Prediction of Hardened Properties of Fly Ash Concrete

8/3/2019 Prediction of Hardened Properties of Fly Ash Concrete

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PREDICTION OF HARDENED PROPERTIESPREDICTION OF HARDENED PROPERTIES

OF FLY ASH CONCRETE USING NEURALOF FLY ASH CONCRETE USING NEURAL

NETWORKS AND EXPERIMENTAL METHODNETWORKS AND EXPERIMENTAL METHOD

By

HARISH.T.S1BI10CSE033rd Sem, M.Tech (Structures)

Under the Guidance of

JEEVAN.NLECTURERDEPARTMENT OF CIVIL ENGINEERING



ContentsContents

y Abstract

y Introduction

y Literature Review

y Objective of the Studyy Experimental Program

y Methodology

y References

Dept. of Civil Engineering, BIT 2



AbstractAbstracty Effect of fly ash content on the hardened properties of concrete

depending on water/cement ratio and concrete age is investigated byuse of Artificial Neural Network(ANN) approach.

y

Compressive strength, split tensile strength and flexural strength valuesof various samples of concrete that produced by replacement of cement by fly ash by ratio of 20% are used.

y Experimental values of the concrete samples are compared with the

values obtained by using ANN model.




IntroductionIntroductiony Concrete is essentially a mixture of paste and aggregate. The paste,

comprised of cement and water, binds the aggregates into a hard mass;the paste hardens because of the chemical reaction of the cement andwater called hydration.

y In concrete mix design and quality control, the uniaxial compressivestrength of concrete is considered as the most valuable property.

y Prediction of concrete strength is important in modern concrete

construction and in engineering judgement.




y Fly ash usually refers to ash produced during combustion of coal. Flyash is generally captured from the chimneys of coal-fired power plants,by electrostatic precipitators.

y Fly ash is a pozzolanic material widely used as a cement replacement toproduce high-performance concrete and high-volume fly ash concrete.

y Benefits, in terms of the durability of high-strength concrete are wellestablished.

y ASTM specification C618 defines two types of fly ash (Class F and ClassC) related to the type of burned coal.

y

The main contribution of fly ash to concrete is to the workability andthe reduction of temperature rise in fresh concrete as well as to thedurability and long term strength development of hardened concrete.




y Artificial neural networks (ANNs) are structures that are inspiredby neurobiological studies of the human brain.

y ANNs are parallel processing computational structures which arelinked to each other by means of weighted connections and which arecomposed of computation elements with their own memories.

y The recent developments in the field of ANNs technology, a sub-field of artificial intelligence, have been serving to solve various problemsencountered in civil engineering processes.

y The successful example of analysis is a neural network model on theprecipitation-effluence procedure, estimation of oceanic wave by using

neural network, damage examination of a construction by using neuralnetwork, but, above all, they invented several systems that examine thestrength of concrete.




Literature ReviewLiterature Reviewy Hasbi Yaprak, Abdu¨lkadir SKarac, IÛlhami Demir [1] The study

aims at developing an artificial neural network (ANN) to predict thecompressive strength of concrete. A data set containing a total of 72concrete samples was used in the study. The following constituted theconcrete mixture parameters: two distinct w/c ratios (0.63 and 0.70),

three different types of cements and three different cure conditions.Measurement of compressive strengths was performed at 3, 7, 28 and90 days. Two different ANN models were developed, one with 4 inputand 1 output layers, 9 neurons and 1 hidden layer, and the other with 5,6 neurons, 2 hidden layers. For the training of the developed models, 60experimental data sets obtained prior to the process were used. The 12

experimental data not used in the training stage were utilized to testANN models. The researchers have reached the conclusion that ANNprovides a good alternative to the existing compressive strengthprediction methods, where different cements, ages and cure conditionswere used as input characteristics.




y Seyed Jamalaldin Seyed Hakim, Jamaloddin Noorzaei,M.S.Jaafar, Mohammed Jameel and MohammadMohammadhassani[2] A method to predict 28-day compressivestrength of high strength concrete (HSC) by using MFNNs is proposedin this paper. The artificial neural networks (ANN) model is constructed

trained and tested using the available data. A total of 368 different dataof HSC mix-designs were collected from technical literature. The dataused to predict the compressive strength with ANN consisted of eightinput parameters which include cement, water, coarse aggregate, fineaggregate, silica fume, superplasticizer, fly ash and granulated graded

blast furnace slag. For the training phase, different combinations of layers, number of neurons, learning rate, momentum and activationfunctions were considered. The ANNs models give high predictionaccuracy, and the research results demonstrate that using ANNs topredict concrete strength is practical and beneficial.




