Geometric Semantic Genetic Programming Algorithm and Slump Prediction

TL;DR

This paper introduces a novel geometric semantic genetic programming model for predicting recycled concrete slump, demonstrating superior accuracy and reliability over traditional nonlinear prediction methods.

Contribution

The paper develops a new GSGP-based model tailored for recycled concrete slump prediction, integrating concrete features for improved forecasting performance.

Findings

GSGP outperforms traditional nonlinear models in accuracy

The model reliably predicts slump across different mixing ratios

Experimental results validate the model's effectiveness

Abstract

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.