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Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC. 

This paper aims to clarify the influence of different types of fly ash on the mechanical properties and self-healing behavior of Engineered Cementitious Composite (ECC). Five types of fly ash with different chemical and physical properties were used in ECC mixtures. The fly ash to cement ratio was fixed at 3.0. The compressive and uniaxial tensile tests were conducted to evaluate the influence of fly ash type on mechanical properties. The permeability test was used to assess self-healing behavior of ECCs with different types of fly ash. The microtopography and chemical characteristics of the self-healing products in the crack were observed and examined by scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS). The fly ash with relatively higher calcium content and smaller particle size was found conducive to a higher compressive strength. The lower combined Al2O3 and CaO content of this fly ash, however, was found to enhance the tensile strain capacity. Furthermore, high calcium fly ash accelerates the self-healing process of ECC for the same pre-damaged level. The self-healing product was a mixed CaCO3/C-S-H system with the CaCO3 as the main ingredient.