Research on Statistical Characteristics and Prediction Methods of Ferronickel Slag Pervious Concrete Performance with Different Sizes of Aggregate and Mixtures

Abstract

In the exploration of sustainable construction materials, the application of ferronickel slag (FNS) in creating pervious concrete has been investigated, considering its potential to meet the dual requirements of mechanical strength and fluid permeability. To elucidate the statistical properties and models for predicting the performance of FNS-composited pervious concrete with different sizes of aggregates and mixtures, a series of experiments, including 54 kinds of mixtures and three kinds of aggregate, were conducted. The focus was on measuring the compressive strength and the permeability coefficient. The results indicate that the compressive strength of pervious concrete decreases with the increase in aggregate size, while the permeability coefficient increases with the increase in aggregate size. Through normalization, the variability of these properties was quantitatively analyzed, revealing coefficients of variation for the concrete’s overall compressive strength and the permeability coefficient at 0.166, 0.132, and 0.150, respectively. Predictive models were developed using machine learning techniques, such as Linear Regression, Support Vector Machines, Regression Trees, and Gaussian Process Regression. These models demonstrated proficiency in forecasting the concrete’s compressive strength and permeability coefficient.

Keywords

• ferronickel slag
• pervious concrete
• compressive strength
• permeability coefficient
• aggregate
• machine learning