Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods

Aug 5, 2026

A vibration-data-driven framework is presented for identifying structural damage in a wooden bridge. The response is decomposed using the Fourier decomposition approach into Fourier Intrinsic Band Functions (FIBF), separating structure-related information from vibration noise. Time series modeling extracts damage-sensitive features, and residuals from undamaged and damaged cases are used as indicators. Supervised machine learning models—including ANN, KNN, SVM, ensemble learning, and decision tree—perform classification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Abstract

Introduction

Structural health monitoring overview

Damage detection phases

Data-based vs model-based methods

Time series modeling and feature extraction

Fourier decomposition and FIBF concept

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Contents lists available at SCCE | | | --- | --- | |
Journal of Soft Computing in Civil Engineering | | |
[Journal homepage:](Journal homepage: www.jsoftcivil.com)[ www.jsoftcivil.com](Journal homepage: www.jsoftcivil.com) | | | Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods
Younes Nouri1, Farzad Shahabian1*, Hashem Shariatmadar1, Alireza
Entezami2
1. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2. Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy Corresponding author: [shahabf@um.ac.ir](shahabf@um.ac.ir)
[https://doi.org/10.22115/SCCE.2023.401971.1669](https://doi.org/10.22115/SCCE.2023.401971.1669) | | | ARTICLE INFO | ABSTRACT
In this article, a novel approach has been employed to identify structural damage in the wooden bridge structure by utilizing vibration data. This method encompasses the Fourier decomposition method that decompose the response of the bridge into a sequence of Fourier Intrinsic Band Functions (FIBF) . These functions comprise the responses of the structure that contain inherent information of structure as well as noise from the vibrations. The time series modeling is utilized to extract damage-sensitive features. The residuals of the time series model of both undamaged and damaged structures are extracted for detecting any damage. To ascertain the presence of damage, supervised classification machine learning algorithms are employed. The algorithms are utilized consist of Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), support vector machines (SVM), ensemble learning, and decision tree. The results indicate that the proposed method of feature extraction is highly effective and reliable in detecting damages. In addition, the capacity of decision tree and ANN algorithms to minimize type 2 error and enhance accuracy is demonstrated when evaluating different machine learning algorithms. The value of the type II error in the ANN model and the decision tree is equal to 13.85% and the accuracy of the model is 93.02% . | | Article history:
Received: 12 June 2023
Revised: 05 October 2023
Accepted: 07 October 2023 | | | Keywords:
Structural health monitoring; Damage detection; The Fourier decomposition method;
Time series;
Machine learning. | | 1. Introduction The process of condition assessment of structural system and damage detection using vibration data and vision data is called structural health monitoring (SHM) [1,2] . At the beginning, visual inspection methods were used to evaluate the performance and health of structures. With the advancement of technology in the production of vibration sensors, data acquisition devices and analyzers, the process of monitoring the health of the structure is carried out using vibration measured data [3–5] . Damage detection in the SHM process, in general, is investigated in four main phase, including early damage detection, location, severity, and predicting the life of the structure after the damage occurs [6,7] . In the first phase, the overall condition of the structure is evaluated. In other words, based on the results in this method, it is possible to find out the occurrence or non-occurrence of damage in the structure. In the second phase, after determining the structural damage, an attempt is made to identify its location of damage. Next, the severity of the damage is estimated at the different states. Finally, with the information obtained from the previous steps, the remaining life of the structure and its performance can be predicted. In the health monitoring of the structure, these steps can be done based on two general solutions, including methods based on the vibration data (data based) and methods based on a physical model of the structure (model based) [8] . In these methods, autoregressive models (AR) have b

FAQs

How does the proposed method detect damage in the wooden bridge?

It uses vibration data, decomposes the bridge response via Fourier decomposition into FIBF, extracts damage-sensitive features through time series modeling, and classifies residual-based indicators to determine whether damage is present.

Which machine learning algorithms are used for supervised classification?

The approach applies Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), support vector machines (SVM), ensemble learning, and decision tree for damage classification.

What features are considered damage-sensitive in this framework?

Damage-sensitive features are obtained from time series modeling outputs, specifically from the extracted model residuals of both undamaged and damaged structures.

Aug 5, 2026

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