论文标题
使用XGBoost的Pervious混凝土的物理和机械性能进行预测建模
Predictive Modeling of Physical and Mechanical Properties of Pervious Concrete using XGBoost
论文作者
论文摘要
透水混凝土（PC）的高渗透性使其成为用于某些应用的特殊类型。但是，PC的行为和特性的复杂性导致昂贵，耗时和能量要求实验性工作，以准确确定PC的机械和物理特性。这项研究提出了一个预测模型，可使用极端梯度提升（XGBoost）预测PC的机械和物理性能。使用使用不同的统计参数评估的四个模型预测了PC的抗压强度，拉伸强度，密度和孔隙率。这些统计量度是根平方误差（RMSE），相关系数的平方（R2），平均绝对误差（MAE）和平均绝对百分比误差（MAPE）。 XGBoost模型对这些性质的估计与实验测量一致。通过将其估计与从四个相应的支持向量回归（SVR）模型获得的估计进行比较，进一步验证了XGBoost的性能。比较表明，XGBOOST通常优于0.58、0.17、0.98和34.97的较低RMSE的SVR，而SVR分别为0.74、0.21、1.28和44.06，分别用于抗压强度，拉伸强度，孔隙率，孔隙率和密度估计。由于预测和实验获得的属性之间的高度相关性，XGBoost模型能够就PC的属性提供快速，可靠的信息，这些信息在实验上是昂贵且耗时的。对输入/预测变量的特征重要性和贡献分析表明，水泥比例是估计的PC属性中最重要和最大的因素。
High permeability of pervious concrete (PC) makes it a special type of concrete utilised for certain applications. However, the complexity of the behaviour and properties of PC leads to costly, time consuming and energy demanding experimental works to accurately determine the mechanical and physical properties of PC. This study presents a predictive model to predict the mechanical and physical properties of PC using Extreme Gradient Boost (XGBoost). The compressive strength, tensile strength, density and porosity of PC was predicted using four models evaluated using different statistical parameters. These statistical measures are the root mean squared error (RMSE), square of correlation coefficient (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE). The estimation of these properties by the XGBoost models were in agreement with the experimental measurements. The performance of XGBoost is further validated by comparing its estimations to those obtained from four corresponding support vector regression (SVR) models. The comparison showed that XGBoost generally outperformed SVR with lower RMSE of 0.58, 0.17, 0.98 and 34.97 compared to 0.74, 0.21, 1.28 and 44.06 in SVR for compressive strength, tensile strength, porosity, and density estimation respectively. Due to high correlation between the predicted and experimentally obtained properties, the XGBoost models are able to provide quick and reliable information on the properties of PC which are experimentally costly and time consuming. A feature importance and contribution analysis of the input/predictor variables showed that the cement proportion is the most important and contributory factor in the PC properties estimated.
