TY - JOUR
T1 - Machine learning applications in designing cementitious materials
AU - Dang, Shichen
AU - Fang, Hu
AU - Yao, Yao
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/6
Y1 - 2025/6
N2 - This review explores the development and application of machine learning (ML) algorithms in cementitious materials, and some highlighting and potential ML-related applications are emphasized. This review takes the commonly employed ML algorithms and training strategies as clues, and it covers commonly used ML models, including Neural Networks based (NN-based) algorithms and Classification and Regression Trees based (CART-based) algorithms, along with transfer learning concepts. Then, the impact of ML on material mechanics is analyzed, emphasizing improved reliability in phenomenal analysis, composite design, and predictive modeling of material properties. The role of ML algorithms in visual material identification and physics-informed modeling is discussed, along with applications in model interpretability, physical constraints, in-situ damage identification. The integration of Large Language Models (LLMs) is also introduced as an emerging research avenue. By providing an overview of ML's role in material mechanics, this review offers insights for researchers and engineers in the field.
AB - This review explores the development and application of machine learning (ML) algorithms in cementitious materials, and some highlighting and potential ML-related applications are emphasized. This review takes the commonly employed ML algorithms and training strategies as clues, and it covers commonly used ML models, including Neural Networks based (NN-based) algorithms and Classification and Regression Trees based (CART-based) algorithms, along with transfer learning concepts. Then, the impact of ML on material mechanics is analyzed, emphasizing improved reliability in phenomenal analysis, composite design, and predictive modeling of material properties. The role of ML algorithms in visual material identification and physics-informed modeling is discussed, along with applications in model interpretability, physical constraints, in-situ damage identification. The integration of Large Language Models (LLMs) is also introduced as an emerging research avenue. By providing an overview of ML's role in material mechanics, this review offers insights for researchers and engineers in the field.
KW - Cementitious
KW - Composite proportioning
KW - Machine learning
KW - Material mechanics
KW - Regressive analysis
UR - http://www.scopus.com/inward/record.url?scp=86000615399&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2025.106125
DO - 10.1016/j.autcon.2025.106125
M3 - 文献综述
AN - SCOPUS:86000615399
SN - 0926-5805
VL - 174
JO - Automation in Construction
JF - Automation in Construction
M1 - 106125
ER -