top of page
APSE Distinguished Lecture
Leveraging Molecular Dynamics to Validate Mechanisms of Bond Strength and Moisture Resistance in Asphalt Composites
Dallas Little
Texas A&M University, USA
Abstract:
The composite that provides the major structural component of trillions of dollars of pavement infrastructure throughout the world is a complex blend of components. These include at a minimum the asphalt binder or bitumen and each mineral aggregate comprising both a larger, structural component and a smaller mineral filler component. The APSE distinguished lecture will describe how atomistic modeling provides a deeper understanding of how mineral fillers interact with the asphalt binder to form a robust, tough, and resilient mastic. Specifically, atomistic modeling validates experimental results that define how specific mineral fillers such as hydrated lime bond with specific asphalt moieties to form a stronger mastic with the ability to increase fracture toughness, aid damage recovery or healing and resist moisture damage. The atomistic modeling paints a picture of asphalt mastic comprising mineral filler ranging from only a few microns to as large as one hundred microns surrounded by a region of asphalt moieties bonded to the mineral inclusions. The moieties that interact with the calcium hydroxide filler, for example, introduce a “halo effect” surrounding the filler. This “halo region” can absorb and dissipate crack energy toughening the mastic and even control crack tip size to a level consistent with microcrack healing. The adsorption of specific moieties allows other moieties to migrate toward the interface with larger mineral aggregate inclusion and form a more tenacious and durable bond, more resistant to debonding in the presence of moisture. Atomistic modeling supports decades of key research by experts at Western Research Institute, at Texas A&M and in France. The lecture also describes how free energies of dissociation are used to characterize the nature, strength, and durability of bonds. The atomistic modeling results correlate with thermodynamic calculations derived from experimental constants and are consistent with infrared spectrometric data.
Biography of Dallas Little: Link
​
Keynote Lecture
Digital Twin of the Road - Recent Advances, Potentials and Challenges
Michael Kaliske
TU Dresden, Germany
Abstract:
The road infrastructure, which serves as a crucial foundation of our modern society, is currently facing various challenges, including increasing traffic and loads, damage and necessary maintenance, as well as the need to conserve energy and resources. Additional challenges are expected in the future due to mobility changes, autonomous driving, and climate change. To address these challenges, there is a strong demand for more sustainable and long-lasting roads, coupled with an optimized design and operational approach. On the other hand, the potential of digitalization has so far been rarely utilized in the design and operation of road systems. To tackle these issues, the interdisciplinary research center (SFB/TRR 339) hosted at TU Dresden and RWTH Aachen is currently focused on the development of a digital twin of the road. The presentation covers the recent research on the required sub-models and components, such as physical models for tire-road interaction, data generation and collection from various sensors, the development of a geographic information system, and first approaches for linking available sensor data with corresponding models.
Biography of Michael Kaliske: Link
Chemomechanics and Data-driven Modelling to Understand and Predict Paving Material Behaviour
Katerina Varveri
TU Delft, The Netherlands
Abstract:
Advances in pavement engineering rely more and more on our ability to understand and predict the behaviour of complex material systems, including recycled, modified, and bio-based binders. At the same time, large volumes of experimental and field data are being generated and yet remain underutilised in the design and optimisation of infrastructure materials.
This keynote speech highlights the importance of integrating chemomechanical understanding with data-driven modelling to improve the accuracy and reliability of performance predictions. By combining physical insights with statistical learning and uncertainty quantification, we can develop models that capture both performance trends and the underlying mechanisms of material behaviour. By bringing data and fundamental understanding together, we can go beyond characterisation, using this synergy to learn from materials more effectively and engineer systems that are more durable, circular, and resilient.
Biography of Katerina Varveri: Link
bottom of page