Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete

被引：57

作者：

Mozumder, Ruhul Amin ^{[1
]}

Roy, Biswajit ^{[1
]}

Laskar, Aminul Islam ^{[1
]}

机构：

[1] Natl Inst Technol, Dept Civil Engn, Silchar 788010, India

来源：

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2017年 / 42卷 / 03期

关键词：

Support vector machine; Artificial neural network; Fiber reinforced polymer composites; Compressive strength; COMPRESSIVE BEHAVIOR; JACKETED CONCRETE; FAILURE CRITERION; NEURAL-NETWORKS; COLUMNS; MODEL; SQUARE;

D O I：

10.1007/s13369-016-2340-y

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Support vector machine regression (SVR) technique was used for predicting the uniaxial compressive strength of FRP confined concrete. Two different SVR models, viz. SVR model for carbon fiber reinforced polymer confined concrete and SVR model for glass fiber reinforced polymer confined concrete, were developed. The prediction efficacy of developed SVR models was compared with that of ANN and existing empirical FRP strength prediction models. Furthermore, for assessing variable contributions in strength prediction process, a sensitivity analysis is also presented. The study showed that SVR could be a powerful alternative physical tool for strength prediction of FRP confined concrete.

引用

页码：1129 / 1146

页数：18

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