Compressive Strength Modelling of Cellulosic Fibers Mortar Composites Using Full Factorial and Artificial Neural Network

被引：0
|
作者：
Lachenani, Amina [1 ]
Bentchikou, Mohamed [1 ]
Hanini, Salah [2 ]
Megateli, Oussama [1 ]
Mekki, Mounir [1 ]
机构：
[1] Medea Univ, Dept Civil Engn, Fac Technol, Medea, Algeria
[2] Medea Univ, LBMPT, Biomat & Transport Phenomena Lab, Medea, Algeria
来源：
PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON APPLIED SMART SYSTEMS (ICASS) | 2018年
关键词：
material; cellulosic fibers; full factorial experimental design; artificial neural network<bold>; </bold>; MECHANICAL-PROPERTIES;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A methodology to study the compressive strength of hard cement mortar with recycled cellulosic fibers under different conditions based on the full factorial experimental design and artificial neural network is presented in this research work. An experimental procedure to show the parameters that affect the compressive strength as percentage of fibers, age, and compaction pressure is conducted. The effect of these parameters and their interaction effects for compressive strength response are determined using full factorial design. Statistical analysis shows that the percentage of fibers has a major effect on the compressive strength. The use of the artificial neural network approach in the prediction of compressive strength indicates a considerable correlation between the model obtained from this approach and the experimental results.<bold> </bold>
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页数：4
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