Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (16)
  • Open Access


    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

    Yue Cao1,2, Longsheng Bao1, Xiaowei Zhang1,*, Zhanfei Wang1, Bingqian Li1

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698

    Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More > Graphic Abstract

    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

  • Open Access


    Identification of Damage in Steel‒Concrete Composite Beams Based on Wavelet Analysis and Deep Learning

    Chengpeng Zhang, Junfeng Shi*, Caiping Huang

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705

    Abstract In this paper, an intelligent damage detection approach is proposed for steel-concrete composite beams based on deep learning and wavelet analysis. To demonstrate the feasibility of this approach, first, following the guidelines provided by relevant standards, steel-concrete composite beams are designed, and six different damage incidents are established. Second, a steel ball is used for free-fall excitation on the surface of the steel-concrete composite beams and a low-temperature-sensitive quasi-distributed long-gauge fiber Bragg grating (FBG) strain sensor is used to obtain the strain signals of the steel-concrete composite beams with different damage types. To reduce the… More >

  • Open Access


    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

    Shuai Chen1, Yinwei Ma2, Zhongshu Wang2, Zongmei Xu3, Song Zhang1, Jianle Li1, Hao Xu1, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

    Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method More > Graphic Abstract

    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

  • Open Access


    Damage Identification Algorithm of Composite Structure Based on Displacement Field

    Xiaoyang Shen1, Xiaojing Zhang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.28, No.1, pp. 1-3, 2023, DOI:10.32604/icces.2023.010519

    Abstract 1 General Introduction
    Reliable structural health monitoring with high detection probability is very important [1]. Therefore, the method of finite element simulation was adopted. Based on the basic equation of material mechanics and stiffness degradation theory, to detect the damage of composite laminates, and further improves the intelligence of the detection process through the method of visual detection neural network.

    2 Theoretical derivation and simulation
    2.1 Equations for buckling
    In the stratified damage area, each layer bears the load independently, and the bearing capacity is determined by the stiffness there: the larger the axial stiffness, the stronger the bearing… More >

  • Open Access


    Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

    Wei Li1,2, Benjian Zou1, Yuxiang Luo2, Ning Yang2, Faye Zhang1,*, Mingshun Jiang1, Lei Jia1

    Structural Durability & Health Monitoring, Vol.17, No.6, pp. 485-500, 2023, DOI:10.32604/sdhm.2023.025989

    Abstract As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put… More >

  • Open Access


    Ensemble 1D DenseNet Damage Identification Method Based on Vibration Acceleration

    Chun Sha1,*, Chaohui Yue2, Wenchen Wang3

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 369-381, 2023, DOI:10.32604/sdhm.2023.027948

    Abstract Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration. By combining multiple 1D DenseNet submodels, a new ensemble learning method is proposed to improve identification accuracy. 1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ensemble learning are used to vote on the results of each submodel, and then vote centrally. More >

  • Open Access


    Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

    Yu Zhang, Zhihua Xiong*, Zhuoxi Liang, Jiachen She, Chicheng Ma

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 447-469, 2023, DOI:10.32604/cmes.2023.022699

    Abstract A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory,… More >

  • Open Access


    Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

    Mohammad Sadegh Barkhordari1, Danial Jahed Armaghani2,*, Panagiotis G. Asteris3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840

    Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of More >

  • Open Access


    Identification of Internal Damage in Circular Cylinders through Laser Scanning of Vibrating Surfaces

    Yisu Xi1, Binkai Shi2, Wei Xu1,3,*, Jing Ge4, Huaxin Zhu5, Dragoslav Sumarac6,7

    Structural Durability & Health Monitoring, Vol.16, No.2, pp. 163-177, 2022, DOI:10.32604/sdhm.2022.022082

    Abstract With the aid of non-contact measurements of vibrating surfaces through laser scanning, operating deflection shapes (ODSs) with high spatial resolutions can be used to graphically characterize damage in plane structures. Although numerous damage identification approaches relying on laser-measured ODSs have been developed for plate-type structures, they cannot be directly applied to circular cylinders due to the gap between equations of motions of plates and circular cylinders. To fill this gap, a novel approach is proposed in this study for damage identification of circular cylinders. Damage-induced discontinuities of the derivatives of ODSs can be used to More >

  • Open Access


    A Review of Structural Health Monitoring Techniques as Applied to Composite Structures

    Amafabia, Daerefa-a Mitsheal1, Montalvão, Diogo2, David-West, Opukuro1, Haritos, George1

    Structural Durability & Health Monitoring, Vol.11, No.2, pp. 91-147, 2017, DOI:10.3970/sdhm.2017.011.091

    Abstract Structural Health Monitoring (SHM) is the process of collecting, interpreting and analysing data from structures in order to determine its health status and the remaining life span. Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties. However, composite materials are prone to develop damage when subjected to low to medium impacts (i.e. 1-10 m/s and 11-30 m/s respectively). Hence, the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of… More >

Displaying 1-10 on page 1 of 16. Per Page