The openLAB is a 45 m long, three-span semi-integral research bridge near Bautzen, Germany, constructed from prestressed concrete girders. It serves as a benchmark platform for evaluating and comparing structural health monitoring (SHM) systems through controlled experiments. This paper presents the experimental setup, procedure, and selected results from load tests conducted in May 2025, focusing on static deformation up to the ultimate limit state (ULS) under a concentrated load of 400 kN, inducing a maximum deflection of 60 mm. The structural response was monitored using interdisciplinary methods – e.g., laser triangulation sensors (LTS), tilt sensors, robotic total station (RTS), unmanned aerial vehicle (UAV) photogrammetry, and fiber optic sensing – with strong agreement among the methods. Finite element (FE) models, developed to support test preparation, showed significant variability, highlighting sensitivity to modeling assumptions. All data – comprising FE models, environmental conditions, geodetic measurements, UAV photogrammetry, crack documentation, and fiber optic sensor readings – are openly accessible, providing a rich, multi-source dataset for future SHM research.
TL;DR: An unsupervised deep learning methodology that integrates generative and discriminative models for enhanced damage detectability under small vibration data conditions is proposed and demonstrates the ability to enhance data diversity, improve class separability, and increase the sensitivity of damage indicators to structural damage.
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 pr…
Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides detailed information about their structural integrity and overall condition. Due to their intricated characteristics, UGWs are…
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.…
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: This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.
While neural networks represent a promising approach for evaluating sensor data to assess damage presence, location and severity, large amounts of data are required for training. However, the generation of experimental data is both labor-intensive and costly. Transfer learning is…
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 detect…