A survey of machine learning-based approaches for the prediction and design of concrete freeze–thaw durability

This publication has 107 references indexed in Scilit:

• Prediction of freeze-thaw deterioration of hydraulic concrete exposed to freeze-thaw temperature and saturation level
  Mechanics of Time-Dependent Materials, 2025
• Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
  Archives of Computational Methods in Engineering, 2025
• Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing
  Advanced Materials, 2024
• Peak and ultimate stress-strain model of confined ultra-high-performance concrete (UHPC) using hybrid machine learning model with conditional tabular generative adversarial network
  Applied Soft Computing, 2024
• Interpretable and explainable machine learning: A methods‐centric overview with concrete examples
  WIREs Data Mining and Knowledge Discovery, 2023
• Microstructure and Service-Life Prediction Models of Aeolian Sand Concrete Under Freeze–Thaw Damage
  International Journal of Pavement Research and Technology, 2023
• Machine learning approach in investigating carbonation depth of concrete containing Fly ash
  Structural Concrete, 2022
• Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)
  Applied Soft Computing, 2022
• Machine learning in materials science
  InfoMat, 2019
• The international cooperative test of the critical degree of saturation method of assessing the freeze/thaw resistance of concrete
  Materials and Structures, 1977