Structural health monitoring for lightweight complex composite structures is being
investigated in this paper with a data-driven deep learning approach to facilitate automated …

This paper presents a new structural health monitoring strategy based on a deep learning
architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing …

Damage detection and the classification of carbon fiber-reinforced composites using non-
destructive testing (NDT) techniques are of great importance. This paper applies an acoustic …

Intelligent composite structures with self-aware functions are preferable for future aircrafts.
The real-time location of damaged areas of composites is a key step. In this study, deep …

Delamination is one of the detrimental defects in laminated composite materials that often
arose due to manufacturing defects or in-service loadings (eg, low/high velocity impacts) …

Ultrasound based structural health monitoring of piezoelectric material is challenging if a
damage changes at a microscale over time. Classifying geometrically similar damages with …

A vibration data‐based machine learning architecture is designed for structural health
monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag‐of‐Features …

This paper presents a machine learning framework that uses the bag of visual words
(BOVW) for structural health monitoring (SHM) of a composite sandwich structure (CSS) …

This research paper presents a novel structural health monitoring strategy based on a hybrid
machine learning and finite element model updating method for the health monitoring of …

This paper introduces a multistage smart structural health monitoring (SHM) model for
carbon-fibre composites, with a focus on multiple types of acoustic emission (AE) source …