An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels

作     者：Yuanjie Zhi Tao Yang Dongmei Fu Yuanjie Zhi;Tao Yang;Dongmei Fu

作者机构：School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center of Industrial Spectrum ImagingUniversity of Science and Technology BeijingBeijing 100083China 

出 版 物：《Journal of Materials Science & Technology》 (材料科学技术（英文版）)

年 卷 期：2020年第47卷第14期

页      面：202-210页

核心收录：

学科分类：080503[工学-材料加工工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：financially supported by the National Key R&D Program of China(No.2017YFB0702100) the National Natural Science Foundation of China(No.51871024) 

主　　题：Random forests Deep forest model Low-alloy steels Outdoor atmospheric corrosion Prediction and data-mining 

摘      要：The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion ***,we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment ***,we give the proposed methods process,including random forests-K nearest neighbors(RF-WKNNs)and ***,we use the collected datasets to verify the performance of the proposed *** results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network(ANN),support vector regression(SVR),random forests(RF),and cascade forests(cForest),the proposed method can obtain the best prediction *** addition,the method can predict the corrosion rates with variations of any one single environmental variable,like pH,temperature,relative humidity,SO2,rainfall or Cl-.By this way,the threshold of each variable,upon which the corrosion rate may have a large change,can be further obtained.