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, we collect 516 datasets from 8 studies of UPV and Rebound Hammer (RH) tests. Then, we propose the High Correlated Variables Creator Machine (HCVCM) to create the new variables that have a better correlation with the output in order to improve the prediction models. Three single models, including a Step-By-Step Regression (SBSR), Gene Expression Programming (GEP), an Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as three hybrid models, i.e. HCVCM-SBSR, HCVCM-GEP, and HCVCM-ANFIS are employed to predict the compressive strength of concrete. The statistical parameters and error terms such as the coefficient of determination, the Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), fractional bias, the maximum positive and negative errors, and the Mean Absolute Percentage Error (MAPE) are computed to evaluate 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 the RMSE, 3% in the NMSE, 20% in MAPE, and 7% in the maximum negative error.

在本文中，我们介绍了一种新的混合模型，该模型可使用超声脉冲速度（UPV）和回弹数（RN）来预测混凝土的抗压强度。首先，我们从8个UPV和回弹锤（RH）测试的研究中收集了516个数据集。然后，我们提出了高相关变量创建者机器（HCVCM）来创建与输出具有更好相关性的新变量，以改善预测模型。三个单一模型，包括逐步回归（SBSR），基因表达编程（GEP），自适应神经模糊推理系统（ANFIS）以及三个混合模型，即HCVCM-SBSR，HCVCM-GEP和HCVCM-ANFIS用于预测混凝土的抗压强度。统计参数和误差项，例如确定系数，均方根误差（RMSE），计算归一化均方误差（NMSE），分数偏差，最大正负误差和平均绝对百分比误差（MAPE）以评估模型。结果表明，HCVCM-ANFIS可以比所有其他模型更好地预测混凝土的抗压强度。HCVCM将ANFIS的准确度提高了5％（确定系数），10％（RMSE），3％（NMSE），20％（MAPE）和7％（最大负误差）。