Data Set of Load Tests and Structural Health Monitoring of a concrete boxgirder bridge

Load tests are an essential tool to verify the compliance of bridges with their design specifications and to assess their actual load-bearing capacity. In this paper, a series of static and dynamic load tests conducted on a concrete boxgirder bridge are documented. The bridge is equipped with a long-term Structural Health Monitoring (SHM) system, providing data covering an entire seasonal cycle upon request for academic research purposes. Due to the large amount of data, the full SHM data cannot be provided. The load test data is available on Zenodo. The objectives of the static and dynamic tests are (i) to capture the bridge’s current condition under various loading scenarios while identifying potential structural weaknesses, (ii) to evaluate the system’s sensitivity to small mass variations, and (iii) to generate data for model calibration and validation of anomaly detection algorithms by simulating a design load case. This article presents an experimental data set obtained from an instrumented concrete box girder bridge. The measurement data provided contributes to reducing the gap of limited availability of data sets from full-scale load tests on structures. The data set includes time series of accelerations during vehicle crossings and strain measurements during static loads. The construction of the bridge and the structural health monitoring system are described in detail and supported by drawings. The structure of the measurement data in the open-access data files is briefly explained. Follow-up studies will analyze the SHM data in collaboration with multiple research groups.

💡 Research Summary

The paper presents a comprehensive, open‑access dataset derived from static and dynamic load testing of an in‑service concrete box‑girder bridge, complemented by a long‑term structural health monitoring (SHM) system. The bridge, built in 1972, spans 50 m with a 10 m deck width and features a single‑cell, prestressed concrete box girder with an open‑frame layout. After refurbishments in 1978, 2010, and 2011, the structure currently exhibits only minor defects such as a 1 cm expansion‑joint offset and superficial cracks.

The SHM installation comprises more than 100 sensors: 36 three‑axis accelerometers (200 Hz), 56 strain gauges (200 Hz), 8 magnetic inclinometers (100 Hz), 4 displacement transducers (100 Hz), and two weather stations (10 Hz) measuring temperature, humidity, wind, solar radiation, and pressure. All sensor locations are referenced to a global coordinate system, facilitating direct comparison with finite‑element models. Accelerometers are mounted on a magnetic steel track that can be repositioned, and three laser rangefinders were temporarily added to capture truck entry/exit times and velocities during dynamic tests.

Two static test campaigns were conducted. The long‑term test placed sand‑filled bags (≈680 kg, 1 420 kg, 2 160 kg) at the mid‑span and at a quarter‑span location, each stage held for ten days, to evaluate the system’s sensitivity to small, slowly varying masses under realistic environmental conditions. The short‑term test used a three‑axle truck (unloaded 12.34 t, loaded 21.25 t) parked on six steel load‑distribution plates (≈187 kg total) at three predefined positions (center, west quarter, east quarter). Each parking position was held for at least ten minutes with the engine off, and the events were logged by the laser sensors.

Dynamic testing involved driving the same truck, both loaded and unloaded, across the bridge at four speeds: crawl (5 km/h), 20 km/h, 40 km/h, and 50 km/h. The crawl speed run was repeated ten times; the other speeds were each repeated five times. Accelerometer, strain, and laser data were recorded simultaneously, allowing the extraction of influence lines and the assessment of non‑linear dynamic behavior, signal‑to‑noise ratio variations, and modal content changes with speed.

All raw and processed data are deposited on Zenodo in CSV and MATLAB formats, with a detailed hierarchical file structure and accompanying metadata described in the manuscript’s appendix. The dataset includes time series of accelerations, strains, displacements, and environmental parameters, as well as derived quantities such as frequency spectra and influence lines.

The authors argue that the availability of this full‑scale, multi‑sensor dataset fills a notable gap in bridge engineering research, where open data are scarce. It enables validation of finite‑element models, calibration of analytical and data‑driven bridge response models, and development of machine‑learning algorithms for anomaly detection, damage identification, and fatigue assessment. Future work will integrate the long‑term SHM records (covering an entire seasonal cycle) with the load‑test data, fostering collaborative studies across multiple research groups. The paper thus provides both a valuable experimental resource and a methodological blueprint for comprehensive bridge monitoring and testing.