y C.Deepa, K.Sathiyakumari, V.Pream Sudha[3] The paper is aimedat guiding the selection of available materials and proportioning them asto produce the most economical concrete suitable for the desiredpurpose. According to the National Council for Cement and BuildingMaterials (NCBM), New Delhi, the compressive strength of concrete is

governed generally, by the water-cement ratio. The mineral admixtureslike fly ash, ground granulated blast furnace, silica fume and fineaggregates also influence it.The main purpose of this paper is to find theaccuracy for the compressive strength of high performance concrete byusing classification algorithms like Multilayer Perceptron, Rnd tree

models and C-RT regression. The result from this study suggests thattree based models perform remarkably well for designing the concretemix.




y Meltem Özturan, Birgül Kutlu, Turan Özturan[4] Prediction of concrete strength is an important issue in ready-mixed concreteindustry, especially, in proportioning new mixtures and for the qualityassurance of the concrete produced. In this paper, it is aimed toillustrate that the artificial neural networks can be used for predicting

the 28-day strength of low to medium strength concretes. Thecompositional, fresh concrete and early strength data obtained fromdifferent batching plants of a ready-mixed concrete company have beendefined in terms of ten independent variables that are grouped in fivedifferent system models to which neural network and multiple linearregression models have been applied. The accuracies of prediction byartificial neural network and multiple linear regression models as well asby Abrams· law are compared on the basis of the coefficient of determination. It appears that the best results are obtained by theartificial neural network models using data for fresh concrete and earlystrength simultaneously.




y I-Cheng Yeh[5] Fly ash and slag concrete (FSC) is a highly complexmaterial whose behavior is difficult to model. This paper describes amethod of modeling slump of FSC using artificial neural networks. Theslump is a function of the content of all concrete ingredients, includingcement, fly ash, blast furnace slag, water, superplasticizer, and coarse and

fine aggregate. The model built was examined with response trace plotsto explore the slump behavior of FSC. This study led to the conclusionthat response trace plots can be used to explore the complex nonlinearrelationship between concrete components and concrete slump.




y I-Cheng Yeh[6] This paper investigates the potential of using design of experiments and neural networks to determine the effect of fly ashreplacements, from 0 to 50%, on early and late compressive strength, from3 to 56 days, of low- and high-strength concrete, at water-cementitiousmaterial ratios in the range of 0.3²0.7. The research reported in this papershows the following conclusions: (1) using a simplex-centroid mixture

experiment design, a much smaller number of experiments need to beperformed to obtain meaningful data; (2) high correlations between thecompressive strength and the component composition of concrete can bedeveloped using the generalization capabilities of the neural networks; (3)analyses of variance to test the effects of the variables and theirinteractions on concrete strength can be performed; (4) the strength ratio,i.e., the percentage of strength of concrete containing fly ash to strength of concrete without fly ash (pure-cement concrete) based on the same w /band the same age, is significantly reduced as the fly ash replacementincreases, is somewhat reduced as the water-binder ratio decreases, and ishighly significantly reduced as the age decreases; and (5) the higher fly ashcontent mixes yielded lower strength ratios throughout, the differencebeing greater at early age and low water-binder ratio.




y Tayfun Uygunoglu and Osman Unal[7] In this study, determinationof effect of fly ash (FA) content on the compressive strength of concrete depending on water/cement ratio and concrete age wasinvestigated by use of fuzzy logic (FL) approach. In the approach of modelling with FL, compressive strength values of various sample of

concrete that produced by replacement of cement by F class of FA byratio of 0 (control), 10%, 20% and 30% were used. Water/binder ratio of these concrete samples was varied between 0.27-0.60 in six differentvalues. Experimental compressive strength values of the concretespecimens at 3, 7, 28, 90, 180 and 365 days compared with FL values

obtained using the fuzzy sets. Optimum FA content and water/binderratio for the best compressive strength for early age and hardenedconcrete can be obtained with FL.




