文章基本信息

标题：Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN)
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作者：Tehmina Ayub ; M. Fadhil Nuruddin ; Sadaqat Ullah Khan 等
期刊名称：International Journal of Soft Computing and Software Engineering
电子版ISSN：2251-7545
出版年度：2013
卷号：3
期号：3
页码：663-667
DOI：10.7321/jscse.v3.n3.101
出版社：Advance Academic Publisher
摘要：This paper focuses on the development of a predictive model for compressive strength of concrete confined with Ferrocement using MATLAB Artificial Neural Network (ANN) approach. Data of fifty five (55) plain concrete cylinders confined with Ferrocement in three (03) ways, has been gathered from existing literature, out of which basic parameters of randomly selected nineteen (19) specimens have been used in the multilayer feed forward neural network model to develop a predictive model through training. Basic eight input parame-ters included cylinder and core dimension, no. of wire-mesh layers, wire diameter and spacing, yield strength of the wire of wire-mesh and unconfined compressive strength. After train-ing, predictive model had been tested using overall data of fifty five (55) specimens which showed excellent agreement between the results generated by the ANN predictive model and exper-imental results. Regression value (R), root mean square error (RMS) and absolute fraction of variance (V) were also calcu-lated to compare experimental and ANN predictive model re-sults which also showed better performance of the ANN pre-dictive model.
关键词：compressive strength; confinement; ferrocement; wire-mesh layers; artificial neural network