期刊
ADVANCES IN ENGINEERING SOFTWARE
卷 149, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2020.102887
关键词
Jute fiber; Modeling; Response surface methodology; Artificial neural networks; Support vector regression; Reinforced concrete
类别
资金
- Deanship of Scientific Research, University of Bahrain [10/2014]
The fibers in concrete is necessary to enhance its engineering properties. Different types of fibers are used as a reinforcing material. Due to low cost, availability, and environmentally friendly, in recent years, the use of natural fibers in concrete has increased attention widely. Among all the natural fibers, jute fibers are very cheap and available in tropical countries. This study assesses different soft computing approaches: RSM (Response Surface Methodology), ANN (Artificial Neural Networks) and SVR (Support Vector Regression) for development of nonlinear empirical models that predict the mechanical properties (compressive and tensile strengths) of Jute Fiber Reinforced Concrete Composites (JFRCC). These properties are mainly dependent on water-cement (W/C) ratio, length and volume of jute fiber. The codes for ANN and SVR were written in MATLAB (R-2019a), while Minitab (R) 18 statistical software was used for generating experimental design matrix via Box-Behnken design (an experimental design for RSM). The data for the properties of JFRCC were obtained based on this design matrix and these data were utilized to develop, compare and evaluate the suggested models. The results indicate that SVR model performs much better than ANN and RSM models with respect to various performance measuring parameters (e.g., correlation coefficient, residual, relative error, mean absolute error, root mean squared error, and fractional bias) for predicting both compressive and tensile strengths.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据