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

Dubey, Anurag; Denis, Vivien; Serra, Roger

doi:10.3390/vibration5010003

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…Dubey et al [48] provided a persistent review on the latest methodologies in vibration-based SHM. They have highlighted the use of a frequency shift coefficient (FSC) algorithm referred from the works of Kim & Stubbs [49] for precise damage detection , localization, and quantification in structures.…”

Section: Emerging Trends and Future Directionsmentioning

confidence: 99%

A Review on the Latest Advancements and Innovation Trends in Vibration-Based Structural Health Monitoring (SHM) Techniques for Improved Maintenance of Steel Slit Damper (SSD)

Payawal,

Kim

2024

IEEE Access

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Steel slit damper (SSD) is specifically engineered to absorb and disperse energy to mitigate the possibility of structural damage and collapse during seismic events. However, the effectiveness of these dampers are compromised due to wear, corrosion and other forms of material degradation and failure mechanisms. For this reason, it is important to consistently monitor its condition and conduct maintenance and repairs to guarantee safety and dependability of the structures it is installed with. Now, vibration-based techniques have emerged as a promising structural health monitoring (SHM) strategy. It involves methodologies for detecting changes in functions and analyzing vibrations to discover potential problems. This study entails a comprehensive examination of the most recent advancements and breakthroughs in vibration-based methodologies used in SHM. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), we identified several key techniques and technologies that show promise in enhancing the maintenance and monitoring of steel -or steel and metal surfaces-slit dampers. The pros and cons of these methods are presented to be applicable when monitoring an SSD. This paper presents notable figures and their corresponding discussions to achieve enhanced understanding of the advancements in SHM towards sensor technologies, monitoring techniques, and applications in civil infrastructure. Furthermore, this paper reviews the applications and functions framework for real-time SHM of SSDs together with their practical implementations and applications using diverse monitoring technologies to promote innovation. Finally, the optimal sensor selection criteria, data gathering methodologies, experimental settings, innovative approaches with comparative analyses, and a proposed maintenance standard for SSDs are described in this study. INDEX TERMSSteel slit damper, structural health monitoring, vibration-based techniques, PRISMA 2020.

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Section: Emerging Trends and Future Directionsmentioning

confidence: 99%

A Review on the Latest Advancements and Innovation Trends in Vibration-Based Structural Health Monitoring (SHM) Techniques for Improved Maintenance of Steel Slit Damper (SSD)

Payawal,

Kim

2024

IEEE Access

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“…Modal parameters are fundamental properties of a structure that reflect its structural condition [2]. When a structure experiences damage, these parameters may shift [7] because the stiffness decreases. Identifying such changes promptly and precisely is crucial for maintaining the system's structural integrity.…”

Section: Introductionmentioning

confidence: 99%

Numerical Verification of the Drive-By Monitoring Method for Identifying Vehicle and Bridge Mechanical Parameters

2023

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The PRE (numerical simulation-based vehicle and bridge parameter and road roughness estimation) method uses vehicle vibration data to identify the vehicle’s and bridge’s mechanical parameters and estimate road unevenness simultaneously. This method randomly assumes the mechanical parameters first. Secondly, it solves the vehicle’s IEP (input estimation problem) and the bridge’s DRS (dynamic response simulation) from the vehicle vibration data to obtain road profiles of the front and rear wheels. Repeat the random assumption of the mechanical parameters to minimize the residual between the obtained road unevenness because the road unevenness of the front and rear wheels are expected to match. To search for a better combination of the mechanical parameters, the MCMC (Monte Carlo Markov chain) algorithm is adopted in this paper. This paper also numerically simulates vehicle vibration data for the cases of the reduced-stiffness bridge model and examines whether this method can identify the position, range, and magnitude of stiffness reduction. The numerical simulation results show that bridge-stiffness reduction can be estimated reasonably.

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“…They utilized the vibration responses to investigate the damaged shell structure [ 12 ]. Dubey et al used an algorithm based on the frequency shift coefficient to estimate both the damage severity and position [ 13 ]. Their modal parameter calculation is based on solving the eigen values obtained from a finite element model.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages

Saha,

Yang

2023

Sensors

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Natural frequency is an important parameter in the structural health monitoring (SHM) system. Any changes in this parameter indicate structural alteration due to damage. This study provides a neural network (NN) solution as an alternative to the finite element (FE) method to measure the natural frequencies of a cantilever beam with random multiple damage. It is based on a statistical dataset of a free vibration test obtained from the APDL (Ansys parametric design language) simulation using a MATLAB (matrix laboratory) script. The script can generate an unlimited number of possible damage combinations for any given parameters with the help of the Monte Carlo (MC) technique. MC helps to generate a random number of damages in random locations at each simulation. Damage conditions are controlled by three parameters including damage severity and damage size (in terms of the mean and standard deviation of damage). Moreover, the method proposes a curve-fitting equation to validate the predicted natural frequency for the first three modes obtained from the neural network model. Both methods are in good agreement with each other, having minimal errors in the range of 0.2–3% for each mode. The frequency result shows that the beam frequency is 8.6486 Hz if the area reduction is 10%, whereas it comes down to 7.2338 Hz if there is a 30% area reduction. A two-level factorial test shows that damage severity is the most impactful factor compared to the damage sizes on the frequency shift event. This indicates that damage alters the composition of the beam and has an impact on its frequency change with the assumed damage parameters. Therefore, the proposed NN model can estimate the frequency shift for various damage scenarios. It can be utilized in the vibration-based damage identification process to predict the frequency changes of the damaged beam without any computational burden.

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

Cited by 7 publications

References 40 publications

A Review on the Latest Advancements and Innovation Trends in Vibration-Based Structural Health Monitoring (SHM) Techniques for Improved Maintenance of Steel Slit Damper (SSD)

A Review on the Latest Advancements and Innovation Trends in Vibration-Based Structural Health Monitoring (SHM) Techniques for Improved Maintenance of Steel Slit Damper (SSD)

Numerical Verification of the Drive-By Monitoring Method for Identifying Vehicle and Bridge Mechanical Parameters

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages

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