The development of structural health monitoring (SHM) techniques is of great importance to
improve the structural efficiency and safety. With advantages of long propagation distances …

Composite materials have been widely used in many industries due to their excellent
mechanical properties. It is difficult to analyze the integrity and durability of composite …

The detection and localization of damage in metallic structures using acoustic emission (AE)
monitoring and artificial intelligence technology such as deep learning has been widely …

In this research work, a deep Convolutional Neural Network (CNN) was trained for image-
based Acoustic Emission (AE) waveform classification. AE waveforms from different damage …

Sandwich structures used in transportation and energy industries are commonly subjected
to low-velocity impact in the course of manufacturing, service and maintenance, which can …

Accurate detection of damage in composite structures is of great significance to ensure safe
service and avoid catastrophic accidents. In this paper, a novel damage diagnosis method …

With the booming prosperity of artificial intelligence (AI) technology, it triggers a paradigm
shift in engineering fields including material science. The integration of AI and machine …

A novel spatial–temporal graph convolutional networks (STGCN) based method for the
regression task of localizing acoustic emission (AE) sources in composite panels is …

Due to aging structures, deterioration is becoming an essential issue in the engineering and
facility management industry. Especially for nuclear power plants, the deterioration of …

This paper proposes a real-time non-destructive evaluation technique to detect defects in
laminated composites by deep learning using highly nonlinear solitary waves (HNSWs) …