Special Issues

Sensing Data Based Structural Health Monitoring in Engineering

Submission Deadline: 31 August 2024 View: 141 Submit to Special Issue

Guest Editors

Hua-Ping Wang, Lanzhou University, China
E-mail: wanghuaping1128@sina.cn

Jose Campos e Matos, University of Minho, Portugal
E-mail: jmatos@civil.uminho.pt

Summary

Since most important structures have suffered from micro defects after working for a few years, the health monitoring and condition assessment of these established structures is particularly significant. Smart sensing technology by using different kinds of sensors (i.e., optical fiber sensor, piezoelectric sensor, strain gauge), digital image techniques and remote radar has been performed to monitor the real-time deformation, vibration and damage of the structures. Therefore, a great number of sensing data has been collected, and how to interpret the big data and accurately reflect the physical status of the monitored structures has become an important issue. Static and dynamic structural theories have been adopted to deal with the processing of the sensing data. Artificial intelligence (AI) method combined with the signal processing technique has also been used to recognize the health and damage condition of the structures. Cloud computing technique has also been aided to perform the real-time health monitoring of structures.

 

Thus, the research topic aims to cover original or review articles exploring the innovation in sensing data based structural health monitoring (SHM). The special issue intends to include, but not limited to:

• Smart sensors and structures

• Sensing data based SHM

• Static and dynamic analysis based on sensing data

• Health and damage condition assessment

• Structural parametric reflection based on monitoring technique

• Sensing data motivated model updating and feature recognition

• Big data analysis

• Artificial intelligence-based feature recognition

• Real-time health monitoring based on cloud computing technique


Keywords

Sensing data, Structural health monitoring, Static and dynamic response analysis, Damage identification, Condition assessment

Published Papers


  • Open Access

    ARTICLE

    Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

    Guangfei Jia, Yanchao Meng, Zhiying Qin
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More >

    Graphic Abstract

    Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

  • Open Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    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

    ARTICLE

    A Comprehensive Investigation on Shear Performance of Improved Perfobond Connector

    Caiping Huang, Zihan Huang, Wenfeng You
    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 299-320, 2024, DOI:10.32604/sdhm.2024.047850
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract This paper presents an easily installed improved perfobond connector (PBL) designed to reduce the shear concentration of PBL. The improvement of PBL lies in changing the straight penetrating rebar to the Z-type penetrating rebar. To study the shear performance of improved PBL, two PBL test specimens which contain straight penetrating rebar and six improved PBL test specimens which contain Z-type penetrating rebars were designed and fabricated, and push-out tests of these eight test specimens were carried out to investigate and compare the shear behavior of PBL. Additionally, Finite Element Analysis (FEA) models of the PBL… More >

  • Open Access

    ARTICLE

    Damage Diagnosis of Bleacher Based on an Enhanced Convolutional Neural Network with Training Interference

    Chaozhi Cai, Xiaoyu Guo, Yingfang Xue, Jianhua Ren
    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 321-339, 2024, DOI:10.32604/sdhm.2024.045831
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operation poses a significant threat to the safety of both life and property. Consequently, it becomes imperative to conduct damage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types of potential damage, and the presence of similar vibration data in adjacent locations make it challenging to achieve satisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmental noise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and anti-noise capabilities… More >

    Graphic Abstract

    Damage Diagnosis of Bleacher Based on an Enhanced Convolutional Neural Network with Training Interference

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