Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

Mehdi Nikoo,Farshid Torabian Moghadam,Łukasz Sadowski

doi:10.1155/2015/849126

Abstract

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

Highlights

In the last decade, a lot of attention has been paid to the application of artificial neural networks (ANNs) in determining the compressive strength of concrete [1,2,3,4,5,6,7,8,9]
In order to economize on the time and cost that laboratory methods have in determining the compressive strength of concrete, the model suggested by the ANN, which has special capability in nonlinear mapping, was used
In order to obtain the required accuracy towards determining compressive strength, the optimal evolutionary artificial neural networks (EANNs) structures consisting of optimal geometry in the number of hidden neurons and layers along with optimal learning algorithm relying on the high power of genetic algorithms (GA) were presented

Summary

Introduction

A lot of attention has been paid to the application of artificial neural networks (ANNs) in determining the compressive strength of concrete [1,2,3,4,5,6,7,8,9]. In reducing the cost and saving time in the class of compressive strength’s determination problems, cascade correlation type of ANN offered quick learning with slightly accurate performance for capturing the intrinsically nonlinear nature of patterns in the concrete properties [14]. The identification of defect in the structure and compressive strength of concrete is intrinsically a pattern-study issue, and for this purpose the EANNs have acted very powerfully [19, 20]

Objectives

Methods

Findings

Conclusion