TY - JOUR
T1 - Deep learning from physicochemical information of concrete with an artificial language for property prediction and reaction discovery
AU - Mahjoubi, Soroush
AU - Barhemat, Rojyar
AU - Meng, Weina
AU - Bao, Yi
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - Artificial language
KW - Concrete properties
KW - Deep learning
KW - Physicochemical information
KW - Reaction discovery
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U2 - 10.1016/j.resconrec.2023.106870
DO - 10.1016/j.resconrec.2023.106870
M3 - Article
AN - SCOPUS:85146093286
SN - 0921-3449
VL - 190
JO - Resources, Conservation and Recycling
JF - Resources, Conservation and Recycling
M1 - 106870
ER -