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

In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are
making big changes to the way traditional structural health monitoring (SHM) is done. Also …

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

Abstract Machine learning algorithms are becoming increasingly resource-intensive. In
contrast to classical computing, quantum computing holds the potential with exponential …

Noise is seen as the annoying something for weak signal detection, but noise is beneficial to
enhance weak signals of interest embedded in noise in nonlinear systems. Moreover, the …

Strength serves as a vital performance metric for assessing long-term durability of cement-
based materials. Nevertheless, there is a scarcity of models available for predicting residual …

This paper presents a footbridge damage detection and classification framework using
smartphone-recorded responses of micromobility and deep learning techniques. Time …

Abstract Design of an automated and continuous framework is of paramount importance to
structural health monitoring (SHM). This study proposes an innovative multi-task …

One of the serious issues of traditional signal processing techniques in analyzing the
responses of real-life structures is related to the presentation of fundamental information of …

Active noise cancellation (ANC) is an efficient and effective method for suppressing low
frequency disturbances generated by tyre–road interaction in a vehicle. There are significant …