Chen Huaicheng, Qian Chunxiang, Liang Chengyao, Kang Wence
School of Materials Science and Engineering, Southeast University, Nanjing, Jiangsu province, China.
Research Institute of Green Construction Materials, Southeast University, Nanjing, Jiangsu province, China.
PLoS One. 2018 Jan 18;13(1):e0191370. doi: 10.1371/journal.pone.0191370. eCollection 2018.
In this paper, a support vector machine (SVM) model which can be used to predict the compressive strength of mortars exposed to sulfate attack was established. An accelerated corrosion test was applied to collect compressive strength data. For predicting the compressive strength of mortars, a total of 638 data samples obtained from experiment was chosen as a dataset to establish a SVM model. The values of the coefficient of determination, the mean absolute error, the mean absolute percentage error and the root mean square error were used for evaluating the predictive accuracy. The main factors affecting the predicted compressive strength were obtained by sensitivity analysis. A SVM model was calibrated, validated, and finally established. Moreover, the performance of the SVM model was compared to an artificial neural network (ANN) model. Results show that the prediction values from the SVM model were close to the experimental values; the main factors sensitive to concrete compressive strength were exposure time, water-cement ratio and sulfate ions; the performance of the SVM model was better than the ANN model. The SVM model developed in this study can be potentially used for predicting the compressive strength of cement-based materials servicing in harsh environments.
本文建立了一种可用于预测遭受硫酸盐侵蚀的砂浆抗压强度的支持向量机（SVM）模型。采用加速腐蚀试验来收集抗压强度数据。为了预测砂浆的抗压强度，从实验中获得的总共638个数据样本被选作数据集来建立SVM模型。决定系数、平均绝对误差、平均绝对百分比误差和均方根误差的值被用于评估预测精度。通过敏感性分析获得了影响预测抗压强度的主要因素。对一个SVM模型进行了校准、验证，最终建立起来。此外，还将SVM模型的性能与人工神经网络（ANN）模型进行了比较。结果表明，SVM模型的预测值与实验值接近；对混凝土抗压强度敏感的主要因素是暴露时间、水灰比和硫酸根离子；SVM模型的性能优于ANN模型。本研究中开发的SVM模型可潜在地用于预测在恶劣环境中服役的水泥基材料的抗压强度。
