Data Set of Load Tests and Structural Health Monitoring of a Concrete Box Girder Bridge

Abstract Static and dynamic load tests were conducted on an anonymized in-service prestressed concrete box girder bridge constructed in 1972 and designed for Bridge Class 30.The bridge is instrumented as part of the Digitalization and Technology Research Center of the Bundeswehr-Structural Health Monitoring (DTEC-SHM) research initiative with a permanent structural health monitoring system comprising 128 sensors, including accelerometers, strain sensors, inclinometers, displacement transducers, embedded temperature sensors, and weather stations. The test program includes long-term static loading with stepwise added masses of 680, 1,420, and 2,160 kg; short-term static truck parking positions; and repeated dynamic truck crossings under loaded and unloaded vehicle configurations. The short-term static tests used a loaded truck of 21,250 kg parked at three bridge positions, while the dynamic tests used loaded and unloaded truck configurations of 21,250 and 12,340 kg at speeds up to 13.9 m/s. The published load-test data are available on Zenodo in HDF5 format and include synchronized measurements from the monitoring system, temporary laser sensors used for vehicle-position and passage-time validation, and derived tracked eigenfrequency time series from the reference and long-term static load-test period. The bridge geometry, sensor layout, acquisition system, load cases, truck geometry, and data hierarchy are documented to support reuse of the data. Plausibility checks are presented for the main response quantities, including laser-based validation of parking positions and passage windows, vibration spectra and tracked modal parameters, and repeatable strain and displacement response profiles under crawl-speed truck passages. The data set addresses the scarcity of publicly available full-scale bridge load-test data and supports applications such as finite-element model calibration, neutral-axis tracking, influence-line extraction, and data-driven anomaly detection.