Structural damage detection from dynamic responses of the bridge deck under a moving load using discrete wavelet transform

被引:3
作者
Gavgani, Seyyed Ali Mousavi [1 ]
Zarnaghi, Amir Ahmadnejad [2 ]
Heydari, Sajad [3 ]
机构
[1] Kharazmi Univ, Fac Engn, Dept Civil Engn, Tehran 1571914911, Iran
[2] Shahid Rajaee Teacher Training Univ, Dept Civil Engn, Tehran 1678815811, Iran
[3] Semnan Univ, Fac Engn, Dept Civil Engn, Semnan 3513119111, Iran
来源
ADVANCES IN BRIDGE ENGINEERING | 2022年 / 3卷 / 01期
关键词
Structural health monitoring; Damage detection; Discrete wavelet transform; Sensor noise; Vibrational response; Bridge deck; CRACK IDENTIFICATION;
D O I
10.1186/s43251-022-00053-2
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Early detection of structural damages and making necessary interventions to repair them are one of the main challenges in structural health monitoring. The wavelet transform is one of the common methods for this purpose, and its efficiency is proven by many researchers. In the present study, this approach is used to assess the performance of Sani-khani bridge with single and multiple-damage scenarios. For this purpose, the displacement response difference between the intact and damaged bridge decks under a moving load is analyzed by discrete wavelet transform (DWT). In the present study, 10 sensors and one-time sampling are used, In fact, the proposition of a method that uses the minimum number of required sensors for practical damage detection. To verify the reliability of the suggested method, not only different damage locations were considered, but also 5% noise is considered for the input signals. The attained results proved that even in the presence of the noise, the proposed approach can detect the damage locations with acceptable accuracy. The accuracy of the method for middle and side damages is higher than corner damages.
引用
收藏
页数:21
相关论文
共 44 条
[1]   Application of two-dimensional wavelet transform to detect damage in steel plate structures [J].
Abdulkareem, Muyideen ;
Bakhary, Norhisham ;
Vafaei, Mohammadreza ;
Noor, Norhazilan Md ;
Mohamed, Roslli Noor .
MEASUREMENT, 2019, 146 :912-923
[2]   New damage index based on least squares distance for damage diagnosis in steel girder of bridge's deck [J].
Ahmadi, Hamid Reza ;
Anvari, Diana .
STRUCTURAL CONTROL & HEALTH MONITORING, 2018, 25 (10)
[3]  
Ahmadnejad Zarnaghi A., 2018, J Model Eng, V16, P299, DOI [10.22075/jme.2017.5623, DOI 10.22075/JME.2017.5623]
[4]   Smartphone-based respiratory rate estimation using photoplethysmographic imaging and discrete wavelet transform [J].
Alafeef, Maha ;
Fraiwan, Mohammad .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (02) :693-703
[5]   EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques [J].
Alturki, Fahd A. ;
AlSharabi, Khalil ;
Abdurraqeeb, Akram M. ;
Aljalal, Majid .
SENSORS, 2020, 20 (09)
[6]   Optical fiber sensors for static and dynamic health monitoring of civil engineering infrastructures: Abode wall case study [J].
Antunes, Paulo ;
Lima, Hugo ;
Varum, Humberto ;
Andre, Paulo .
MEASUREMENT, 2012, 45 (07) :1695-1705
[7]   Damage detection of railway bridges using operational vibration data: theory and experimental verifications [J].
Azim, Md Riasat ;
Zhang, Haiyang ;
Gul, Mustafa .
STRUCTURAL MONITORING AND MAINTENANCE, 2020, 7 (02) :149-166
[8]   Structural health monitoring of harbor caissons using support vector machine and principal component analysis [J].
Bolourani, Anahita ;
Bitaraf, Maryam ;
Tak, Ala Nekouvaght .
STRUCTURES, 2021, 33 :4501-4513
[9]  
dos Santos JA., 2020, European workshop on structural health monitoring, DOI [10.1007/978-3-030-64908-176, DOI 10.1007/978-3-030-64908-176]
[10]   Crack identification in beams using wavelet analysis [J].
Douka, E ;
Loutridis, S ;
Trochidis, A .
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES, 2003, 40 (13-14) :3557-3569