y Rajiv Gupta, Manish A. Kewalramani and Amit Goel[8] Over theyears, many methods have been developed to predict the concrete strength.In recent years, artificial neural networks ANNs have been applied to manycivil engineering problems with some degree of success. In the presentpaper, ANN is used as an attempt to obtain more accurate concretestrength prediction based on parameters like concrete mix design, size andshape of specimen, curing technique and period, environmental conditions,etc. A total of 864 concrete specimens were cast for compressive strengthmeasurement and verification through the ANN model. The back propagation-learning algorithm is employed to train the network forextracting knowledge from training examples. The predicted strengthsfound by employing ANN are compared with the actual values. The results

indicate that ANN is a useful technique for predicting the concretestrength. Further, an effort to build an expert system for the problem isdescribed in this paper. To overcome the bottleneck of intricate knowledgeacquisition, an expert system is used as a mechanism to transferengineering experience into usable knowledge through rule-basedknowledge representation techniques.




y Dong-Suk Yang, Sun-Kyu Park, and Jong-Heon Le[9] An artificial neural network was applied to predict compressive strength, slump value and mix proportion of aconcrete. Standard mixed tables were trained and estimated, and the results werecompared with those of the experiments. To consider variabilities of material properties,the standard mixed tables from two companies of Ready Mixed Concrete were used.Andthey were trained with the neural network. In this paper, standard back propagationnetwork was used. The mix proportion factors such as water cement ratio, sandaggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand,

unit coarse aggregate and air entraining admixture were used. For the arrangement onthe approval of prediction of mix proportion factor, the standard compressive strength of 180 kgf/cm2~300 kgf/cm2, and target slump value of 8 cm, 15 cm were used. For thearrangement on the approval of prediction of compressive strength and slump value, thestandard compressive strength of 210 kgf/cm2~240 kgf/cm2, and target slump value of 12cm and 15 cm were used because these ranges are most frequently used. In results, in theprediction of mix proportion factor, for all of the water cement ratio, sand aggregateratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse

aggregate, air entraining admixture, the predicted values and the values of standard mixedtables were almost the same within the target error of 0.10 and 0.05, regardless of twocompanies. And in the prediction of compressive strength and slump value, the predictedvalues were converged well to the values of standard mixed tables within the target errorof 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to theprediction of concrete mixture and compressive strength.




y Jong In Kim and Doo Kie Kim[10] The compressive strength of concrete is mostly used criterion in producing concrete. However, testingfor compressive strength of concrete specimens is a complicated and time-consuming task. More importantly, it is too late to make improvement if thetest result does not satisfy the required strength, since the test is usuallyperformed at the 28th day after the placement of concrete at theconstruction site. Therefore, strength prediction before the placement of concrete is highly desirable. This study presents the effort in applying theneural network technique for predicting the compressive strength of concrete based on concrete mix proportions. For training and testing, thedata sets on the mix proportions of two ready mixed concrete companieswere used, and then the required compressive strength was predicted bytrial and error. The predicted compressive strengths were verified bycomparing the predicted results with those tested in the laboratory. The

results show that the neural networks are very efficient in predicting thecompressive strength of concrete with good accuracy. The application of this technology in predicting the compressive strength of concrete isexpected to contribute to the assurance of concrete quality formanufacturing of optimal concrete.




Objective of the StudyObjective of the Studyy To design a mix proportioning of concrete using fly ash as part

replacement of Ordinary Portland Cement (OPC) for M20 and M25grade using IS 10262:2009.

y To study the effect of fly ash on concrete with 20% replacement of cement by fly ash and study its hardened properties.

y To develop ANN between concrete mix ingredient properties namelymaximum size of aggregate, degree of quality control, degree of workability, type of exposure, characteristic compressive strength, splittensile test strength and flexural strength of concrete is required in thefield at 28 days and concrete mix proportion.

y The objective of this study is to illustrate that the Artificial NeuralNetworks can be used to predict the 28-day compressive strength of flyash concrete.

y To obtain more accurate concrete strength prediction.




Experimental ProgramExperimental Program PHASE 1: Evaluation of physical properties of natural aggregates.

