Deep learning from physicochemical information of concrete with an artificial language for property prediction and reaction discovery

Soroush Mahjoubi, Rojyar Barhemat, Weina Meng, Yi Bao

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish
Article number106870
JournalResources, Conservation and Recycling
Volume190
DOIs
StatePublished - Mar 2023

Keywords

  • Artificial language
  • Concrete properties
  • Deep learning
  • Physicochemical information
  • Reaction discovery

Fingerprint

Dive into the research topics of 'Deep learning from physicochemical information of concrete with an artificial language for property prediction and reaction discovery'. Together they form a unique fingerprint.

Cite this