High correlated variables creator machine: Prediction of the compressive strength of concrete

TL;DR

This paper introduces a hybrid model combining high correlated variables creator machine with various prediction techniques to accurately forecast concrete's compressive strength, demonstrating significant improvements over individual models.

Contribution

The study presents a novel hybrid modeling approach that enhances prediction accuracy by creating better correlated variables for concrete strength estimation.

Findings

HCVCM-ANFIS outperforms other models in accuracy.

HCVCM improves ANFIS's prediction metrics significantly.

The hybrid approach reduces errors and increases reliability.

Abstract

In this paper, we introduce a novel hybrid model for predicting the compressive strength of concrete using ultrasonic pulse velocity (UPV) and rebound number (RN). First, 516 data from 8 studies of UPV and rebound hammer (RH) tests was collected. Then, high correlated variables creator machine (HVCM) is used to create the new variables that have a better correlation with the output and improve the prediction models. Three single models, including a step-by-step regression (SBSR), gene expression programming (GEP) and an adaptive neuro-fuzzy inference system (ANFIS) as well as three hybrid models, i.e. HCVCM-SBSR, HCVCM-GEP and HCVCM-ANFIS, were employed to predict the compressive strength of concrete. The statistical parameters and error terms such as coefficient of determination, root mean square error (RMSE), normalized mean square error (NMSE), fractional bias, the maximum positive and negative errors, and mean absolute percentage error (MAPE), were computed to evaluate and compare the models. The results show that HCVCM-ANFIS can predict the compressive strength of concrete better than all other models. HCVCM improves the accuracy of ANFIS by 5% in the coefficient of determination, 10% in RMSE, 3% in NMSE, 20% in MAPE, and 7% in the maximum negative error.