PHASE 2: Fly ash concrete mix proportioning and preparation of testSpecimens. The trial mix design works has to be carried to proposed target strength of concrete.

Weigh batch mixing is proposed for mixing.

Cubes and cylinder sampling procedure to be followed according to IS code of practice.

Preparation of Beam specimen.

PHASE 3: Experimental compressive strength values, split tensilestrength values and flexural strength values of concrete depending on

concrete mix ingredient properties namely maximum size of aggregate,water/cement ratio, fly ash content, degree of quality control, degree of workability, type of exposure, concrete mix proportion and age arecalculated.




MethodologyMethodologyy The two different concrete mix grades used for the present study are

M20 and M25.

y More number of cubes, cylinders and beams are casted which are to betested for each variation in the ingredient parameters.

y Cube, cylinder samples and beam specimens are casted as per relevantI.S code of practice.

y

After casting all specimen and samples will be kept at roomtemperature for 24 hours. Then the moulds are demoulded and kept inwater for curing for 28 days.

y Compression test and split tensile test are conducted for the Cubes

and cylinder samples as per relevant I.S code of practice.Dept. of Civil Engineering, BIT 19



MethodologyMethodologyy Flexure strength of simply supported beams will be carried by two

point loading method with uniform rate of increase in load as perrelevant I.S code of practice.

y

Each and every variations in the results obtained from the three testssuch as Compression test, Split tensile test and the flexural test are tobe predicted usingANN technique.

y By using the data generated as said above as a knowledge base for

future prediction ay using ANN and also decided to produceappropriate relationship between ingredient parameters and saidknowledge base by soft computing technique.




ReferencesReferences1. HasbiYaprak, Abdu¨lkadir Karac, IÛlhami Demir. (2011). ́ Prediction of the

effect of varying cure conditions and w/c ratio on the compressivestrength of concrete using artificial networks.µ J. Neural Comput & Applic.,

2. Seyed Jamalaldin Seyed Hakim, Jamaloddin Noorzaei, M.S.Jaafar, Mohammed Jameel and Mohammad Mohammadhassani. (2011). ́ Application of artificialneural networks to predict compressive strength of high strength

concrete.µ Inter national Jour nal of the Physical Sciences., 6(5), 975-981.

3. C.Deepa, K.Sathiya Kumari, V.Pream Sudha. (2010). ́ Prediction of theCompressive Strength of High Performance Concrete Mix using treeBased Modeling.µ Inter national Jour nal of Computer Applications (0975-8887).,

6(5), 18-24.

4. Meltem Özturan, Birgül Kutlu, Turan Özturan. (2008). ́ Comparison of Concrete Strength Prediction Techniques with Artificial NeuralNetwork approach.µ Building Research Jour nal., 56(1), 23-36.

5. I-Cheng Yeh. (2006). ́ Exploring Concrete Slump Model Using ArtificialNeural Networks.µ ASCE Jour nal of Computing in Civil Eng ineering ., 20(3), 217-221.




ReferencesReferences

6. I-Cheng Yeh. (2006). ́ Analysis of Strength of Concrete Using Design of Experiments and Neural networks.µ ASCE Jour nal of Materials in Civil

Eng ineering ., 18(4), 597-604.

7. Tayfun Uygunoglu & Osman Unal. (2006). ́ A new approach todetermination of compressive strength of fly ash concrete usingfuzzy logic.µ Jour nal of Scientific & Industrial Research., 65, 894-899.

8. Rajiv Gupta, Manish A. Kewalramani, & Amit Goel. (2006). ́ Prediction of Concrete Strength Using Neural-Expert System.µ ASCE Jour nal of

Materials in Civil Eng ineering ., 18(3), 462-466.

9. Dong-SukYang, Sun-Kyu Park, & Jong-Heon Lee. (2003). ́ A Prediction onMix Proportion Factor and Strength of Concrete Using Neural

Network .µK SCE Jour nal of Civil Eng ineering ., 7(5), 525-536.

10. Jong In Kim & Doo Kie Kim. (2002). ́ Application of Neural Networks for Estimation of Concrete Strength.µ K SCE Jour nal of Civil Eng ineering ., 6(4),429-438


Prediction of Hardened Properties of Fly Ash Concrete

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