Impact Testing and Dynamic Behavior of Materials

Meyer, Lothar; Herzig, Norman; Pursche, Frank; Abdel‐Malek, S.

doi:10.1007/978-1-4419-9794-4_12

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…It can cause catastrophic damage to the structural and non-structural components that lead to the progressive collapse of the building. 1,2 This progressive collapse failure is initiated by the sudden loss of at least one load-carrying structural element that propagates further in a chain reaction for the collapse of the entire building. From the literature review, it was evident that the progressive collapse failure in the buildings due to the extreme events is a mostly catastrophic failure that is associated with the beam-column sub-assemblies.…”

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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Section: Introductionmentioning

confidence: 99%