Shockwave is a high pressure and short duration pulse that induce damage and lead to progressive collapse of the structure. The shock load excites high-frequency vibrational modes and causes failure due to large deformation in the structure. Shockwave experiments were conducted by imparting repetitive localized shock loads to create progressive damage states in the structure. Two-phase novel damage detection algorithm is proposed, that quantify and segregate perturbative damage from microscale damage. The first phase performs dimension reduction and damage state segregation using principal component analysis (PCA). In the second phase, the embedding dimension was reduced through empirical mode decomposition (EMD). The embedding parameters were derived using singular system analysis (SSA) and average mutual information function (AMIF). Based, on Takens theorem and embedding parameters, the response was represented in a multidimensional phase space trajectory (PST). The dissimilarity in the multidimensional PST was used to derive the damage sensitive features (DSFs). The DSFs namely: (i) change in phase space topology (CPST) and (ii) Mahalanobis distance between phase space topology (MDPST) are evaluated to quantify progressive damage states. The DSFs are able to quantify the occurrence, magnitude, and localization of progressive damage state in the structure. The proposed algorithm is robust and efficient to detect and quantify the evolution of damage state for extreme loading scenarios.

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage.

In this paper, the time-varying autoregressive (TVAR) model is integrated with the K-means—clustering technique to detect the damage in the steel moment-resisting frame. The damage is detected in the frame using nonstationary acceleration response of the structure excited using ambient white noise. The proposed technique identifies and quantifies the damage in the beam-to-column connection and column-to-column splice plate connection caused due to loosening of the connecting bolts. The algorithm models the nonstationary acceleration time history and evaluates the TVAR coefficients (TVARCs) for pristine and damage states. These coefficients are represented as a cluster in the TVARC subspace and segregated and classified using K-means—segmentation technique. The K-means—approach is adapted to simultaneously perform partition clustering and remove outliers. Eigenstructure evaluation of the segregated TVARC cluster is performed to detect the temporal damage. The topological and statistical parameters of the TVARC clusters are used to quantify the magnitude of the damage. The damage is quantified using the Mahalanobis distance (MD) and the Itakura distance (ID) serving as the statistical distance between the healthy and damage TVARC clusters. MD calculates a multidimensional statistical distance between two clusters using the covariance between the state vectors, whereas ID measures the dissimilarity of the autoregressive (AR) parameter between reference state and unknown states. These statistical distances are used as damage-sensitive feature (DSF) to detect and quantify the initiation and progression of the damage in the structure under ambient vibrations. The outcome of both the DSFs corroborate with the experimental investigation, thereby improving the robustness of the algorithm by avoiding false damage alarms.

In the present study, a damage detection algorithm is proposed based on the amalgamation of independent component analysis (ICA) and phase space topology (PST) reconstruction. An experimental investigation is conducted by inducing damage in steel moment resisting frame (SMRF) and exciting it with ambient white noise base excitation. The experimental study is conducted for a healthy state and multiple progressive damage states. The damage in SMRF is manifested progressively by loosening the bolts of the beam-to-column and column-to-column bolted connections. The algorithm consists of two segments: In the first segment, the modal responses are obtained from the acquired response with the help of ICA. The optimal embedding parameters for modal responses are evaluated with the help of average mutual information function and singular system analysis. The second segment deals with the reconstruction of PST with the help of modal responses and embedding parameters. Lastly, the damage sensitive features (DSFs) are evaluated to identify and quantify the induced damage. The DSFs evaluated in the present study are, namely, (a) change in phase space topology (CPST), (b) Mahalanobis distance between phase space topology (MDPST), and (c) Itakura distance (ID). The CPST and MDPST measure the dissimilarity between the PSTs of healthy and damage states, whereas ID measures the dissimilarity between the set of auto-regressive coefficients of healthy and damage states of SMRF. The DSFs are able to detect and quantify the commencement and evolution of the damage in the SMRF subjected to ambient base excitation.

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.