive computational requirements …