In this study, a physics-informed Cycle-Consistent Generative Adversarial Network (CycleGAN) framework is proposed for vibration-based structural health monitoring of structures subjected to lateral impacts. The proposed method aims to translate vibration data between the healthy and damaged state domains. Within the CycleGAN, a Graph Neural Network block is employed to model the spatial topology of the sensor network: a weighted graph is constructed according to the physical distances between sensors using a Gaussian radial basis function. This enables the network to capture correlations and structural response propagation characteristics among the multi-channel sensor data. A Transformer block is also incorporated to model long-time sequence data, enhancing the ability to capture vibration decay patterns without altering the adversarial–cyclic training structure. During training, in addition to the adversarial, identity, and cycle-consistency losses, a multi-resolution Short-Time Fourier Transform spectral loss and an adaptive frequency-band regularization loss are integrated within the domain adaptation framework. These physics-based loss functions enforce coherence in both the time and frequency domains between generated and real structural responses. Results obtained for concrete-filled steel tubular (CFST) structures, based on seven-channel acceleration recordings sampled at 25 kHz, demonstrate that the proposed framework effectively enhances cross-domain stability in healthy–damage signal translation and suppresses abnormal frequency peaks.

Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point, allowing us to capture the dynamic behavior of these structures under their normal operating conditions. Our ongoing work involves a large-scale AVM campaign across Venice, using synchronized velocimeters to systematically gather vibration data. From this, we extract key dynamic parameters like dominant frequencies, mode shapes and damping ratios, alongside essential geometric features. Simultaneously, careful visual inspections provide the critical qualitative context regarding each bridge's state of conservation. By integrating these distinct quantitative and qualitative data streams, we have been developing a unique baseline archive crucial for moving beyond characterization towards meaningful interpretation.

The central aim of this paper is to explore how this integrated dataset can be leveraged to address the significant challenge of automated condition assessment. We employ a supervised machine learning framework to bridge the gap between measured data and assessed structural condition. The curated archive, which links the dynamic and geometric measurements (our inputs) to discrete condition categories derived from the visual inspections (our outputs), provides the training ground for established classification algorithms – specifically Support Vector Machines (SVM) and Random Forest (RF). Standard pre-processing techniques, including feature scaling, are incorporated to ensure the classifiers can effectively learn from the data without scale-induced biases.

Initial explorations with this framework have already yielded valuable insights. For instance, feature importance analysis of the Random Forest model, strongly suggests that the dynamic parameters captured via AVM—particularly damping ratio and dominant frequency—offer considerably more diagnostic information about condition than the simpler geometric descriptors alone. This finding reinforces the value embedded in vibration monitoring. Moreover, our work confirms the feasibility of training these models to distinguish between different condition states using only the combined instrumental and geometric data. Importantly, from an engineering perspective, we recognize that raw classification accuracy is insufficient; ensuring high sensitivity (Recall) in identifying potentially compromised structures is critical for responsible heritage management and risk mitigation.

This combined AVM-ML methodology provides a scalable foundation for developing more objective, data-supported tools to assist in condition assessment and guide proactive maintenance efforts for vital cultural infrastructure. Further research will necessarily involve expanding the dataset and undertaking rigorous validation of the models.

Modal-based structural health monitoring requires both reliable long-term extraction of modal features and statistical models capable of separating normal environmental and operational variability from structurally induced change. This paper presents a fully automated framework that combines frequency-domain-decomposition-based mode identification and tracking with a latent-factor state-space model with exogenous inputs (SSM-X) for innovation-based novelty detection. Tracked natural frequencies are treated as the observation vector, while measured environmental and operational variables are incorporated as exogenous inputs. The healthy evolution of the modal features is represented through a low-rank linear-Gaussian dynamical system, estimated from baseline data by expectation-maximization using Kalman filtering and Rauch–Tung–Striebel smoothing. Novelty is quantified through the one-step-ahead normalized innovation squared, yielding a probabilistically normalized multivariate damage indicator that naturally accommodates missing observations. The framework is demonstrated on two months of continuous monitoring data from an aluminium arch pedestrian bridge. Results show stable predictive performance on unseen healthy data and clear sensitivity to simulated novelty scenarios. In particular, the low-rank multivariate formulation exploits cross-modal structure to detect departures that may remain weak at the single-mode level, while preserving robustness to incomplete and irregular modal time series.

Strain and displacement measurement is a fundamental task in structural health monitoring. Traditionally, Digital image correlation (DIC) is used for image-based deformation measurement by comparing corresponding local regions in two images to determine the surface deformation field. However, DIC requires the surface to be coated with speckles to create unique texture features to achieve matching. This requirement significantly limits the application of DIC technology, as it is often impractical to apply speckle patterns on a large scale. Recognizing that natural material surfaces often possess natural texture features, we propose a speckle-free approach to deformation field estimation by leveraging deep learning techniques to extract these natural textures. We frame this challenge as a generalized dense correspondence matching problem, encompassing two specific tasks: optical flow estimation and disparity estimation. To address this, we introduce a unified cost volume transformer designed to extract 4D cost volume information, facilitating the aggregation of long-range dependencies. Additionally, we employ a compact latent space strategy to compress the cost volume dimensions, thereby minimizing memory costs. Furthermore, we implement a masked fine-tuning strategy to adapt the pre-trained model to various texture characteristics in the absence of ground truth reference outputs. Finally, we construct a binocular measurement system using two GoPro cameras. The effectiveness of our proposed method is validated through experiments conducted with different materials.