Materials Design and Model based Characterization: Leveraging simulation-based approaches to innovate, design, and evaluate novel sustainable cementitious composites and other building materials. This includes enhancing properties such as strength and durability, with a specific focus on improving performance with reduced environmental impact.
Durability and Damage Prediction through Mechanics based Multiscale Modeling: Utilizing mechanics based multiscale modeling to accurately predict the behavior of concrete and assess the effects of various loadings on material durability, aiming to significantly extend thier lifespan.
2. Innovative Computational Techniques for Material Science:
Machine Learning for Efficient Simulations: Applying machine learning for model-order reduction to streamline complex simulations, enhancing efficiency without sacrificing accuracy.
Development of Novel Numerical Methods: Innovating in numerical methods (including Continuum Micromechanics, Voxel Finite Element Method, Finite-Cell Method, Lippmann-Schwinger-FFT, and the Lattice Boltzmann Method) for accurate scale-bridging simulations to improve predictive capabilities and analysis across different material scales.
Simulation of Non-equilibrium Processes: Employing Cellular-Potts models to simulate and understand the behavior of materials under non-equilibrium conditions, crucial for predicting complex microstructural evolution.
3. Data-Driven and AI-Enhanced Approaches for Material Innovation:
Harnessing extensive experimental datasets using machine learning to drive the development, characterization, and optimization of building materials.
