1. 1. ARTIFICIAL NEURAL NETWORK FOR CONCRETE MIX DESIGN M. Monjurul Hasan Student No.: 0604148 Undergraduate Student (Level-4, Term-2) Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh Supervised by, Dr. Ahsanul Kabir Professor, Dept. of Civil Engineering Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh CE 400 Project & Thesis
2. 2. Introduction Concrete has a versatile use in the construction practice. The compressive strength is one of the most important and useful properties of concrete. The design strength of the concrete normally represents its 28th day strength 28 days is a considerable time to wait for the test results of concrete strength, while it is mandatory to represent the process of quality control.
3. 3. Introduction (Contd..) For every mix we have to wait a long time for the assurance of its quality. Hence, the need for an easy and suitable method for estimating the strength for specific mix proportion of the concrete The necessity of knowing the exact mixing proportion of the concrete ingredients for satisfying the target strength is also being felt.
4. 4. Aims of the study To design and construct an Artificial Neural Network (ANN) to predict the concrete mix proportions for desired target strength with satisfying the desired material properties. To construct an ANN to predict the concrete target strength for specific mix proportions of the ingredients of the concrete mix. Evaluate the concrete strength from the simple mathematical model and check its efficiency. To measure the efficiency of the ANN model in case of concrete mix design process.
5. 5. Aims the study (Contd) Making a comparison of the efficiency of the ANN model with the existing conventional model. To study the variations of the mix proportions of the concrete mixes with the variations of the strength of the concrete. To study the variations of the mix proportions of the concrete with the variations of water- cement (W/C) ratios of the concrete.
6. 6. Objectives of mix design Satisfying the requirements of fresh concrete (Workability). Satisfying the properties of hardened concrete (Strength and durability). Most economical for the desired specifications and given materials at a given site. Performing most optimally in the given structure under given conditions of environment
7. 7. Previous Approaches Traditional empirical formula Linear Regression model Artificial neural network Genetic algorithm Support vector mechanism M5P Tree model
8. 8. Artificial Neural Network (ANN) Inspired by a neuronal structure and operation of biological brain Appeared for the first time in the 1940 An ANN represents highly ideological mathematical model have the ability to learn and generalize the problems
9. 9. ANN Structure The Biological Neuron The human brain is estimated to contain about 1011 interconnected neurons Interconnected each other The structure of a biological neuron consists of a central cell body, an axon and a multilayer of dendrites In this cybernetic counterpart the neurons body is referred to as a processor
10. 10. ANN Structure (Contd.)
11. 11. ANN Structure (Contd.)ANN Structure (Contd.) Signals are transmitted to other nerve cellsSignals are transmitted to other nerve cells The output from the cell is transmitted through the axonThe output from the cell is transmitted through the axon The outputs are increased or decreased depending uponThe outputs are increased or decreased depending upon the synaptic weights before passing it to the next neuronthe synaptic weights before passing it to the next neuron The dendrites are the points where the input signals areThe dendrites are the points where the input signals are receivedreceived The inputs are got from the sensory organs or from otherThe inputs are got from the sensory organs or from other neurons and creates different levels of activations in theneurons and creates different levels of activations in the neuronsneurons
12. 12. ANN Structure (Contd.)ANN Structure (Contd.) The Artificial NeuronThe Artificial Neuron An Artificial Neuron models over the behavior of theAn Artificial Neuron models over the behavior of the biological neuronbiological neuron The structure of the ANN is, layeredThe structure of the ANN is, layered The function of an artificial neuron isThe function of an artificial neuron is to receive inputs under an input receiving unitto receive inputs under an input receiving unit carry them inside the neuron for processingcarry them inside the neuron for processing finally give an outputfinally give an output
13. 13. ANN Structure (Contd.)ANN Structure (Contd.) Each neuron can classify as the input unit, oneEach neuron can classify as the input unit, one summation block, one activation block and finallysummation block, one activation block and finally one result processing unitone result processing unit
14. 14. ANN Structure (Contd.)ANN Structure (Contd.)
15. 15. ANN Structure (Contd.)ANN Structure (Contd.)
16. 16. ANN Structure (Contd.)ANN Structure (Contd.) ANN ArchitectureANN Architecture The ANN that we have created for our case has one input, oneThe ANN that we have created for our case has one input, one hidden and one output layer.hidden and one output layer. The neurons of each layer are interconnected to every neuronsThe neurons of each layer are interconnected to every neurons of the adjacent layer.of the adjacent layer.
