Multi-scale theoretical modeling with molecular simulation framework for fly ash-based high-performance concrete.

Area of Science:

• Materials Science
• Computational Modeling
• Civil Engineering

Background:

• Current fly ash concrete models lack accuracy due to oversimplification.
• Existing models fail to capture crucial material properties like heterogeneity and defect evolution.

Purpose of the Study:

• To develop a comprehensive multi-scale theoretical framework for fly ash concrete.
• To enhance the predictive accuracy of concrete properties and enable optimal design.

Main Methods:

• Hybrid Multiphase Microstructure Descriptor Modeling (HMMDM) for 3D digital twins.
• Quantum-Corrected Machine-Learned Interatomic Potential Mapping (QML IPM) for accurate force fields.
• Topological Reaction Pathway Network Modeling (TRPNM) for kinetics prediction.
• Fractal Defect Evolution Analysis using Molecular Simulation (FDEAMS) for crack propagation.
• Dynamic Multi-Scale Simulation Coupling with Feedback Optimization (DMSCF) for bridging scales.

Main Results:

• HMMDM improved porosity prediction by 15%.
• QML IPM reduced RMS force errors and improved reactivity prediction by 22%.
• TRPNM achieved <7% error in time-strength prediction.
• FDEAMS increased tensile failure zone prediction by 20%.
• DMSCF achieved <4% process lag in real-time stiffness updates.

Conclusions:

• The integrated multi-scale framework provides unprecedented predictive fidelity for fly ash concrete.
• This approach enables the design of fly ash concrete with optimized properties and performance.