ABSTRACT

The present study is a continuation of a previously published by the authors. In Part I, mechanical data of Alkaline-treated natural fibers-reinforced plaster was examined using response surface methodology (RSM) statistics, while in this work (Part II), the data are analyzed using Artificial neural network (ANN) statistics. Many studies have focused on substituting synthetic fibers by natural fibers in plaster matrix. The present study reports ANN statistics data analysis on the mechanical properties of plaster and natural fibers. ANN and RSM methods are employed and their findings discussed. During this study, flexural properties of sodium hydroxide solution (NaOH)-treated natural fibers in plaster mortars were investigated. Experimental data effects were established by analysis of variance (ANOVA) method. Fiber length optimization and NaOH percentage treatment of fibers were also performed utilizing desirability function DF to obtain maximum flexural properties. Experimental results showed good agreement with those obtained statistically. The ANN and RSM models also have correlated highly to the experimental data. However, the ANN model proves more to be accurate.

摘要

本研究是作者先前发表的研究的延续。在第一部分中，使用响应面法 (RSM) 统计检查碱处理的天然纤维增强石膏的力学数据，而在本工作（第二部分）中，使用人工神经网络 (ANN) 统计分析数据。许多研究集中在石膏基质中用天然纤维替代合成纤维。本研究报告了关于石膏和天然纤维机械性能的 ANN 统计数据分析。使用 ANN 和 RSM 方法并讨论了它们的发现。在这项研究中，研究了石膏砂浆中氢氧化钠溶液 (NaOH) 处理的天然纤维的弯曲性能。通过方差分析（ANOVA）方法建立实验数据效果。还利用合意性函数 DF 对纤维进行了纤维长度优化和 NaOH 百分比处理，以获得最大的弯曲性能。实验结果与统计结果显示出良好的一致性。ANN 和 RSM 模型也与实验数据高度相关。然而，人工神经网络模型被证明更准确。