基于SVM的粉煤灰混凝土抗氯离子渗透性预测方法

提出一种基于支持向量机(SVM)的粉煤灰混凝土氯离子渗透性新型智能预测模型,并给出了相应的步骤和算法。通过该模型分析了水胶比、水泥用量、用水量及粉煤灰掺入量4个因素对粉煤灰混凝土渗透性的影响。在此基础上,能利用有限的试验数据方便地由不同的配比对所浇注混凝土渗透性进行预测,有助于准确认识混凝土耐久性随配比参数的变化规律。通过具体实例及与BP预测方法效果对比表明,该模型构造速度高2~3个数量级,预测精度高5倍左右。因此该模型在混凝土性能预测和优化设计中具有广阔的应用前景。

SVM-Based Intelligent Method for Prediction of Chloride Permeability in Fly-Ash Concrete

A novel prediction model for chloride permeability in fly ash concrete based on support vector machine(SVM)was proposed.The design steps and learning algorithm were also hiven.Four main factors of the water and cement ratio,complex scoria,cement and water quantity,which influence the chloride permeability in fly ash concrete,were,analyzed with the proposed model.Based on this,the chloride permeability in fly ash concrete by different proportioning can be conveniently predicted with limited test data.The practical experiment results show that the speed of this SVM model is 10 or 100 times,while the prediction errors are 80% less than,that of BP model,therefore,this model has bright application prospect in prediction of the performance and optimal design of concrete.