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: 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…
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.…
Accurate prediction of the structural temperature field is crucial for the static and dynamic monitoring of engineering structures, with particular significance for heritage buildings where material preservation is paramount. The complex, time-lagged, and non-linear relationship…
Online Health Management (HM) plays a pivotal role in optimizing the lifecycle of aircraft while ensuring safety, reliability, and structural integrity. During service, aircraft structures experience complex cyclic loading, making accurate load analysis essential for effective li…
Fatigue in metallic materials leads to progressive degradation driven by a sequence of microstructural mechanisms occurring over the life cycle. While fracture is typically the most obvious and critical damage state, it only appears at the end of life. However, when no fracture i…
Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point,…