Prediction of Rubber Fiber Concrete Strength Using Extreme Learning Machine - Zhang, Jingkui; Xu, Juncai; Liu, Changshun; Zheng, Ji (Frontiers in Materials, 2021)
The conventional design method of concrete mix ratio relies on a large number of tests for trial mixing and optimization, and the workload is massive. It is challenging to cope with today's diverse raw materials and the concrete's specific performance to fit to the development of modern concrete. For innovating the design method of concrete mix ratio and effectively using the various complex novel raw materials, the traditional mix ratio test method can be replaced with the intelligent optimization algorithm, and the concrete performance prediction can be realized rapidly and accurately. The mixed ratio of the rubber fiber concrete was designed with its 28d strength test. Then the range and variance analysis of the orthogonal test results was carried out to determine the optimal mix ratio and its influencing factors. The amount of rubber, rubber particle size, and polymer propylene fiber content and sand content, the four influencing factors, were used as the input variable. The 28d compressive, splitting tensile and flexural strength, three types of concrete strength, were used as output variables to establish a strength prediction model of the rubber fiber concrete based on the extreme learning machine (ELM). For verifying the ELM prediction model's performance, this paper has conducted a comparison experiment between this model and other intelligent algorithm models. The results show that the model has the advantages of high accuracy and high generalization ability compared with other algorithm models such as conventional neural networks. It can be used as an effective method for predicting the concrete performance. The method allows for the innovation and development of concrete mixing technology.