Investigating of Crack resistance and Healing in BITuminous materials

This project aims to develop a deeper understanding of the mechanisms behind cracking and healing in bituminous materials by combining advanced experimental techniques with artificial intelligence. Asphalt degradation due to traffic loading, thermal stresses, and environmental aging is a significant problem in infrastructure worldwide. While the causes of cracking are well known, the detailed behavior at different scales, especially the healing potential of bitumen, is not yet fully understood. 

The research takes a multi-scale approach, divided into two main parts: 

• The first phase of the project focuses on the microscale behavior of bitumen. A novel test setup is being developed to stretch a droplet of bitumen in nano-scale steps, allowing direct observation of micro-crack formation and healing. This test is carried out using a Confocal Laser Scanning Microscope (CLSM), which enables high-resolution, real-time visualization of internal structural changes in the bitumen. These tests will provide insight into how cracks initiate and close at the binder level and will generate valuable data for later modeling efforts. 
•  The second phase of the project shifts focus to the mesoscale, examining bituminous mortar, this is a composite material consisting of bitumen and fine aggregates. The test is being designed to utilize Digital Image Correlation (DIC) for capturing strain fields and tracking crack propagation during mechanical loading. DIC allows for full-field, non-contact deformation measurements, making it ideal for observing localized damage and healing effects. 

In parallel with the experimental work, the project involves building a neural network (NN) to quantify and predict both cracking and healing behavior. Experimental data from both scales will be used to train the AI model. Additionally, TU Delft will contribute by developing a high-fidelity Finite Element Model (FEM) that accurately simulates the mechanical behavior of bituminous mortar under various conditions. The simulated data will further enhance the training dataset for the neural network. The long-term goal is to create a robust AI-based tool capable of quantifying crack growth and healing capacity in bituminous materials, contributing to more durable and sustainable pavement designs.