Structural health monitoring (SHM) has become an important and hot topic for decades in
various fields of civil, mechanical, automotive, and aerospace engineering, etc. Estimating …

To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …

Artificial intelligence (AI) is proving to be an efficient alternative approach to classical
modeling techniques. AI refers to the branch of computer science that develops machines …

Abstract Structural Health Monitoring (SHM) systems provide a useful tool to diagnose any
engineered structural system and eventual critical damage that may occur at any moment …

Smart structural health monitoring (SHM) for large-scale infrastructure is an intriguing
subject for engineering communities thanks to its significant advantages such as timely …

Structural systems are under deterioration due to ageing, mechanical stressors, and harsh
environment, among other threats. Corrosion and fatigue can cause gradual structural …

A novel time series analysis is presented to locate damage sources in a mechanical system,
which is running in various operational environments. The source of damage is located by …

This paper casts structural health monitoring in the context of a statistical pattern recognition
paradigm. Two pattern recognition techniques based on time series analysis are applied to …

This article explores the use of principal component analysis (PCA) and T2 and Q-statistic
measures to detect and distinguish damages in structures. For this study, two structures are …

Statistical pattern recognition methodologies have gained considerable attention for
Structural Health Monitoring (SHM) applications to detect changes in a structure (eg …