Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network

Nick, Hooman; Ashrafpoor, Asgar; Aziminejad, Armin

doi:10.1016/j.istruc.2023.03.152

Cited by 18 publications

(5 citation statements)

References 56 publications

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“…The efficacy of their technique is underscored by an impressive damage detection accuracy rate of 96.62%. Nick et al [64] introduced a novel two-stage damage detection approach tailored for steel frameworks, employing ANNs. This method accentuates the utilization of adjusted damage indices, derived from modal flexibility and strain energy to first pinpoint the locations of damage.…”

Section: Dd Using Artificial Neural Network (Anns)mentioning

confidence: 99%

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Rabi,

Vailati,

Monti

2024

Buildings

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Bridges are essential to infrastructure and transportation networks, but face challenges from heavier traffic, higher speeds, and modifications like busway integration, leading to potential overloading and costly maintenance. Structural Health Monitoring (SHM) plays a crucial role in assessing bridge conditions and predicting failures to maintain structural integrity. Vibration-based condition monitoring employs non-destructive, in situ sensing and analysis of system dynamics across time, frequency, or modal domains. This method detects changes indicative of damage or deterioration, offering a proactive approach to maintenance in civil engineering. Such monitoring systems hold promise for optimizing the management and upkeep of modern infrastructure, potentially reducing operational costs. This paper aims to assist newcomers, practitioners, and researchers in navigating various methodologies for damage identification using sensor data from real structures. It offers a comprehensive review of prevalent anomaly detection approaches, spanning from traditional techniques to cutting-edge methods. Additionally, it addresses challenges inherent in Vibration-Based Damage (VBD) SHM applications, including establishing damage thresholds, corrosion detection, and sensor drift.

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Section: Dd Using Artificial Neural Network (Anns)mentioning

confidence: 99%

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Rabi,

Vailati,

Monti

2024

Buildings

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“…Theoretically, the proposed method can accurately calculate the structural damage parameters only by using the first-order eigen-parameters of structural free vibration. From Equations ( 24) and (25), one can find that the system matrices K and M of the undamaged FEM are used in the computation of the frequency-shift flexibility sensitivity. However, Equation ( 25) can be approximated by using Equation (20) with a few lower-frequency vibration modes.…”

Section: Theoretical Developmentmentioning

confidence: 99%

“…The damage index proposed by them is particularly sensitive to the damage location and can be successfully applied to the steel truss bridge with different damage patterns. Nick et al [25] proposed a damage index based on modal flexibility and modal strain energy, and a two-stage multi-criteria damage detection method using an artificial neural network (ANN) to locate and quantify the damage of steel frames. The modal flexibility matrix was obtained by the first three bending vibration modes.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Using the First-Order Vibration-Mode-Based Frequency-Shift Flexibility Sensitivity Algorithm

Cao

Yang

Peng

2023

Axioms

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The diagnosis of structural damage usually belongs to a mathematical inverse problem. This work presents a novel frequency-shift flexibility sensitivity algorithm for structural damage assessment using only the first-order vibration mode to achieve the goal of successfully identifying structural damage with fewer modal parameters. The core idea of the proposed method is to make the first-order vibration mode contribute the most to a structural flexibility matrix through the frequency-shift operation. A high-precision flexibility matrix can be obtained after the frequency-shift operation, which only needs the first mode of structural free vibration. Through this special advantage, structural damage coefficients can be accurately calculated by the frequency-shift flexibility sensitivity equation. Thus, a reliable identification result can be obtained according to the values of the calculated damage coefficients. In some engineering applications, another advantage of the proposed method is that it does not require a complete finite element modeling process, as long as a few lower-frequency vibration modes of the intact structure are measured. A truss structure and a beam structure are used as two numerical examples to demonstrate the proposed approach. The results show that the proposed method has higher calculation accuracy than the ordinary flexibility sensitivity method by using only the first-order vibration mode. The proposed method can overcome possible misdiagnosis of the ordinary flexibility sensitivity method. It also has been shown that the proposed method may have the potential to identify minor damage in a structure. Using the experimental data of a steel frame structure, the effectiveness and reliability of the proposed method have been further verified. The proposed method provides a simple way for structural damage identification with only a few vibration modal data.

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“…The damage indices are computed using the mode shape components and their resultants to improve the precision and effectiveness of damage identification. The MSEDI-based ANNs predicted the damage magnitudes of the single and multiple damage scenarios with errors of up to 0.95% and 1.12%, respectively (Nick et al, 2023). Steel column bucking resistance was examined using finite element analysis and validated using an artificial neural network.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology

S.A.,

Nambiappan

2023

Preprint

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This paper focused on the finite element analysis of structural system in extreme loading condition. Two different stud shape and thicknesses were analyzed under blast. The stud thickness such as 1.19 mm and 1.5 mm were modelled and analyzed using ABAQUS 6.14. FEM is a tool which predicts the engineering physics of the real structure. To validate the finite element modelling performed by authors, a reference work published by earlier researchers on cold-formed steel stud wall is considered and examined in the present study. The novelty of this study was web corrugation and influence of flange width on stud. The models mimic like an air bag in a car to delay the pressure timing inside the stud wall. The mass of explosive used as 1.56 kg at a standard scaled distance. Time versus displacement was captured out at four locations in the stud wall. One of the objectives is to develop mathematical model to validate the deformation of stud under blast loading. Two mathematical models were validated using Artificial Neural Network (ANN). The results captured in ANN model was error histogram, regression plot, best performance fit and training data. The models were capable of resisting the moderate blast load. The response surface methodology (RSM) was employed to evaluate model performance Regression equations are useful for predicting future trends and outcomes, which is crucial for planning and decision-making. The primary goal of this work is to evaluate cold-formed steel stud walls with varying stud sizes under blast loading using finite element analysis and validated by ANN and RSM.

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Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network

Cited by 18 publications

References 56 publications

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Structural Damage Identification Using the First-Order Vibration-Mode-Based Frequency-Shift Flexibility Sensitivity Algorithm

Finite Element Analysis of Cold-Formed Steel Stud Wall Subjected to Blast Loading and Validation Using Artificial Neural Network and Response Surface Methodology

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