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 …

Damage identification forms a key objective in structural health monitoring. Several state‐of‐
the‐art review papers regarding progress in this field up to 2011 have been published. This …

Deep learning has ushered in many breakthroughs in vision‐based detection via
convolutional neural networks (CNNs), but the vibration‐based structural damage detection …

The difficulty in quantifying the benefit of Structural Health Monitoring (SHM) for decision
support is one of the bottlenecks to an extensive adoption of SHM on real-world structures …

A data-driven approach based on Optimized AdaBoost-Linear SVM is proposed to indicate
the bridge damage using only raw vibration signals received from a vehicle passing over the …

This study presents a novel artificial neural network (ANN) based methodology within a
vibration-based structural health monitoring framework for robust damage detection. The …

The analysis presented in this work relates to the quantification of the effect of a selected set
of measured Environmental and Operational Parameters (EOPs) on the dynamic properties …

Introducing deep learning algorithms into the field of structural health monitoring (SHM) has
contributed to the automatic extraction of damage-sensitive features, but the type and …

Coastal bridges are vulnerable to complicated hydrodynamics induced by hostile natural
hazards, relevant research is thus required to ensure the safe operation of these critical …

Overtime, bridge condition declines due to a number of degradation processes such as
creep, corrosion, and cyclic loading, among others. Traditionally, vibration-based damage …