Damage detection in multi-span beams based on the analysis of frequency changes

Gillich, Gilbert-Rainer; Ntakpe, Jean Loius; Wahab, Magd Abdel; Praisach, Zeno-Iosif; Mimis, M C

doi:10.1088/1742-6596/842/1/012033

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…To generate the training data, for several scenarios of the damaged beam, the relative frequency shift (RFS) values are used, according to Equation (4) [12,13]:…”

Section: Training Datasetmentioning

confidence: 99%

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

David

Tufiși

Gillich

et al. 2022

Anal. Tech. Szeged.

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Because our infrastructure is aging and approaching the end of its intended functioning time, the detection of damage or loosening of joints is a topic of high importance in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods that can offer a global evaluation of the structure’s integrity. A comparison of using different modal data for training feedforward backpropagation neural networks for detecting transverse damages in beam-like structures that can also be affected by imperfect boundary conditions is presented in the current paper. The different RFS, RFSmin, and DLC training datasets are generated by applying an analytical method, previously developed by our research team, that uses a known relation, based on the modal curvature, severity estimation of the transverse crack, and the estimated severity for the weak clamping. The obtained dataset values are employed for training three feedforward backpropagation neural networks that will be used to locate transverse cracks in cantilever beams and detect if the structure is affected by weak clamping. The output from the three ANN models is compared by plotting the calculated error for each case.

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“…To generate the training data, for several scenarios of the damaged beam, the relative frequency shift (RFS) values are used, according to Equation (4) [12,13]:…”

Section: Training Datasetmentioning

confidence: 99%

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

David

Tufiși

Gillich

et al. 2022

Anal. Tech. Szeged.

View full text Add to dashboard Cite

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“…Many researchers have deployed frequency approaches on simple structures like beams or plates (84)(85)(86)(87)(88). For instance, Kim et al (89) compared frequency-based damage detection (FBDD) vs. mode-shape-based damage detection (MBDD) on a simple beam and two sets of modal parameters.…”

Section: Natural Frequenciesmentioning

confidence: 99%

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Gharehbaghi

Farsangi

Noori

et al. 2021

Arch Computat Methods Eng

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The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several fields, such as aerospace, automotive, and mechanical engineering. However, the focus of this paper is to provide a comprehensive upto-date review of civil engineering structures such as buildings, bridges, and other infrastructures. For this reason, this article commences with a concise introduction to the fundamental definitions of SHM. The next section presents the general concepts and factors that determine the best strategy to be employed for SHM. Afterward, a thorough review of the most prevalent anomaly detection approaches, from classic techniques to advanced methods, is presented. Subsequently, some popular benchmarks, including laboratory specimens and real structures for validating the proposed methodologies, are demonstrated and discussed. Finally, the advantages and disadvantages of each method are summarized, which can be helpful in future studies.

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“…The development of a damage detection method by Zhang et al [24] based on the frequency shift curve caused by auxiliary mass with both the natural frequency and mode shape information for cylindrical shell structures. Gillich et al [25] performed crack identification based on natural frequency change. They established a mathematical model and signal processing algorithm, which can predict frequency changes for any boundary conditions with the identification of cracks on multi-span beams.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

2022

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Health surveillance in industries is an important prospect to ensure safety and prevent sudden collapses. Vibration Based Structure Health Monitoring (VBSHM) is being used continuously for structures and machine diagnostics in industry. Changes in natural frequencies are frequently used as an input parameter for VBSHM. In this paper, the Frequency Shift Coefficient (FSC) is used for the assessment of various numerical damaged cases. An FSC-based algorithm is employed in order to estimate the positions and severity of damages using only the natural frequencies of healthy and unknown (damaged) structures. The study focuses on cantilever beams. By considering the minimization of FSC, damage positions and severity are obtained. Artificially damaged cases are assessed by changes in its positions, the number of damages and the size of damages along with the various parts of the cantilever beam. The study is further investigated by considering the effect of uncertainty on natural frequencies (0.1%, 0.2% and 0.3%) in damaged cases, and the algorithm is used to estimate the position and severity of the damage. The outcomes and efficiency of the proposed FSC based method are evaluated in order to locate and quantify damages. The efficiency of the algorithm is demonstrated by locating and quantifying double damages in a real cantilever steel beam using vibration measurements.

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Damage detection in multi-span beams based on the analysis of frequency changes

Cited by 12 publications

References 15 publications

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

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