Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach

Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization...

Optimization of vascular structure of self-healing concrete using deep neural network (DNN)

In this paper, optimization of vascular structure of self-healing concrete is performed with deep neural network (DNN). An input representation method is proposed to effectively represent the concrete beams with 6 round pores in the middle span as well as benefit the optimization process. To investigate the feasibility of using DNN for...

The role of eigen-stresses on apparent strength and stiffness of normal, high strength, and ultra-high performance fibre reinforced concrete

Concrete is characterized in terms of its engineering properties, mainly strength and stiffness, which are subsequently used in structural design. However, the apparent (i.e., measured) concrete properties are not intrinsic but dependent on the conditions under which the measurement is performed. Herein a combined experimental and numerical...

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...

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...

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...

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...

Lattice Fracture Model for Concrete Fracture Revisited: Calibration and Validation

The lattice fracture model is a discrete model that can simulate the fracture process of cementitious materials. In this work, the Delft lattice fracture model is reviewed and utilized for fracture analysis. First, a systematic calibration procedure that relies on the combination of two uniaxial tensile tests is proposed to determine the input...