Basuraj Bhowmik

Bhargav Nath, Mehulkumar Lakhadive, Anshu Sharma et al.

Reliable modal identification from output-only vibration data remains a challenging problem under measurement noise, sparse sensing, and structural variability. These challenges intensify when global modal quantities and spatially distributed mode shapes must be estimated jointly from frequency-domain data. This work presents a physics-aware variational graph autoencoder, termed UResVGAE, for joint modal identification with uncertainty quantification from power spectral density (PSD) representations of truss structures. The framework represents each structure as a graph in which node attributes encode PSD and geometric information, while edges capture structural connectivity. A residual GraphSAGE-based encoder, attention-driven graph pooling, and a variational latent representation are combined to learn both graph-level and node-level modal information within a single, unified formulation. Natural frequencies and damping ratios are predicted through evidential regression, and full-field mode shapes are reconstructed through a dedicated node-level decoder that fuses global latent information with local graph features. Physical consistency is promoted via mode-shape reconstruction and orthogonality regularisation. The framework is assessed on numerically generated truss populations under varying signal-to-noise ratios and sensor availability. Results demonstrate accurate prediction of natural frequencies, damping ratios, and mode shapes, with high modal assurance criterion values and stable performance under noisy and sparse sensing conditions. Reliability analysis indicates that the predictive uncertainty is broadly consistent with empirical coverage. The proposed framework offers a coherent and physically grounded graph-based route for joint modal identification with calibrated uncertainty from frequency-domain structural response data.

Ishan Pathak, Ishan Jha, Aditya Sadana et al.

Structural Health Monitoring (SHM) is vital for evaluating structural condition, aiming to detect damage through sensor data analysis. It aligns with predictive maintenance in modern industry, minimizing downtime and costs by addressing potential structural issues. Various machine learning techniques have been used to extract valuable information from vibration data, often relying on prior structural knowledge. This research introduces an innovative approach to structural damage detection, utilizing a new Convolutional Neural Network (CNN) algorithm. In order to extract deep spatial features from time series data, CNNs are taught to recognize long-term temporal connections. This methodology combines spatial and temporal features, enhancing discrimination capabilities when compared to methods solely reliant on deep spatial features. Time series data are divided into two categories using the proposed neural network: undamaged and damaged. To validate its efficacy, the method's accuracy was tested using a benchmark dataset derived from a three-floor structure at Los Alamos National Laboratory (LANL). The outcomes show that the new CNN algorithm is very accurate in spotting structural degradation in the examined structure.