Abstract
In the research project presented here, the focus was on the design, manufacture, and incorporation of a hybrid fabric made of natural fibers, which can detect changes in the condition of the surrounding components by means of integrated sensor technology. The aim was to incorporate the functionalized fabric into the asphalt base layer of road pavements in order to detect structural damage to the layer. At the core of the fabric is a sensor material that is stretched by stresses and the electrical resistance is changed. The data obtained from the road is interpreted with the help of sensored test specimens loaded under laboratory conditions. In this way, signals and patterns can be assigned to the respective type of damage and its severity. This enables a statement to be made about the fatigue condition of the asphalt base layer without having to take samples on site. Furthermore, the condition can now be monitored continuously and over a large area. To this end, a software solution was developed that uses adapted machine learning methods to learn the relationship between an electrical voltage measured in the fabric and the condition of the asphalt base layer determined by load tests. With the aid of this software, the degradation state can then be interpreted exclusively from the data measured in the fabric and presented in the form of a few key figures. From this, appropriate recommendations for rehabilitation work can be derived directly in practical use.
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Acknowledgements
This work is funded by the Federal Ministry for digital and transport within the project “SenAD”. With the mFUND innovation initiative, the BMDV has been funding research and development projects around digital data-based applications for the mobility of the future since 2016. In addition to financial support, the mFUND supports networking between stakeholders from politics, business, and research with various event formats as well as access to the data portal mCLOUD. Also, a special thanks to the practice partners Fraunhofer-Institut für Holzforschung, Wilhelm-Klauditz-Institut WKI, and Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung IFAM. We also would like to thank all the co-workers in the Magdeburg-Stendal University of Applied Sciences, who are supporting this project.
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Herrmann, J., Kayser, S., Leopold, M., Dunkel, J. (2024). Sensor Integration in Asphalt for Data-Based Degradation Monitoring. In: Akhnoukh, A., Kaloush, K., Souliman, M.I., Chang, C. (eds) A Pathway to Safe, Smart, and Resilient Road and Mobility Networks. IRF IRF 2022 2022. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-47612-9_12
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