Damage quantification in moment resisting frame using phase space reconstructed from independent component sources

Pamwani, Lavish; Shelke, Amit

doi:10.1002/stc.2438

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…In their experimental study, acceleration was collected under intact and various damage scenarios. They then employed the ICA and PST algorithms to evaluate modal parameters and identify damage [170].…”

Section: Model Updating-based Shm Methods At the Bolt Jointmentioning

confidence: 99%

A comprehensive review on health monitoring of joints in steel structures

Naresh,

Kumar,

Pal

et al. 2024

Smart Mater. Struct.

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Steel-framed structures find extensive application in civil engineering projects, including buildings, bridges, and towers, due to their dependable material characteristics, expeditious construction capabilities, and cost-efficiency. In such structures, beams and columns are interconnected through either welding or bolting methods. However, it's imperative to recognize that joints represent the critical areas susceptible to damage stemming from a variety of factors, both human-induced and environmental, in addition to the effects of aging. Over the past few decades, substantial attention has been dedicated to the field of Structural Health Monitoring (SHM) at the joints of steel structures. This study seeks to comprehensively evaluate various methods employed for structural health monitoring at the joints of steel structures, encompassing both bolted and welded connections. While there have been numerous prior review studies that focus on localized and vibration-based techniques for detecting damage at these joints, there is a conspicuous absence of research covering the amalgamation of localized and global approaches across diverse steel structure types. This review paper addresses this gap by offering a thorough examination, incorporating the most recent applications of SHM methodologies employed in research and practical contexts for joint damage detection. Furthermore, it serves as a valuable resource for professionals, engineers, and academics engaged in civil structure design, construction, and maintenance.

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Section: Model Updating-based Shm Methods At the Bolt Jointmentioning

confidence: 99%

A comprehensive review on health monitoring of joints in steel structures

Naresh,

Kumar,

Pal

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…The SHM methodologies are majorly classified as local and global damage identification methodologies. [27][28][29][30][31][32][33] The spectral domain SHM methodologies models identifies the changes in characteristics of structure globally but are inefficient in determining the characteristics changes at elemental level. 34 These techniques are categorized into two different approaches, that is, parametric and nonparametric approaches.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading

Rajeev

Pamwani

Ojha

et al. 2022

Structural Health Monitoring

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In the present work, a novel technique based on the combination of singular spectral analysis (SSA) and recursive estimate of coefficients of adaptive autoregressive (AR) modelling is employed to identify the damage in the reinforced (RC) beam-column joints. The damage is induced by imparting shock load at the tip of the beam-column joints. The damage is identified with the help of the acceleration response of the healthy and damaged specimens excited by high intensity white noise. The proposed approach has two major components, first, filtering and removing the noise from the dynamic response using the singular spectral analysis and second, modelling the filtered response using adaptive AR process to get the recursive estimate of coefficient matrix for baseline and damage states. The coefficients evaluated for each time instant are presented in a multi-dimensional subspace to form distinct clusters corresponding to a healthy and damaged state. In order to identify and quantify the damage, the geometrical and statistical measures are evaluated that quantifies the segregation of clusters. In total, three distinct measures are used to quantify the damage, namely, Euclidean distance (ED), Mahalanobis distance (MD) and Bhattacharyya distance (BD). The BD accounts the variation in the distribution of both the clusters, thereby shows superior results comparatively than ED and MD. The results of DSFs also manifest the superiority of BD over the other two DSFs. These geometrical and statistical distances are the damage sensitive feature (DSF) to identify and quantify the damage in the specimen due to the shock load. The obtained results of all the DSFs show good consistency with the maximum deformation of the specimen due to shock loading highlighting the accuracy of the proposed algorithm.

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Data driven structural damage assessment using phase space embedding and Koopman operator under stochastic excitations

Peng

Hao

2022

Engineering Structures

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Damage quantification in moment resisting frame using phase space reconstructed from independent component sources

Cited by 3 publications

References 65 publications

A comprehensive review on health monitoring of joints in steel structures

A comprehensive review on health monitoring of joints in steel structures

Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading

Data driven structural damage assessment using phase space embedding and Koopman operator under stochastic excitations

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