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

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.

Automated summary

Modern AI-assisted approaches have revolutionized our understanding of concrete and composite materials. This study introduces self-supervised learning (SSL) to overcome the limitation of labeled data in concrete material characterization. A generalized SSL-based framework is proposed, which demonstrates its robustness and ability to predict concrete properties with minimal data. The SSL model achieves comparable performance to supervised learning with only 5% of the data, and it is also easy to implement.