Structural health monitoring (SHM) of small structures is necessary to ensure their long-term sustainability. However, the large number and scattered distribution of such structures raise a significant techno-economic challenge, which calls for optimization of the monitoring solutions to be deployed. Monitoring these structures requires complementary data to better understand their behavior. Such information is typically obtained through the deployment of additional sensors measuring temperature, solar radiation, humidity, and wind speed. Nevertheless, this approach is difficult to balance for small or isolated structures, whose maintenance programs already face tight constraints to ensure durability. Optimizing the number of sensors to be installed is therefore crucial. In this context, artificial neural networks can model complex relationships between time series through learning processes and thus represent an alternative to the deployment of numerous physical sensors. Instead of multiplying measurement points on a structure, neural networks can model various phenomena affecting it (gradients, internal temperature, surface heating, drying, wind effects) from a smaller set of input data. In this context, this study focuses on the use of publicly available datasets in France This paper presents investigates the use of Temporal Convolutional Networks (TCN) for sensor data regression based on external and public data, the challenges faced with such approach and discusses their potential as a “ready-to-use” analysis tool. A case study using multiple type of sensors on concrete structures is presented.
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…
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…
Advances in robotics have led to autonomous platforms that convey sensor packages used for reconfigurable (mobile) monitoring solutions. In particular, ultrasound has rapidly evolved in this modality, where in-situ advanced autonomous manufacturing processes are outfitted with ul…
TL;DR: This work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures.
This paper presents an integrated Structural Health Monitoring (SHM) strategy for flax fiber reinforced thermoplastic composites, combining Nonlinear Resonance Acoustic Spectroscopy (NLRAS), Acoustic Emission (AE), and data-driven damage identification based on machine learning.…
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…