New Opportunity: Materials Genome Strategy for Engineered Cementitious Composites (Ecc) Design

Abstract

Engineered Cementitious Composites (ECC) is considered as one of the most promising cement-based materials in construction industry. Despite the success of micromechanical design of ECC with robust strain-hardening performance, a performance-based design of ECC with specific mechanical properties is still lacking. Here, we introduce a Materials Genome Initiative (MGI)-oriented strategy to design ECC through properties prediction and optimization design based on machine learning (ML). The genomic characteristics of ECC materials were summarized and analyzed, demonstrating that the mechanical properties of ECC could be closely related with its mix compositions and microstructure features. A data-driven ML framework for the design of ECC was developed, and a preliminary study using ML and multi-objective optimization techniques in designing ECC mix proportions that meet specific requirements was conducted. As a result, our proposed ML framework could achieve reliable properties prediction and rapid, quantitative, and intelligent design for ECC, which were further validated by the experiments. This study initiates the concept of MGI and lays the groundwork of an AI-guided performance-based design of ECC, while further research remains.

Keywords: Engineered cementitious composites, Materials genome strategy, Machine learning, Multi-objective optimization