High-efficient low-cost characterization of composite material properties using domain-knowledge-guided self-supervised learning

Abstract

Modern AI-assisted approaches have revolutionized our abilities to better understand the properties of concrete and composite materials. However, current machine learning models such as supervised learning (SL) models usually require a large amount of training data to feed the model. Here, we introduce self-supervised learning (SSL) to address the issue of lacking labeled data in concrete material characterization. We propose a generalized SSL-based framework with domain knowledge and demonstrate its robustness to predict the properties of a commonly-used composite material (concrete) with the fewest data possible. Our numerical results show that the performance of the proposed SSL model can match the commonly-used supervised learning model with only 5 % of data, and the SSL model is also proven with ease of implementation. Our study paves the way to expand further the usability of machine learning tools for composite material fields and the broader material science community.