17. 17. Training of ANNTraining of ANN Training of the neural networks usually entailsTraining of the neural networks usually entails modification of the connecting weightsmodification of the connecting weights Neural networks learn from examples and exhibit someNeural networks learn from examples and exhibit some capability for generalization beyond the training datacapability for generalization beyond the training data Testing data are used for checking the generalizationTesting data are used for checking the generalization Back-propagationBack-propagation network is used to solve this particularnetwork is used to solve this particular problemproblem It is a generalized form of the Widrow-Hoff learning ruleIt is a generalized form of the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiableto multiple-layer networks and nonlinear differentiable transfer functionstransfer functions
18. 18. Training of ANN (Contd.)Training of ANN (Contd.) During training process, data are passed to the input layerDuring training process, data are passed to the input layer and then it passes from layer to layer maintaining the systemand then it passes from layer to layer maintaining the system of forward passof forward pass.. each neuron in the hidden layer receives inputs from inputeach neuron in the hidden layer receives inputs from input layers neurons.layers neurons. are already being multiplied by the adjacent weightare already being multiplied by the adjacent weight and then summed upand then summed up in some case it may modified by adding biasin some case it may modified by adding bias after it passes through the transfer function and delivers it forafter it passes through the transfer function and delivers it for the output layerthe output layer
19. 19. Training of ANN (Contd.)Training of ANN (Contd.) Comparing the output values with the target values errors areComparing the output values with the target values errors are being calculatedbeing calculated Errors are minimized with the process of iteration and in caseErrors are minimized with the process of iteration and in case of Back-propagationof Back-propagation
20. 20. Training of ANN (Contd.)Training of ANN (Contd.)
21. 21. Mix Design Data Two sets of data are usedTwo sets of data are used One from a previous literature ( 56 data, termed asOne from a previous literature ( 56 data, termed as group-1)group-1) Generated in the laboratory by experiment ( 23 data,Generated in the laboratory by experiment ( 23 data, termed as group-2)termed as group-2) In both case mix design was done by following ACIIn both case mix design was done by following ACI 211.1-91 design method211.1-91 design method For testing the cylinders ASTM (ASTM C39)For testing the cylinders ASTM (ASTM C39) recommendation was used.recommendation was used.
22. 22. Mix Design Data (Contd) Ordinary Portland Cement was usedOrdinary Portland Cement was used No admixtureNo admixture Cured upto the day of testing.Cured upto the day of testing. Only the general constituents of concreteOnly the general constituents of concrete [Cement(C), Coarse-Aggregate (CA), Fine-Aggregate[Cement(C), Coarse-Aggregate (CA), Fine-Aggregate (FA) and Water (W)] were used to evaluate the(FA) and Water (W)] were used to evaluate the concrete compressive strength.concrete compressive strength.
23. 23. Mix Design Data (Contd)Mix Design Data (Contd)
24. 24. Mix Design Data (Contd)Mix Design Data (Contd)
25. 25. Compressive Strength PredictionCompressive Strength Prediction From early age test dataFrom early age test data The model represents a simple equation (a rationalThe model represents a simple equation (a rational polynomial, Equation 6.1) that consists of only twopolynomial, Equation 6.1) that consists of only two constants and one variable.constants and one variable. The mathematical model is developed based on theThe mathematical model is developed based on the analysis of data collected from a previous study (groupanalysis of data collected from a previous study (group 1 data) and is validated with some data of cylinder tests1 data) and is validated with some data of cylinder tests (group 2 data).(group 2 data). The equation was the outcome of the observation ofThe equation was the outcome of the observation of concrete strength gaining pattern with age.concrete strength gaining pattern with age.
26. 26. Compressive Strength Prediction (Contd)
27. 27. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd)
28. 28. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) It was observed that, , values of p can be expressed as the function of qIt was observed that, , values of p can be expressed as the function of q and (Stand (Stnn) [which is a polynomial surface equation]. The equation of the) [which is a polynomial surface equation]. The equation of the correlation is given below:correlation is given below: p = a + b.q + c.Stp = a + b.q + c.Stnn + d.q.St+ d.q.Stnn + e.St+ e.Stnn 22 (6.3)(6.3) Where, StWhere, Stnn= Strength of the concrete at n th day. (n = 1, 2, 3, ) and a,= Strength of the concrete at n th day. (n = 1, 2, 3, ) and a, b, c, d and e are the coefficientsb, c, d and e are the coefficients.
29. 29. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) As we build up the correlation forAs we build up the correlation for 77thth day testday test result of concrete [result of concrete [n=7n=7],], the values of the coefficients were derived as,the values of the coefficients were derived as, a = 10.23a = 10.23;; b = -0.9075b = -0.9075;; cc = 0.3412= 0.3412;; d = 0.1721d = 0.1721;; e = 0.0112e = 0.0112 from regression analysis of thefrom regression analysis of the Putting these values in Equation 3 the following equation wasPutting these values in Equation 3 the following equation was obtained:obtained: p = 10.23 - 0.9075q + 0.3412Stp = 10.23 - 0.9075q + 0.3412St77 + 0.1721qSt+ 0.1721qSt77 + 0.0112St+ 0.0112St77 22 (6.3)(6.3) ForFor 1414thth dayday strength results [strength results [n=14n=14] the coefficients becomes,] the coefficients becomes, a =a = -4.527-4.527;; b = -1.041; c = 1.373b = -1.041; c = 1.373;; d = 0.1406d = 0.1406;; e = -0.0125e = -0.0125. Putting these. Putting these values in to Equation 3 the following equation was obtained:values in to Equation 3 the following equation was obtained: p = -4.527- 1.041q + 1.373Stp = -4.527- 1.041q + 1.373St1414 + 0.1406qSt+ 0.1406qSt1414 + -0.0125St+ -0.0125St1414 22 (6.4)(6.4)
30. 30. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) Represented surface .Represented surface .
