Alireza Entezami

Climate-induced freezing events can significantly alter the dynamic behaviour of bridge structures, leading to abrupt shifts in modal frequencies that may obscure true structural conditions or mimic damage scenarios. The negative consequence of these shifts is the emergence of false alarms in structural health monitoring (SHM) of these structures, resulting in misinterpretation of healthy states as damage and unnecessary maintenance interventions. To address these challenges, this study presents an unsupervised anomaly detection approach for identifying freezing-induced anomalies in long-term bridge modal frequency data without relying on temperature records. The proposed anomaly detector first learns the normal dynamic behaviour of the monitored bridge under non-freezing conditions. When climate–induced freezing events occur, causing sudden increases in modal frequencies, the trained anomaly detector automatically identifies and flags these events, wherein the freezing-induced anomaly scores exceed a predefined decision threshold. The detected anomalies are subsequently cross-validated against environmental data to confirm their correspondence with actual freezing periods. Modal frequency datasets from two bridge structures subjected to freezing conditions are employed to validate the proposed approach. Results indicate that the unsupervised detection of climate-induced freezing events can effectively reduce false alarms in SHM of bridges without requiring labelled data for decision-making.

Dam displacement monitoring is imperative to assess the operational status and structural safety of dams under various environmental conditions and operational loads. Although most of the dam structures are instrumented with robust in-situ sensing systems, long-term field monitoring is often constrained by practical challenges such as instrumentation costs, sensor drift or malfunction, data gaps, and the inherent complexity of operating and maintaining large-scale monitoring networks. Machine learning offers an alternative solution to deal with these challenges by enabling data-driven prediction and interpretation of structural responses. Despite numerous regression-based predictive models for predicting dam structural responses, a demanding issue arises from incomplete training data stemming from the unavailability of some influential environmental and operational factors. This study aims to address the aforementioned engineering and technical limitations by proposing an intelligent hybrid regressor. This model integrates ridge regression with a convolutional neural network (CNN), leveraging the strengths of both statistical learning and deep learning paradigms, centralized on a residual correction mechanism. First, the ridge regression model performs initial dam displacement predictions using the available environmental and operational factors. Second, prediction errors (residuals) of the ridge regression model are fed into the CNN to capture hidden nonlinear relationships embedded in the residuals and subsequently enhance overall prediction accuracy. Given this sequential dual-stage prediction architecture, the proposed hybrid regressor can address the limitation of unavailable environmental and operational factors that significantly influence dam deformation behaviour. A real-world dam structure, along with limited data including reservoir levels and temperature, is employed to validate the proposed predictive method. Results show that the proposed hybrid regressor achieves a prediction accuracy of approximately 89% using incomplete data while effectively capturing strong nonlinear characteristics in dam displacement responses.

Ensuring the long-term integrity and health of bridge structures under diverse structural, environmental, and operational conditions remains a persistent challenge within the structural health monitoring (SHM) community. Although machine learning–aided unsupervised anomaly detection offers promising solutions for vibration-based SHM, its generalization and robustness across different bridge types and structural conditions are still limited. A model trained for one bridge often fails to perform effectively on another with distinct material properties, geometries, or boundary conditions. To overcome this limitation, this study introduces a novel hybridized unsupervised learning (HUL) framework for detecting structural changes in heterogeneous concrete bridge systems. The proposed framework employs daily measurement of modal frequencies as the primary dynamic features for vibration-based change detection, performing two essential tasks including data normalization by synergetic integration of Bidirectional Encoder Representation from Transformer (BERT) with statistical regularization of a Generative Adversarial Network (GAN), to suppress environmentally/operationally-induced outliers. At the last stage of HUL the change detection is conducted via applying a statistical anomaly detector on obtained residuals to identify genuine structural changes. Using a masked training strategy within the GAN structure, the generator utilizes a self-attention mechanism to reconstruct input sequences of modal frequencies where a specific portion of time steps are stochastically masked. By minimizing a dual-loss objective combining reconstruction error with adversarial loss, the model learns underlying temporal correlations of the healthy bridge state without requiring labelled damage data. Three concrete bridges, including one numerical and two experimental structures, with distinct configurations are used to validate the proposed method. Moreover, structural variations are categorized into four representative classes: detrimental (damage), innocuous (normal environmental or operational conditions), extreme (severe environmental and operational conditions), and mixed changes. Results indicate that the HUL framework can effectively detect true structural changes while mitigating false alarms arising from environmental and operational variability, thus providing a generalizable and robust approach for concrete bridge monitoring.

