论文标题
高相关变量创建器机器：混凝土抗压强度的预测
High correlated variables creator machine: Prediction of the compressive strength of concrete
论文作者
论文摘要
在本文中，我们引入了一种新型的混合模型，用于使用超声脉冲速度（UPV）和反弹数（RN）预测混凝土的抗压强度。首先，收集了来自8项UPV和回弹锤（RH）测试的516个数据。然后，使用高相关变量创建器计算机（HVCM）来创建与输出具有更好相关性并改善预测模型的新变量。三个单一模型，包括分步回归（SBSR），基因表达编程（GEP）和自适应神经模糊推理系统（ANFIS）以及三种混合模型，即HCVCM-SBSR，HCVCM-SBSR，HCVCM-GEP和HCVCM-ANFIS，用于预测CONCRETE的压缩强度。计算评估和比较模型的统计参数和误差项，例如确定系数，均方根误差（RMSE），归一化均方根误差（NMSE），分数偏差，最大正误和最大误差以及平均绝对百分比误差（MAPE），以评估和比较模型。结果表明，HCVCM-ANFI可以比所有其他模型更好地预测混凝土的抗压强度。 HCVCM在确定系数中将ANFI的准确性提高了5％，RMSE为10％，NMSE的3％，MAPE的20％，最大负误差为7％。
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.
