Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste

This study aims to provide an efficient alternative for predicting creep modulus of cement paste based on Deep Convolutional Neural Network (DCNN). First, a microscale lattice model for short-term creep is adopted to build a database that contains 18,920 samples. Then, 3 DCNNs with different consecutive convolutional layers are built to learn...

Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete

This study aims to provide an efficient and accurate machine learning (ML) approach for predicting the creep behavior of concrete. Three ensemble machine learning (EML) models are selected in this study: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting Machine (LGBM). Firstly, the creep data in...

Prediction of the autogenous shrinkage and microcracking of alkali-activated slag and fly ash concrete

This study aims to predict the autogenous shrinkage of alkali-activated concrete (AAC) based on slag and fly ash. A variety of analytical and numerical models are available for the prediction of autogenous shrinkage of ordinary Portland cement (OPC) concrete, but these models are found to show dramatic discrepancies when applied for AAC due...