Orthogonal Test and Convolution Neural Network Prediction of Hybrid Fiber Recycled Brick Aggregate Concrete

Orthogonal test was used to study the influence of the three factors of recycled aggregate ratio, hybrid fiber ratio and the amount of water reducing agent on the mechanical properties sensitivity of hybrid fiber recycled brick aggregate concrete, and the experimental results were predicted and analyzed by convolution neural network. The results show that the ratio of recycled brick aggregate to recycled concrete aggregate has the greatest influence on the compressive and splitting tensile strength of hybrid fiber recycled brick aggregate concrete, followed by the amount of water reducing agent, and finally the ratio of glass fiber to polypropylene fibers. The compressive strength and splitting tensile strength increase with the decrease of the ratio of recycled brick aggregate to recycled concrete aggregate, and decrease with the increase of the amount of water reducing agent. The convolution neural network model established in this paper has high prediction accuracy and can be used to analyze the test results with variable parameters. © 2021, Materials Review Magazine. All right reserved.