Inspired by deep learning applications in structural mechanics, we focus on how to train two
predictors to model the relation between the vibrational response of a prescribed point of a …

The acoustical properties of wood are primarily a function of its elastic properties. Numerical
and analytical methods for wood material characterization are available, although they are …

We show that deep convolutional neural networks (CNNs) can massively outperform
traditional densely connected neural networks (NNs)(both deep or shallow) in predicting …

Understanding vibroacoustic wave propagation in mechanical structures like airplanes, cars
and houses is crucial to ensure health and comfort of their users. To analyze such systems …

This paper leverages each pixel of a picture acquired from a video camera, in which
structural dynamic information is contained, in order to decompose spatiotemporal …

This study utilizes vibration-based signal analysis as a non-destructive testing technique that
involves analyzing the vibration signals produced by a structure to detect possible defects or …

In the case of damage detection, it is important to estimate the frequencies accurately. DFT-
based methods provide us with amplitude-frequency pairs, but displayed frequencies carry …

We recently developed a neural network that receives as input the geometrical and
mechanical parameters that define a violin top plate and gives as output its first ten …

Bridges are the essential components in lifeline transportation systems, and their safe
operation is of great importance. Information on structural damage could assist in timely …

The conventional modal analysis involves physically-attached wired or wireless sensors for
vibration measurement of structures. However, this method has certain disadvantages …