Neuro Computing Techniques for Prediction of Compressive Strength of Concrete

Abstract

Neuro-computing techniques are being widely used these days and these techniques are considered good for forecasting applications. In recent years, these techniques have also been applied to many civil-engineering problems with reasonable success. These techniques are particularly useful in applications where the complexity of the data or task makes the design of such a function impractical. As the development of concrete strength is a complex non-linear process, depending upon many parameters, it is a problem well suited for applying neuro-computing techniques. In this work, we focused on the prediction of compressive strength of concrete with the help of neuro-computing techniques. Data for this work has been taken from the experiments conducted by Kumar (2003). For generating a reliable data bank on concrete compressive strength, he considered five parameters, namely, water-cementitious ratio, cementitious content, water content, workability and curing ages in his experiments. He performed all experiments in controlled laboratory conditions. A set of 15 cubes for each of the mixes so proportioned were cast and tested after 28, 56 and 91 days of curing. Thus, an extensive data bank for analyzing the compressive strength of concrete has been used in the present work. Factor analysis has been performed on the data in order to decide variables for predicting compressive strength of concrete with the help of SPSS and investigation, reveal that water-cementitious ratio is the leading predictor variable. Regression models have been also developed in this work. These have been developed: (i) To analyze the effect of workability on compressive strength; (ii) To analyze the effect of FA on compressive strength; and (iii) For predicting compressive strength of concrete with three different aggregate zones, i.e., Zone-A, Zone-B and Zone-C with and without FA. The regression models developed can predict compressive strength of various mixes very efficiently.