Synthesis of real loads in road pavements using novel laboratory techniques

The objective of subproject A04 is to synthesize the effect of traffic and environmental loads on the response of flexible and rigid pavement structures. To this end, new experimental techniques and procedures will be developed to characterize the behavior of asphalt and concrete materials used in road construction. A new True Triaxial Test Device (TTTD) will be acquired aiming to simulate real stress scenarios within the pavement in which the magnitude of the three principal stresses is different. The behavior of asphalt and concrete under shear stress conditions will also be investigated using a novel in-house developed shear tester known as the Dresden Dynamic Shear Tester (DDST).
Subproject A04 is also responsible for the development of the numerical representation, at meso-scale level, of asphalt materials. The mesomodel will be calibrated with experimental data obtained with the TTTD and DDST. Geometrical reconstruction techniques and finite element models will be used within a cyber-physical framework to accelerate material innovation efforts.
Summarizing, in subproject A04, numerical and experimental investigations will be carried out for the development of a mesoscale-level digital twin of asphalt materials that can effectively capture the behavior of a physical asphalt twin. In addition, subproject A04 will also provide experimental data to other subprojects for the validation of material models for Asphalt and Concrete at the macro-scale level.

Project Participants
Publications of the Subprojects
- A04A06
Quantification of viscous and damage dissipationof bituminous binder and mastic using White-Metzner model
- Journal
- International Journal of Pavement Engineering ·
- Publisher
- Taylor & Francis ·
- Year
- 2023
- Keyword
- Fatigue; bitumen; mastic;non-linear viscoelasticity;White-Metzner model;viscous dissipation; damagedissipation; large amplitudeoscillatory shear; harmonicanalysis
- A04A06
A stochastic neural network based approach for metamodelling of mechanical asphalt concrete properties
- Journal
- International Journal of Pavement Engineering ·
- Volume
- 24 ·
- Pages
- 2177650 ·
- Publisher
- Taylor & Francis ·
- Year
- 2023
- Keyword
- Convolutional neural networks; machine learning; asphalt concrete; stochastic modeling
Interactions
