Autogenous deformation-induced stress evolution in cementitious materials considering viscoelastic properties: A review of experiments and models

Early-age cracking risk induced by autogenous deformation is high for cementitious materials of low water-binder ratios. The autogenous deformation, viscoelastic properties, and stress evolution are three important factors for understanding and quantifying the early-age cracking risk. This paper systematically reviewed the experimental and...

Bayesian Inverse Modelling of Early-Age Stress Evolution in GGBFS Concrete Due to Autogenous Deformation and Aging Creep

Stress evolution of restrained concrete is directly related to early-age cracking (EAC) potential of concrete, which is a tricky problem that often happens in engineering practice. Due to the global objective of carbon reduction, Ground granulated blast furnace slag (GGBFS) concrete has become a more promising binder comparing with Ordinary...

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...

Stress evolution in restrained GGBFS concrete due to autogenous deformation: bayesian optimization of aging creep

Stress evolution of restrained concrete is a significant direct index in early-age cracking (EAC) analysis of concrete. This study presents experiments and numerical modelling of the early-age stress evolution of Ground granulated blast furnace slag (GGBFS) concrete, considering the development of autogenous deformation and creep. Temperature...

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...

The Influence of Autogenous Shrinkage and Creep on the Risk of Early Age Cracking

The study aims to investigate the mechanism of early-age cracks in different massive concrete structures (i.e. tunnels, bridge foundations and underground parking garages), with the objective of answering the following three specific questions: <br/><br/>1) How does the parameters of concrete proportion mix (e.g. w/c ratio, cementitious...