Mesoscale Model Capability Informed by Cemetitious Composite Microstructure

The objective of this SBIR effort is the formulation of a comprehensive and accurate computational tool for the prediction of macroscopic material properties based on the concrete basic constituents (aggregate, cement, water, and additives) and on environmental conditions. The analytical approach for accomplishing this goal involves three intermediate concrete scales: micro-scale (scale of the cement paste constituents), mini-scale (fine scale of paste, aggregates, and explicit Interfacial Transition Zone), and meso-scale (coarse scale of aggregates and paste). The work proposed in Phase I will establish the entire computational framework for accomplishing the objectives of this SBIR and will study the feasibility of the proposed overall approach. The research activities planned for Phase I are as follows: 1) formulation of the hydration-informed stochastic LDPM framework, 2) validation of LDPM at small length scales, 3) development of an Artificial Intelligence approach based on Artificial Neural Network (ANN) for connecting concrete properties and model parameters at different scales, 4) development and demonstration of an ANN  for predicting macroscopic properties of concrete based on meso-scale LDPM parameters.