Damage detection is perhaps the most critical objective of structural health monitoring. Proper documentation of the progression of structural damage from onset to failure enables performance-based maintenance that mitigates risk and contains socioeconomic impact. Recently, damage detection methods that focus on change detection through analyses of the structural global response in the frequency or time domain have garnered attention due to their efficiency and accuracy. This work proposes a novel time-domain, nonparametric, data-driven change detection method. The time-domain B-Spline transfer function is defined as a signature time history response of the structure, referred to in this work as the B-Spline signature response (BSR). BSR may be computed directly through a physics-based simulation, or it be extracted in a discrete form from measurements of displacement, velocity, or acceleration time histories of structural response to any external excitation. The BSR is independent of loading, is a characteristic of the system, and captures the condition of the structure at the time of acquisition without the need for identifying structural properties. In this work, the fundamentals of the BSR concept are introduced first along with the process of extracting a BSR from structural responses to arbitrary excitations. Verification and validation studies are conducted based on computer simulations and laboratory testing and are discussed in detail. Subsequently, an approach is proposed to detect change through correlation of BSR signals acquired at different times during the monitoring period. Any changes in the intrinsic characteristics affecting the response cause BSR changes and correlation loss. The change detection methodology is demonstrated through computer simulations and experimental investigations. Initial parametric studies are conducted to investigate the suitability of the proposed approach to detect small structural changes. This article demonstrates the validity of the proposed method and establishes the framework for further development and case-specific applications.

With nearly 140,000 mi of track, railroads are central to North America’s transportation infrastructure, carrying over 40% of freight by ton-miles and serving millions of passengers annually. Maintaining the structural integrity of the track is essential for operational safety and economic efficiency. Integrity is compromised by stiffness changes caused by ballast degradation, subgrade settlement, aging ties, temperature-induced stresses, and repeated loading. Proactive track health monitoring systems are needed to detect such changes continuously under dynamic train loads. This paper presents a multistage physics-informed framework for the in-motion detection of track stiffness irregularities (TSIs), serving as a proxy for identifying potential defects. The proposed system uses onboard vibration measurements, processed through advanced signal processing techniques. The system consists of three modules, for data acquisition, change detection, and change classification. It operates on an edge-computing platform, allowing real-time processing, and achieves over 95% data compression. The change-detection module, emphasized in this paper, combines wavelet packet analysis, variational mode decomposition, and the Hilbert transform to extract instantaneous energy features from vertical acceleration signals. These features act as sensitive indicators of track stiffness variation. The method was validated through both simulation and offline field data. Simulations captured a wide range of TSIs, including abrupt changes, gradual transitions, and localized weak zones. Field validation confirmed the model’s ability to consistently detect recurring irregularity patterns along the track, without requiring any assumptions about their physical origin. These results demonstrate the robustness, scalability, and real-world applicability of the proposed approach for continuous rail infrastructure monitoring.

Train derailments pose a critical failure mode in railway systems, often resulting in severe safety hazards and significant financial losses. Understanding how train loading patterns interact with track deficiencies is essential for effective failure analysis and prevention. This paper introduces the Rapid Vehicle–Track Interaction (R-VTI) as a framework to simulate the complexities of dynamic train–track interactions. Central to the framework is the novel Pseudo-Dynamic Coupling (PDC) technique, which enables computation of wheel–rail dynamic forces with substantially greater computational efficiency than currently used coupling techniques. The R-VTI framework supports a wide range of solver techniques and subsystem coupling schemes, making it adaptable for different simulation requirements. The framework is validated against Federal Railroad Administration field measurements, achieving agreement within 5% error. A case study of different train–track configurations shows that the framework can quickly detect when loading patterns and track conditions exceed derailment thresholds. Axle-level results reveal that unloaded cars near the front or middle of the train increase the likelihood of derailment-failure modes. The efficiency of the R-VTI framework enables large-scale scenario analysis, supporting both optimized loading strategies and targeted track maintenance. By providing a robust and scalable solution, the R-VTI framework advances derailment potential assessment practices, offering a practical tool for improving railway safety and operational resilience.