Special Issues

Sensing Data Based Structural Health Monitoring in Engineering

Submission Deadline: 31 March 2025 View: 1116 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

    Dynamic Characteristic Testing of Wind Turbine Structure Based on Visual Monitoring Data Fusion

    Wenhai Zhao, Wanrun Li, Ximei Li, Shoutu Li, Yongfeng Du
    Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.057759
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies, as well as the inability to achieve precise full-field monitoring, this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion. Firstly, the Lucas-Kanade Tomasi (LKT) optical flow method and a multi-region of interest (ROI) monitoring structure are employed to track pixel displacements, which are subsequently subjected to band pass filtering and resampling operations. Secondly, the actual displacement time history is derived through double integration of the acquired acceleration data and… More >

  • Open Access

    ARTICLE

    Optimizing Computed Tomography Processing Parameters for Accurate Detection of Internal Defects in Reinforced Concrete

    Yueshun Chen, Yupeng Zhou, Cao Yin
    Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.057005
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract Computed tomography (CT) can inspect the internal structure of concrete with high resolution, but improving the accuracy of measurements remains a key challenge due to the reliance on complex image processing and significant manual intervention. This study aims to optimize CT scanning parameters to enhance the accuracy of measuring crack widths and rebar volumes in reinforced concrete. Nine sets of specimens, each with varying rebar diameters and concrete cover thicknesses, were scanned before and after corrosion using an Optima CT scanner, followed by three-dimensional reconstructions using Avizo software. The effects of threshold values and “Erosion” More >

  • Open Access

    ARTICLE

    A Damage Control Model for Reinforced Concrete Pier Columns Based on Pre-Damage Tests under Cyclic Reverse Loading

    Zhao-Jun Zhang, Jing-Shui Zhen, Bo-Cheng Li, De-Cheng Cai, Yang-Yang Du, Wen-Wei Wang
    Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.054671
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract To mitigate the challenges in managing the damage level of reinforced concrete (RC) pier columns subjected to cyclic reverse loading, this study conducted a series of cyclic reverse tests on RC pier columns. By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK (Ibarra Medina Krawinkler) recovery model, the energy dissipation capacity coefficient of the pier columns were able to be determined. Furthermore, utilizing the calibrated damage model parameters, the damage index for each specimen were calculated. Based on the obtained damage levels, three distinct More >

  • Open Access

    REVIEW

    Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

    Chao Zhang, Shang-Xi Lai, Hua-Ping Wang
    Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods… More >

  • Open Access

    ARTICLE

    Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

    Hao Xu, Jing Wang, Rubin Zhu, Alfred Strauss, Maosen Cao, Zhanjun Wu
    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393
    (This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
    Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >

  • 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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