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® 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.

混凝土中的纤维是增强其工程性能所必需的。不同类型的纤维用作增强材料。由于低成本，可用性和环境友好性，近年来，在混凝土中使用天然纤维已引起广泛关注。在所有天然纤维中，黄麻纤维非常便宜，可在热带国家获得。这项研究评估了不同的软计算方法：RSM（响应表面方法），ANN（人工神经网络）和SVR（支持向量回归），用于开发非线性经验模型，这些模型可预测黄麻纤维增强混凝土的机械性能（抗压强度和抗拉强度）。复合材料（JFRCC）。这些性质主要取决于水灰比，黄麻纤维的长度和体积。ANN和SVR的代码是用MATLAB（R-2019a）编写的，而Minitab®18统计软件则用于通过Box-Behnken设计（RSM的实验设计）生成实验设计矩阵。基于该设计矩阵获得了JFRCC的性能数据，并将这些数据用于开发，比较和评估建议的模型。结果表明，在各种性能测量参数（例如，相关系数，残差，相对误差，平均绝对误差，均方根误差和分数偏差）的预测上，SVR模型的性能远优于ANN和RSM模型。抗张强度。基于此设计矩阵获得了JFRCC的特性数据，并将这些数据用于开发，比较和评估建议的模型。结果表明，在预测压缩性和压缩性的各种性能测量参数（例如，相关系数，残差，相对误差，平均绝对误差，均方根误差和分数偏差）方面，SVR模型的性能远优于ANN和RSM模型。抗张强度。基于此设计矩阵获得了JFRCC的特性数据，并将这些数据用于开发，比较和评估建议的模型。结果表明，在预测压缩性和压缩性的各种性能测量参数（例如，相关系数，残差，相对误差，平均绝对误差，均方根误差和分数偏差）方面，SVR模型的性能远优于ANN和RSM模型。抗张强度。