Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

نویسنده

  • S. U. Khan
چکیده

Abstract This paper is an extension of the work published in year 2010 in which compressive strength of plain concrete confined with Ferrocement was estimated using mathematical models and compared with 55 experimental results. In this paper, predictive model of compressive strength for plain concrete confined with Ferrocement has been developed by using MATLAB Artificial Neural Network (ANN) simulation. Out of 55, 19 experimental results are selected for training of multilayer feed forward neural network. Comparative analysis of the results showed that compressive strength estimated by ANN predictive model are very close to the experimental results than existing theoretical models.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Compressive Strength Prediction by ANN Formulation Approach for FRP Confined Rectangular Concrete Columns

Enhancement of strength and ductility is the main reason for the extensive use of FRP (fiber reinforced polymer) jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP confined concrete for practical design purposes. This study present...

متن کامل

A COMPREHENSIVE STUDY ON THE CONCRETE COMPRESSIVE STRENGTH ESTIMATION USING ARTIFICIAL NEURAL NETWORK AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement conten...

متن کامل

Prediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks

Pervious concrete is a concrete mixture prepared from cement, aggregates, water, little or no fines, and in some cases admixtures. The hydrological property of pervious concrete is the primary reason for its reappearance in construction. Much research has been conducted on plain concrete, but little attention has been paid to porous concrete, particularly to the analytical prediction modeling o...

متن کامل

Compressive Strength of Confined Concrete in CCFST Columns

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

متن کامل

EVALUATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION MODELS

In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013