Numerical and Experimental Development of an “Accelerated Repeated Rolling Wheel Load Simulator” (ARROWS) Phase II: Scaling Effects Analysis and Predictive Modeling for Enhanced Pavement Durability

• Applicants: Professor Dr. Alvaro Garcia-Hernandez; Professor Dr.-Ing. Michael Kaliske; Professor Dr.-Ing. Pengfei Liu, Ph.D.
• Subject Area: Construction Material Sciences, Chemistry, Building Physics ; Applied Mechanics, Statics and Dynamics
• Term: since 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 414936990

Upon completing the first phase, which yielded the ARROWS device for lab-scale fatigue testing at Institute of Highway Engineering (ISAC) of RWTH Aachen and the corresponding numerical simulation methods at Institute for Structural Analysis (ISD) of TU Dresden, the project now advances to its second phase. The central aim of this next phase is to enhance the predictive analysis of pavement lifespans. This enhancement will be achieved by integrating advanced sensor technologies into both the ARROWS and the EvAH fatigue testing. The EvAH test device was developed at ISAC within a parallel project and offers a novel approach to accelerated testing on half-scale asphalt pavements. The data obtained from these technologies will drive the development of corresponding finite element methods and machine learning algorithms, specifically tailored to understand and quantify scale effects. This approach is fundamental to bridge the gap between lab-scale experiments and real-world pavement conditions. The advanced sensors will enable detailed laboratory experiments that simulate experimentally heavy traffic wear, yielding valuable insights into pavement behavior. Fast predictive models, drawing on the extensive datasets from both the initial phase and newly developed multi-physical and multi-scale numerical simulations, will provide accurate predictions of pavement performance. Machine learning techniques will further refine these models, enabling robust simulations and analyses of time dependent degradation. This phase aims to yield more precise predictions about pavement durability and the requisite maintenance, ensuring a more efficient use of resources for pavement management. The integration of these innovative testing and predictive methods marks a significant advancement in the sustainable management and optimization of pavement infrastructure.

• DFG Programme: Research Grants