J. Today’s Ideas - Tomorrow’s Technol.

Concrete Mix Design Using Artificial Neural Network

Sakshi Gupta

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  • DOI Number
    https://doi.org/10.15415/jotitt.2013.11003
KEYWORDS

concrete mix, artificial neural network (ANN), 7-days strength, 14-days strength, 28-days compressive strength, fineness modulus, activation function, modeling.

PUBLISHED DATE June 2013
PUBLISHER The Author(s) 2013. This article is published with open access at www.chitkara.edu.in/publications
ABSTRACT

Concrete mix design is a process based on sound technical principles for proportioning of ingredients in right quantities. This paper demonstrates the applicability of Artificial Neural Networks (ANN) Model for approximate proportioning of concrete mixes. For ANN a trained back propagation neural network is integrated in the model to learn experimental data pertaining to predict 7, 14 and 28-day compressive strength which have been loaded into a model, containing 55 concrete mixtures. The ANN model proposed is based on 5 input parameters such as cement, sand, coarse aggregate, and water and fineness modulus. The proposed concrete mix proportion design is expected to reduce the number of trials in laboratory as well as field, saves cost of material as well as labor and also saves time as it provides higher accuracy. The concrete designed is expected to have higher durability and hence is economical.

INTRODUCTION

Concrete is the most widely used construction material because of its flowability in most complicated form i.e. its ability to take any shape while wet, and its strength development characteristics when it hardens. Concrete production is a complex process that involves the effect of several processing parameters on the quality control of concrete pertaining to workability, strength etc. These parameters are all effective in producing a single strength quantity of compressive strength.

Artificial intelligence has proven its capability in simulating and predicting the behaviour of the different physical phenomena in most of the engineering fields. Artificial intelligence is receiving greater attention from the building industry to aid in the decision-making process in areas such as diagnostics, design, and repair and rehabilitation. In civil engineering, design of concrete mix is difficult and sensitive. The classical way for the determination of concrete mix design is based on uncertainty and depends on expert ideas.

Concrete is essentially a mixture which comprises of paste and aggregates. In concrete mix design and quality control, the uniaxial compressive strength of concrete is considered as the most valuable property, which in turn is influenced by a number of factors. The concrete mix design is based on the principles of workability of fresh concrete, desired strength and durability of hardened concrete which in turn is governed by water-cement ratio law. The strength of the concrete is determined by the characteristics of the mortar, coarse aggregate, and the interface. For the same quality mortar, different types of coarse aggregate with different shape, texture, mineralogy, and strength may result in different concrete strengths. There are various types of mixes such as nominal mix, standard mix and design mix. Nominal mixes are mixes of fixed cement-aggregate ratio which ensures adequate strength. However, due to the variability of mix ingredients the nominal concrete for a given workability varies widely in strength. The nominal mixes of fixed cement-aggregate ratio (by volume) vary widely in strength and may result in under- or over-rich mixes. For this reason, the minimum compressive strength has been included in many specifications. These mixes are termed standard mixes. In designed mixes the performance of the concrete is specified by the designer but the mix proportions are determined by the producer of concrete, except that the minimum cement content can be laid down. The common method of expressing the proportions of ingredients of a concrete mix is in the terms of parts or ratios of cement, fine and coarse aggregates. For e.g., a concrete mix of proportions 1:2:4 means that cement, fine and coarse aggregate are in the ratio 1:2:4 or the mix contains one part of cement, two parts of fine aggregate and four parts of coarse aggregate. The proportions are either by volume or by mass which provides two design methods. The concrete mix design can be carried out using IS standard code or US system of units. The tests for compressive strength are generally carried out at about 7, 14 or 28 days from the date of placing the concrete. The testing at 28-days is standard and therefore essential and at other ages can be carried out, if necessary.

ANNs have been applied to many civil engineering applications with some degree of success. ANNs have been applied to geotechnical problem like prediction of settlement of shallow foundations [1]. Many researchers have used ANN in structural engineering developing various neural network models [2-14].

Page(s) 29-43
URL http://dspace.chitkara.edu.in/jspui/bitstream/1/35/1/11003_JOTTITT_Sakshi%20Gupta.pdf
ISSN Print : 2321-3906, Online : 2321-7146
DOI https://doi.org/10.15415/jotitt.2013.11003
CONCLUSION

Concrete is a highly complex material, and prediction of the accurate compressive strength of concrete is quite a difficult task to model. The proposed ANN Artificial intelligence models will save time, reduce the waste of material and the design cost. In the study, Artificial intelligence controller was proposed for determination of the Compressive strengths at various ages 7, 14 and 28 days. The graphs show a marginal difference between the actual and predicted values. This difference is acceptable as the method is approximate. From the end user (engineers) point of view, outcome of the model is significant on following counts; it provides a way to capture inherent vagueness in the design. It offers flexibility for the mix design expert to decide appropriate value for parameters like 7, 14 and 28 compressive strength. Successful prediction of the outputs was done by all the methods, which indicated that ANN could be useful modeling tool for engineers and research scientists in the area of cement and concrete.

The correlation coefficients (Cc) for 7, 14 and 28 days is 0.8926, 0.8540 and 0.8787 respectively. The ANN model helps to capture experimental data and to use it expeditiously during the design of fresh batches of trial mixes. The analysis demonstrates the feasibility of using neural networks for capturing non-linear interactions between various parameters in complex civil engineering systems. Thus, it can be concluded that the application of ANN is more user-friendly and more explicit model can be made which help the concrete industry to avoid the risk of faulty or deficient concrete that often entails durability and safety problems.

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