Conference Agenda

Smart infrastructure systems require monitoring frameworks that can function autonomously, remain effective under varying operational conditions, and issue dependable warnings without requiring labeled damage information. This study presents an unsupervised structural health monitoring (SHM) framework for transportation infrastructure that integrates LLM-inspired representation learning with a gated multi-GPT fusion autoencoder for robust intact-only anomaly detection using networked strain measurements. In the proposed pipeline, raw multi-channel strain-gauge signals are first processed through an adversarial autoencoder (AAE) to extract compact and informative feature representations, which are then fed into the downstream model. Multiple GPT-based reconstruction experts analyze these segmented AAE-derived features in parallel, while a compact gating network adaptively combines their outputs. The proposed workflow supports calibration using only intact-condition data, adaptive threshold setting, and continuous monitoring under non-stationary loading scenarios. The framework is validated on a laboratory pedestrian bridge instrumented with multichannel strain gauges and exposed to walking-induced excitation, where it achieves high anomaly detection performance across two damage levels. Robustness studies under both white and colored noise, together with threshold sensitivity analysis, confirm stable class separability and reliable operational performance. Comparative evaluation against leading unsupervised anomaly detection methods further highlights improved robustness to correlated noise and a reduced number of false alarms, demonstrating a scalable sensing-to-decision solution for resilient SHM.

Aging civil structures are increasingly vulnerable to environmental degradation and natural hazards, highlighting the need for reliable and automated structural health monitoring (SHM) systems. This study proposes a novel SHM framework that integrates Multiscale Increment Entropy (MIE) with the InceptionTime deep convolutional neural network (CNN) to detect and localize structural damage with high accuracy. The MIE technique is employed to analyze the velocity responses of structures under ambient excitation, providing robust and scale-independent entropy features that capture nonlinear and nonstationary characteristics of structural behavior. These entropy-based features are input into the InceptionTime model for automated damage classification and localization. To validate the performance of the system, both numerical simulations and laboratory-scale experiments were conducted using a seven-story steel frame benchmark structure. The proposed method achieved 99.78% accuracy in numerical simulations and 94.21% accuracy in experimental verification, demonstrating strong consistency, robustness, and generalization capability. The integration of MIE with InceptionTime effectively enhances the interpretability and reliability of entropy-based damage assessment, offering a scalable, data-driven approach for real-time SHM applications in complex engineering systems.

The safe and reliable operation of natural gas compressor units is crucial for ensuring a secure and stable gas supply. However, the interaction between natural gas and pipelines inevitably induces flow-induced vibrations, leading to long-term cyclic stress variations in the pipelines. This results in the accumulation of fatigue damage and the initiation of cracks in stress concentration areas, thereby threatening the health of the compressor unit system. In this context, this study proposes a deep learning framework for predicting flow-induced vibration and fatigue life in pipelines, which is based on numerical simulations and experimental measurements, enabling accurate prediction of pipeline vibration responses and fatigue states under different gas transmission conditions. This study first conducts modal analysis on numerical and experimental models, updating the finite element model using the first three natural frequencies to accurately reflect the dynamic characteristics of the experimental model. Subsequently, a deep learning approach based on a multi-layer perceptron is proposed for predicting pipeline flow-induced vibration responses. The time-frequency acceleration responses of the pipeline under different gas flow conditions are predicted via numerical simulation, and the corresponding strain fields at stress concentration locations are evaluated using a quantitative relationship with the predicted accelerations. The results demonstrate that the proposed deep learning framework, leveraging simulated and measured pipeline data, enables accurate prediction of flow-induced vibrations and fatigue life.