Borchers, J.-H., & Sotto, L. F. P. D. (2025). Transverse evenness prediction in asphalt pavement using an artificial neural network. In L. Eberhardsteiner, B. Hofko, & R. Blab (Eds.), Advances in Materials and Pavement Performance Prediction IV : Contributions to the 4th International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2025), 7-9 May 2025, Vienna, Austria (pp. 543–546). TU Wien, E230-03 Road Engineering. https://doi.org/10.34726/10560
Advances in Materials and Pavement Performance Prediction IV : Contributions to the 4th International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2025), 7-9 May 2025, Vienna, Austria
A pivotal aspect of pavement management systems (PMS) and road asset management is the deployment of predictive algorithms for pavement performance. The objective of this paper is to investigate the potential of artificial intelligence (AI) in this process, specifically for predicting transverse evenness, which is represented by rut depth and water depth. Firstly, the condition survey and assessment in Germany is described. Then an overview of the current methodology for prediction of road condition data and the artifi- cial neural network (ANN) employed in this paper is given. Subsequently, both methods are evaluated on a subnetwork and their performances are compared, with the employed ANN showing an improved prediction performance.
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Research Areas:
Modeling and Simulation: 35% Structure-Property Relationsship: 30% Computational System Design: 35%
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