Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction

Abstract

An orthogonal test method was used to do sensibility analysis on the compressive strength and splitting strength of hybrid fiber-reinforced recycled aggregate concrete (HyFRAC). And a prediction model of compressive strength of HyFRAC based on Convolutional Neural Network (CNN) was proposed. The results show the ratio of recycled brick aggregate (RBA) to recycled concrete aggregate (RCA) has been proved the greatest influence on the compressive strength and splitting tensile strength of HyFRAC, followed by the water reducing agent content, finally the ratio of glass fiber (GF) to polypropylene fiber (PF). When RBA/RCA = 2/8, GF/PF = 7/3, and water reducing agent content is 0%, the compressive strength and splitting tensile strength of HyFRAC are the highest. According to JGJ/T10-2011, when RBA/RCA ≤ 6/4 and water reducing agent content ≥ 0.4%, the HyFRAC slump meets the 50m pumping height requirement; when RBA/RCA ≤ 4/6 and water reducing agent content ≥ 0.6%, the HyFRAC slump meets the 100m pumping height requirement. Compared to back propagation (BP) neural network model and multiple linear regression model, CNN model is more efficient in estimating the compressive strength of HyFRAC. The average relative errors and max relative errors of CNN model are 1.98% and 4.12%, respectively.