Deep Learning from Physicochemical Information for AI-Assisted Design of Low-Carbon Cost Effective

Title: Deep Learning from Physicochemical Information for AI-Assisted Design of Low-Carbon Cost Effective Concrete

Presented By: Yi Bao, Stevens Institute of Technology

Description: Existing machine learning-based approaches to investigate and design concrete mainly use the mixture design variables to predict concrete properties and do not consider the physicochemical properties of ingredients such as the particle size distribution and chemical composition of various binders and aggregates. This paper presents an approach to discover the intrinsic relationships between the physicochemical properties of the ingredients and mechanical properties of concrete. Specifically, this research creates an artificial language to represent concrete mixtures and the physicochemical information of their ingredients, develops a feature extraction method based on character-level N-grams, and proposes a method to configure deep learning models automatically. The proposed approach has been implemented to predict the compressive strength of complex concrete mixtures, assess the importance of variables, and discover chemical reactions, showing high accuracy and high generalizability. This research advances the capabilities of understanding the underlying reactions for complex concrete mixtures and designing low-carbon cost-effective concrete.