Contributing person datacite.contributor.HostingInstitution	I4S at the Universitiy of Applied Sciences Leipzig
Contributing person datacite.contributor.ProjectMember	Max Moeller
Contributing person datacite.contributor.ProjectMember	Tobias Juler
Contributing person datacite.contributor.ProjectMember	Adrian Patzelt
Contributing person datacite.contributor.RightsHolder	I4S at the Universitiy of Applied Sciences Leipzig
Documentation of the data datacite.description.TechnicalInfo	Please see provided datasheet.
Additional geographical or spatial references datacite.geolocation	51°03'25.3"N 12°05'56.8"E
Description of the data datacite.resourceType	The dataset contains 273 measurements of ten minutes each. The acceleration data is obtained by 56 equally distributed PCB303A03 sensors. The large-scale experiment took place from September 10th to September 12th and was divided into three different ensembles with corresponding data packages. The first one is referred to as ‘Reference’ and contains data with one lane blocked and without any additional mass. On September 11th, the additional mass (cargo trucks with a total mass of 39 t) was placed in Field 4 and the corresponding ensemble is referred to as ‘Field 4’. For the third ensemble, the cargo trucks were placed in Field 3 on September 12th. The obtained measurements from this day are found in the data package 'Field 3'. More details about the data are available in the provided datasheet.
Type of the data datacite.resourceTypeGeneral	Dataset
Total size of the dataset datacite.size	70857388229
Author dc.contributor.author	Rohrer, Maximilian
Author dc.contributor.author	Lenzen, Armin
Upload date dc.date.accessioned	2025-03-05T17:45:45Z
Publication date dc.date.available	2025-03-05T17:45:45Z
Data of data creation dc.date.created	2024-09
Publication date dc.date.issued	2025-03-05
Abstract of the dataset dc.description.abstract	The research presented is part of the SPP100+ of the German Research Foundation. Subject of the research is the early detection of damage, especially on large bridge structures. For this purpose, a large-scale experiment for vibration-based output-only damage detection was conducted at Flossgraben Bridge near Zeitz (Germany). The experiment was conducted over the course of three days for which the structure was equipped with 56 uniaxial piezoelectric acceleration sensors. The experimental structure is a healthy bridge structure constructed in 2001 and is part of the federal road B2 with two lanes for traffic. The bridge is composed of seven spans, with a total length of 358 m and a total weight of approximately 4750 t. The superstructure consists of an in-situ concrete slab resting on a trapezoidal, torsionally rigid steel box girder with inclined webs. The large-scale experiment was divided into a reference phase and two damage equivalent load case phases. Cargo trucks with a total mass of 39 t were used as additional mass and placed in field 4 and field 3 as mass alterations. During the complete course of the large-scale experiment one traffic lane was closed, while the other was open for traffic. The experiment was carried out with the kind support of the LSBB Saxony-Anhalt.
Public reference to this page dc.identifier.uri	https://opara.zih.tu-dresden.de/handle/123456789/1328
Public reference to this page dc.identifier.uri	https://doi.org/10.25532/OPARA-767
Publisher dc.publisher	Technische Universität Dresden
Licence dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	en
URI of the licence text dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/
Specification of the discipline(s) dc.subject.classification	4::45::410::410-05
Specification of the discipline(s) dc.subject.classification	4::45::410::410-04
Title of the dataset dc.title	Flossgraben Bridge - Large-Scale Experiment Acceleration Data
Research instruments opara.descriptionInstrument	Piezoelectric uniaxial acceleration sensors PCB393A03
Underlying research object opara.descriptionObject.PhysicalObject	Flossgraben Bridge - 7 span bridge structure of 358 m length
Project abstract opara.project.description	Part of the DFG (German Research Council) focus program “SPP100+”, subproject: ADMO — Automatic Data-Drive Modeling and H2/H∞-Norm-based Dimension Reduction of Process-Oriented and cooperative systems for SHM-Condition analysis with Methods of System Identification and Machine Learning on Exposed Structures The digital change is causing profound changes in all areas of society. In the fusion of BIM, the optimized planning, execution and management of plants, buildings and infrastructures, with Structural Health Monitoring (SHM) a digital twin functions as a central element of an efficient data organization. The aim of this project is a method that realizes automated data-driven modeling based on the H2/H∞-norm and methods of system identification coupled with machine learning. This enables a condition monitoring as a digital twin over the service life of the real twin, the building, which is incorporated into an SHM/BIM concept. Based on process-oriented cooperative systems, special physically interpretable indicators are able to automatically display and localize structural changes. The numerical method works with stochastic multi-correlated output-only measurement data, with special consideration and classification of environmental and operational conditions. The automatically generated parameterized stochastic process models of the system and filter theory enables a prediction of future damage states on the examined structure. This gives the public authority a set of tools for predictive planning of maintenance measures on structures with high economic benefits. The research was conducted at the I4S — The Institute for Statics, Structural Dynamics, System Identification, and Simulation at the University of Applied Sciences.
Public project website(s) opara.project.publicReference	https://i4s.htwk-leipzig.de/en/research/current-research-projects
Public project website(s) opara.project.publicReference	https://i4s.htwk-leipzig.de/en/research/current-research-projects/automatic-data-driven-modeling-with-system-identification-methods
Public project website(s) opara.project.publicReference	https://www.spp100plus.de/forschung/cluster-c/teilprojekt-c03/
Project title opara.project.title	ADMO - Automatic Data-Drive Modeling and H2/H∞-Norm-based Dimension Reduction of Process-Oriented and cooperative systems for SHM-Condition analysis with Methods of System Identification and Machine Learning on Exposed Structures

Flossgraben Bridge - Large-Scale Experiment Acceleration Data

Files

Original bundle

License bundle

Collections