Prediction of Compressive Strength of Geopolymer Concrete Based on Support Vector Machine and Modified Cuckoo Algorithm

Abstract

Coal gangue-based geopolymer concrete is an environmentally friendly material made from coal gangue, solid waste from the coal mine. Compressive strength is one of the most important indexes for concretes. Different oxide contents of coal gangue will affect the compressive strength of the geopolymer concrete directly. However, there is little study on the relationship between oxide contents and compressive strength of the geopolymer concrete. Experiments are commonly used methods of determining the compressive strength of concretes, including geopolymer concrete, which is time-consuming and inefficient. Therefore, in the work here, a support vector machine and a modified cuckoo algorithm are utilized to predict the compressive strength of geopolymer concrete. An orthogonal factor is introduced to modify the traditional cuckoo algorithm to update new species and accelerate computation convergence. Then, the modified cuckoo algorithm is employed to optimize the parameters in the support vector machine model. Then, the compressive strength predictive model of coal gangue-based geopolymer concrete is established with oxide content of raw materials as the input and compressive strength as the output of the model. The compressive strength of coal gangue-based geopolymer concrete is predicted with different oxide contents in raw materials, and the effects of different oxide contents and oxide combinations on compressive strength are studied and analyzed. The results show that the support vector machine and the modified cuckoo algorithm are valid and accurate in predicting the compressive strength of geopolymer concrete. And, coal gangue geopolymer concrete compressive strength is significantly affected by oxide contents.