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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System identification is primarily studied for unidirectional excitation using the Bouc-Wen model, neglecting the torsional coupling, even though real structure experiences multidirectional seismic excitation. Moreover, the high damping rubber bearings exhibit bidirectional effects, thereby requiring coupled biaxial Bouc-Wen (BBW) model and demand the estimation of model parameters for structural health monitoring. The current work presents three numerical case studies followed by experimental validation to demonstrate the applicability and efficacy of Bayesian filters named constraint unscented Kalman filter (CUKF) in identifying model parameters for the nondeteriorating system as well as deteriorating systems. With limited measurements and increased states, a two-stage framework of the CUKF is used to enhance the performance in identifying the hysteresis parameters and system dynamics of the nondeteriorating systems. For the deteriorating system, the Paris-Erdogan law is coupled with the stiffness in the BBW model to introduce degradation as per the acceleration fatigue crack growth. The degradation parameters and deteriorating stiffness is captured through CUKF accurately.The application of CUKF to the experimental responses proves the robustness of the algorithm for coupled biaxial hysteresis system. Additionally, a unified structural health monitoring (SHM) framework is proposed for condition monitoring during extreme events and long-term periodic maintenance through ambient vibrations. Overall, the result concludes that CUKF is a reliable Bayesian estimator for coupled biaxial hysteresis systems and demonstrates promising potential in identifying fatigue-induced deterioration.

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