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.
TL;DR: A framework for CVSHM, which allows for robust detection, localization, and assessment of the damage even for highly contaminated displacement data, is proposed and tested using realistic synthetic videos representing vibrating truss structure.
Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a poss…
Structural Health Monitoring (SHM) is becoming essential in civil engineering due to its ability to continuously assess the condition of infrastructures and detect potential damage. SHM techniques are generally categorized into data-driven (DD) and model-driven (MD) approaches. D…
TL;DR: A framework that combines unsupervised learning and domain adaptation to enhance model transferability under limited data, reducing dependence on labeled datasets while preserving sensitivity to structural and operational changes is proposed.
Abstract: The scarcity of long-term vibration data real-world structures remains a significant barrier to the application of machine learning in structural health monitoring (SHM). Available datasets are often short, unlabeled, and affected by operational and environmental variab…
TL;DR: An AI-driven system that can provide comprehensive structural health diagnostics from a single input structural image, using six parallel computer-vision pipelines with experimentally validated real-time inference performance, forms a strong base toward automated, scalable, and data-driven structural health monitoring.
Context / Content: Civil infrastructure and heritage structures deteriorate with time due to environmental exposure, material aging, moisture ingress, pollution, and biological growth. Traditional inspection relies heavily on manual assessment, which is slow, risky, and subjectiv…
The structural integrity of railway rails is essential for the safety and efficiency of modern transportation networks, where early detection of damage is crucial to preventing catastrophic failures and service disruptions. Guided wave-based structural health monitoring (SHM) off…
TL;DR: Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements, and a supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support.
Ensuring the integrity of critical infrastructure, such as bridges, dams, and large-scale structures, is essential to safety, reliability, and operational continuity. These assets are exposed to mechanical, thermal, environmental, and operational loads that can accelerate fatigue…