Climate change has become a critical challenge for maintenance and functionality of civil structures. Apart from global warming, climate change-induced frost periods can seriously affect dynamic behaviour of bridges. From a meteorological perspective, instability in the polar vortex and weakening of the jet stream are critical climatic phenomena that allow cold Arctic air to move toward mid-latitudes, leading to unexpected and severe freezing events. Under such circumstances, vibration features of bridges, especially modal frequencies, alter significantly. Generally, freezing temperatures cause sharp shifts in bridge modal frequencies due to sudden stiffening, particularly deck asphalts. Because these impacts can obscure true structural changes, produce false damage alarms, and reduce the reliability of long-term structural health monitoring (SHM), this paper proposes machine learning-aided data normalizers to mitigate climate change-induced frost conditions on modal frequencies of bridges during their SHM programs. Given the availability of temperature records, supervised data normalizers in terms of classifiers are trained by using both the modal frequencies and temperatures. In contrast, when temperature records are unavailable, unsupervised data normalizers in terms of reconstruction-based models are developed by the only bridge modal frequencies. Both types of data normalizers extract residuals between the measured and reconstructed modal frequencies, serving as normalized dynamic features free from frost effects. The proposed hybrid machine learning framework is validated using long-term monitoring data from concrete and steel bridges subjected to freezing conditions. Results show that this framework effectively eliminates frost-induced frequency jumps, enhancing the stability, accuracy, and climate resilience of long-term SHM programs.

Long-span cable-supported bridges are highly susceptible to windstorm-induced excitations due to their flexible structures, low damping ratios, and large aerodynamic surfaces. Extreme wind events can trigger excessive vibrations, loss of serviceability, and even structural instability, posing serious risks to both safety and traffic operations. Timely bridge closure during severe windstorms is therefore critical to prevent accidents and ensure operational resilience. However, manual decision-making may suffer from delays and inaccuracies under rapidly evolving windstorm conditions. To address this challenge, this study proposes an automated AI-aided alert framework for bridge closure during windstorms based on the concept of unsupervised anomaly detection. Building upon this, an unsupervised anomaly detector is trained by solely using acceleration time histories recorded under normal wind conditions. In this case, the developed anomaly detector can autonomously learn the baseline dynamic behaviour of the bridge being monitored. When windstorm-induced vibrations occur, the trained anomaly detector identifies deviations from the learned normal patterns in real time based on a decision threshold and triggers automated closure alerts. The proposed method is validated using measured acceleration responses measured from a long-span cable-stayed bridge under both normal and windstorm conditions. Results demonstrate that the proposed model can effectively detects windstorm-induced anomalies with high sensitivity and low false-alarm rates, enabling timely and reliable bridge closure decisions.

Bridges, as critical components of transportation networks, demand reliable structural health monitoring (SHM) programs that enable quantitative assessment of their structural states and long-term performance under varying environmental and loading conditions. However, in many practical cases, vibration-based SHM projects, while effective and efficient, are short-term due to budget, accessibility, or operational constraints, leading to small vibration datasets that limit the reliability and detectability of structural damage. Such data scarcity hinders the use of machine learning models, which typically rely on abundant labelled data for robust pattern recognition and anomaly detection. To overcome this limitation, this study proposes an unsupervised deep learning methodology that integrates generative and discriminative models for enhanced damage detectability under small vibration data conditions. First, a generative neural network is employed to perform data augmentation by learning the underlying distribution of limited modal frequency samples of normal structural states and producing synthetic yet physically consistent features. Then, an unsupervised anomaly detection network, trained only on augmented healthy-state data, is utilized to identify damage-induced deviations. This combined generative–discriminative approach enables learning rich feature representations while preserving the unsupervised nature of the problem. The framework is validated using a modal frequency dataset of a cable-stayed bridge monitored within a short-term program. Despite limited modal frequency instances, the proposed method demonstrates the ability to enhance data diversity, improve class separability, and increase the sensitivity of damage indicators to structural damage.