Author:
Wang Lei,Wu Xianguo,Chen Hongyu,Zeng Tiemei
Abstract
Abstract
The durability of concrete has a significant impact on the service life. Impermeability is one of the important indicators of concrete durability. It is of great significance to quickly and reasonably predict the impermeability of concrete. This paper combines random forest and support vector machine (RF-SVM) methods. Taking a highway project as the research background, 11 factors were selected as the impact index of concrete impermeability, and the chloride permeability coefficient was used as the evaluation index of concrete impermeability. After random forest index screening, six factors including water-binder ratio, cement dosage, cement strength, fine aggregate, water-reducing agent and coarse aggregate were selected to construct a support vector machine model to predict the impermeability of concrete. The prediction results of the RF-SVM model are compared with the BP neural network model and the support vector machine model without index screening. The results show that the RF-SVM model has higher prediction accuracy and better fitting effect, which provides an effective method for the prediction of concrete impermeability.
Reference14 articles.
1. A New Intelligent Medical Decision Support System Based on Enhanced Hierarchical Clustering and Random Decision Forest for the Classification of Alcoholic Liver Damage, Primary Hepatoma, Liver Cirrhosis, and Cholelithiasis;Singh,2018
2. Diffusion behavior of chloride ions in concrete;Zhang;Cement and Concrete Research,1996
3. Role of nano-SiO2 in improving the microstructure and impermeability of concrete with different aggregate gradations;Xiao;Construction and Building Materials,2018
4. The investigation of factors affecting the water impermeability of inorganic sodium silicate-based concrete sealers;Jiang;Construction and Building Materials,2015
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献