Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

Aug 4, 2026

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The study develops a vibration-signal–based approach for structural damage assessment of beam structures subjected to moving loads. It focuses on how correlation coefficients extracted from measured vibration responses can reveal stiffness, mass distribution, and damping changes induced by damage. To interpret the correlation-informed features, a machine learning algorithm and an artificial neural network are employed to enable early detection and localization while addressing limitations of traditional inspection methods. The paper also emphasizes the role of data preprocessing, including techniques such as random decrement, to improve robustness and signal clarity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Structural damage and moving-load effects on beams

Vibration-based damage identification (VBDI)

Signal preprocessing and feature preparation

Random decrement technique (RDT) and Random Decrement Signatures (RDS)

Motivation for correlation-based features and machine learning

T. Pham-Bao et alii, Frattura edIntegrità Strutturale, 70 (2024) 55-70; DOI: 10.3221/IGF-ESIS.70.03 | Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load
Toan Pham-Bao*, Vien Le-Ngoc
Laboratory of Applied Mechanics (LAM), Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi
Minh City, VietNam [baotoanbk@hcmut.edu.vn](baotoanbk@hcmut.edu.vn), [https://orcid.org/0000-0002-2105-2403](https://orcid.org/0000-0002-2105-2403)
[lnvien.sdh19@hcmut.edu.vn](lnvien.sdh19@hcmut.edu.vn), [http://orcid.org/0000-0002-8154-1014](http://orcid.org/0000-0002-8154-1014) | | | --- | --- | | |
Citation: Pham-Bao, T., Le-Ngoc, V., Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load, Frattura ed Integrità Strutturale, 70 (2024) 55-70.
Received: 24.05.2024
Accepted: 09.07.2024
Published: 16.07.2024
Issue: 10.2024
Copyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | | KEYWORDS. Beam structures, Correlation coefficient, Machine learning, Artificial neural network, Structural health monitoring. | | | INTRODUCTION
B ridges, buildings, aircraft wings, and other civil infrastructure applications rely on beam structures. It is clear that
maintaining the structural integrity of beam structures to ensure safety, reliability, and longevity is essential. It is common for beam structures to degrade over time due to fatigue, corrosion, and external loading, all of which can negatively impact their mechanical characteristics and ultimately affect their structural integrity. It is common for structures to be subjected to dynamic loads from moving vehicles, which are the leading cause of gradual deterioration and damage over time. Excitation of heavy vehicles, in particular, can cause significant stresses and strains, which can lead to cracks, | | T. Pham-Bao et alii, Frattura edIntegrità Strutturale, 70 (2024) 55-70; DOI: 10.3221/IGF-ESIS.70.03 deformations, and other forms of structural damage. In spite of their effectiveness, traditional inspection methods are timeconsuming, labor-intensive, and costly. It is, therefore, becoming increasingly important to develop automated, nondestructive monitoring techniques for these structures. For example, Dipendra Gautam et al. studied safety evaluations of a prestressed concrete bridge by using vibration properties obtained from dynamic identification. The finite element model is updated based on vibration frequencies calculated using parametric and non-parametric system identification methods. Through periodic measurements and system identification, the calibrated model can be used for further analyses, including non-linear response and damage detection [1] . Furthermore, Xiang Zhu et al. provided a concise survey of damage identification in bridges by processing dynamic responses to moving loads. It includes four methods: three direct (Fourier Transform, Wavelet Transform, Hilbert-Huang Transform) and one indirect (Heuristic Interrogation of Damage) [2] . To improve damage detection capabilities, advanced techniques based on structural dynamics and data analytics have been increasingly popular. Vibration-based damage identification (VBDI) has emerged as one of the most promising techniques, as it is sensitive to subtle changes in structural behaviour caused by damage [3, 4] . The method is based on the fact that damage alters the structural dynamics, resulting in changes to vibration characteristics like frequencies, mode shapes, or modal parameters. Through the use of sensors for data collection and advanced algorithms, structural damage and degradation can be identified based on vibration signals. Additionally, VBDI involves analysing

FAQs

Why are vibration-based techniques important for detecting damage in beam structures under moving loads?

Vibration-based damage identification is sensitive to subtle changes in structural behavior caused by damage. Damage alters dynamic characteristics such as frequencies, mode shapes, and modal parameters, enabling early detection and localization when monitored before severe deterioration.

What limitations do traditional inspection methods have, and how does this motivate automated monitoring?

Traditional inspection methods are time-consuming, labor-intensive, and costly. This drives the need for automated, nondestructive monitoring techniques that can process vibration data efficiently.

What is the role of preprocessing techniques like the random decrement technique (RDT)?

RDT helps isolate structural responses from noise by reducing random noise and extracting underlying trends or patterns. This improves the clarity of vibration signals and enhances the reliability and accuracy of subsequent analysis and machine learning performance.

Aug 4, 2026

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