In this paper a new signal processing technique on multiple piezoelectric sensor/actuator for structural health monitoring of steel structure joints is presented.This technique coupled the fast fourier transformer with wavelet MATLAB simulink...

identification. Based on these data and mentioned procedures, it is possible to demonstrate the superior performance of random decrement technique in the statistical pro-cess control as an applicable and useful method for structural health monitoring. Results...

Statistical time-series modeling has recently emerged as a promising and applicable methodology to structural health monitoring. In this methodology, an important step is to choose robust and optimal orders of time-series models for extracting...

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article...

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient
statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article...

This study presents a new similarity measure method in order to detect and locate structural

damage based on statistical pattern recognition paradigm. The similarity method is

Bhattacharyya distance (BD) ...

Structural health monitoring (SHM) using artificial neural networks has received increasing

attention due to robustness of neural networks, better performance compared to conventional

damage detection methods, and influential pattern...

Anomaly detection by Mahalanobis-squared distance (MSD) is a popular unsupervised learning approach to structural health monitoring (SHM). Despite the popularity and high applicability of the MSD-based anomaly detection method, some major...