{"title":"Genetic Programming Based Formulation to Predict Compressive Strength of High Strength Concrete","authors":"G. Abdollahzadeh, E. Jahani, Zahra Kashir","doi":"10.7508/CEIJ.2017.02.001","DOIUrl":null,"url":null,"abstract":"This study introduces, two models based on Gene Expression Programming (GEP) to predict compressive strength of high strength concrete (HSC). Composition of HSC was assumed simplified, as a mixture of six components (cement, silica fume, super-plastisizer, water, fine aggregate and coarse aggregate). The 28-day compressive strength value was considered the target of the prediction. Data on 159 mixes were taken from various publications. The system was trained based on 80% training pairs chosen randomly from the data set and then tested using remaining 20% samples. Therefore it can be proven and illustrated that the GEP is a strong technique for the prediction of compressive strength amounts of HSC concerning to the outcomes of the training and testing phases compared with experimental outcomes illustrate that the.","PeriodicalId":43959,"journal":{"name":"Civil Engineering Infrastructures Journal-CEIJ","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Civil Engineering Infrastructures Journal-CEIJ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7508/CEIJ.2017.02.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 12
Abstract
This study introduces, two models based on Gene Expression Programming (GEP) to predict compressive strength of high strength concrete (HSC). Composition of HSC was assumed simplified, as a mixture of six components (cement, silica fume, super-plastisizer, water, fine aggregate and coarse aggregate). The 28-day compressive strength value was considered the target of the prediction. Data on 159 mixes were taken from various publications. The system was trained based on 80% training pairs chosen randomly from the data set and then tested using remaining 20% samples. Therefore it can be proven and illustrated that the GEP is a strong technique for the prediction of compressive strength amounts of HSC concerning to the outcomes of the training and testing phases compared with experimental outcomes illustrate that the.