Prediction of the Strength Properties of Carbon Fiber-Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Abstract

The artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). The carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m3). The specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. The amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. The compressive and flexural strengths of the lightweight concrete were determined as the output variables. The model results were compared with the experimental results. The best results were achieved from the artificial neural network model. The accuracy of the artificial neural network model was found at 99.02% and 96.80%.