Chao Zhang¹, Shang-Xi Lai¹, Hua-Ping Wang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662 - 15 November 2024

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 >

Hao Xu¹, Jing Wang², Rubin Zhu², Alfred Strauss³, Maosen Cao⁴, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393 - 20 September 2024

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 >

Yue Cao^1,2, Longsheng Bao¹, Xiaowei Zhang^1,*, Zhanfei Wang¹, Bingqian Li¹

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698 - 05 June 2024

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

Chengpeng Zhang, Junfeng Shi^*, Caiping Huang

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705 - 05 June 2024

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 >

Shuai Chen¹, Yinwei Ma², Zhongshu Wang², Zongmei Xu³, Song Zhang¹, Jianle Li¹, Hao Xu¹, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594 - 22 March 2024

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

Xiaoyang Shen¹, Xiaojing Zhang^1,*

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 >

Wei Li^1,2, Benjian Zou¹, Yuxiang Luo², Ning Yang², Faye Zhang^1,*, Mingshun Jiang¹, Lei Jia¹

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

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 >

Chun Sha^1,*, Chaohui Yue², Wenchen Wang³

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 369-381, 2023, DOI:10.32604/sdhm.2023.027948 - 07 September 2023

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 >

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 - 05 January 2023

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 >

Mohammad Sadegh Barkhordari¹, Danial Jahed Armaghani^2,*, Panagiotis G. Asteris³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840 - 31 August 2022

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 >