Prediction of compressive strength of rice husk ash concrete: A comparison of different metaheuristic algorithms for optimizing support vector regression

Concrete made from rice husk ash (RHA) is stronger and more durable than normal concrete. It can also help reduce greenhouse gas emissions. A feasible prediction model to rapidly determine the compressive strength of RHA concrete can save resources and time, and the properties and characteristics of RHA concrete can be more accurately determined. In this study, the compressive strength of RHA concrete is predicted by using support vector regression (SVR) in conjunction with three optimization algorithms, namely firefly algorithm (FA), particle swarm optimization (PSO), and grey wolf optimization (GWO). The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), a10-index and adjusted R2 are used to assess the predictive accuracy of models. The results show that all the three optimization algorithms can significantly improve the prediction performance of support vector regression. The prediction accuracy is also substantially higher than that of the classical machine learning models like the random forest (RF) model and the back propagation neural network (BPNN) model. The FA-SVR model with R2 values of 0.9544 and 0.9614, adjusted R2 values of 0.9530 and 0.9560, RMSE values of 3.7506 and 3.6571, MAE values of 2.2880 and 3.0732, and a10-index values of 0.8584 and 0.8793 corresponding to the training and test sets, respectively. The FA-SVR model outperforms the other two hybrid models in terms of prediction performance. The sensitivity analysis reveals that the fine aggregate content is the key factor in predicting the compressive strength of RHA concrete.

Automated summary

Concrete made from rice husk ash (RHA) is stronger and more durable than normal concrete, and can help reduce greenhouse gas emissions. In this study, the compressive strength of RHA concrete is predicted using support vector regression (SVR) combined with three optimization algorithms. The results show that all three optimization algorithms improve the prediction performance of SVR, with the firefly algorithm (FA) achieving the best results.