Geometric Semantic Genetic Programming Algorithm and Slump Prediction
This work addresses slump prediction for recycled concrete in construction, which is an incremental improvement over existing methods.
The researchers tackled the problem of predicting slump in recycled concrete, which conventional methods struggle with due to its complex composition, and developed a model using geometric semantic genetic programming (GSGP) that achieved higher accuracy and reliability compared to other nonlinear prediction models.
Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we propose a recycled concrete slump prediction model based on geometric semantic genetic programming (GSGP) and combined it with recycled concrete features. Tests show that the model can accurately predict the recycled concrete slump by using the established prediction model to calculate the recycled concrete slump with different mixing ratios in practical projects and by comparing the predicted values with the experimental values. By comparing the model with several other nonlinear prediction models, we can conclude that GSGP has higher accuracy and reliability than conventional methods.