31. 31. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) Represented surface .Represented surface .
32. 32. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd)
33. 33. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) Prediction effectivenessPrediction effectiveness
34. 34. Compressive Strength Prediction (Contd) Prediction effectivenessPrediction effectiveness
35. 35. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) Prediction with ANNPrediction with ANN From total 79 data set 59 are used for training the ANNFrom total 79 data set 59 are used for training the ANN and rest are for testing.and rest are for testing. water-cement ratio, fineness-modulus of the fine-water-cement ratio, fineness-modulus of the fine- aggregate, the coarse-aggregate ratio (10mm: 20mm)aggregate, the coarse-aggregate ratio (10mm: 20mm) and most importantly the mix ratio (FA/C and CA/C) areand most importantly the mix ratio (FA/C and CA/C) are used as the input parameters for predicting the strength.used as the input parameters for predicting the strength.
36. 36. Compressive Strength Prediction (Contd)Compressive Strength Prediction (Contd) Prediction with ANN..Prediction with ANN..
37. 37. Prediction of Mix RatioPrediction of Mix Ratio 28th day strength of the concrete, water-cement ratio, fineness- modulus of the fine-aggregate, the coarse-aggregate ratio (10mm: 20mm) are used as the input parameters
38. 38. Prediction of Mix Ratio (Contd.)Prediction of Mix Ratio (Contd.)
39. 39. Different Parametric StudiesDifferent Parametric Studies Effect of W/C Ratio upon StrengthEffect of W/C Ratio upon Strength
40. 40. Different parametric studies (Contd) Effect of the Fineness-modulus on StrengthEffect of the Fineness-modulus on Strength
41. 41. Different parametric studies (Contd) Effect of the Fineness-modulus on StrengthEffect of the Fineness-modulus on Strength
42. 42. Different parametric studies (Contd)Different parametric studies (Contd) Variation of Mix Ratio with the Variation of StrengthVariation of Mix Ratio with the Variation of Strength
43. 43. Different parametric studies (Contd)Different parametric studies (Contd) Variation of Mix Ratio with the Variation of StrengthVariation of Mix Ratio with the Variation of Strength
44. 44. ConclusionConclusion Artificial neural network (ANN) for predicting the concrete strength has enormous potentiality with negligible quantity maximum Root Mean Square Error of 0.46 , Mean absolute error of 0.30 and it shows the efficiency of 98.36%. Artificial neural network (ANN) shows a great efficiency of about 99% on an average for predicting the concrete mix ratio for satisfying the target strength in desired workability condition. The correlation coefficient 0.997 indicates a very good correlation of predicting mix ratio with actual one. From the results it can conclude that ANN is a very powerful tool for predicting both mix ratio and strength and can use in practice.
45. 45. Conclusion (Contd)Conclusion (Contd) a simple mathematical model for predicting the compressivea simple mathematical model for predicting the compressive strength from early age test result is modeled by a simplestrength from early age test result is modeled by a simple mathematical equation (rational polynomial) and a polynomialmathematical equation (rational polynomial) and a polynomial surface equation.surface equation. It was observed that the in prediction that the highest root meanIt was observed that the in prediction that the highest root mean square error (RMSE) is 3.02 and mean absolute error (MAE) 2.68. Itsquare error (RMSE) is 3.02 and mean absolute error (MAE) 2.68. It is been counted in the percentage of the actual value, lowest valueis been counted in the percentage of the actual value, lowest value of the prediction is 83% of the actual one, which is so acceptable inof the prediction is 83% of the actual one, which is so acceptable in the concrete construction practice.the concrete construction practice. The proposed equations have the potential to predict strength dataThe proposed equations have the potential to predict strength data for every age.for every age. This will help in making quick decision for accidental poorThis will help in making quick decision for accidental poor concreting at site and reduce delayconcreting at site and reduce delay
46. 46. Conclusion (Contd)Conclusion (Contd) From a trained ANN network different parametric correlation can be evaluated like the concrete strength variation with the change of water cement ratio, which can be used to determine optimum W/C ratio for getting the highest strength. the changing pattern of the CA/C and FA/C ratios with the change of strength gives an important conclusion, that the changes of the ratios have effect up to a certain level on strength of concrete. After that the effect becomes insignificant For making the whole system more convenient, some graphs can be drawn from utilizing the trained ANN and present them to usable format to make a direct, convenient use in the field.
47. 47. Thats it,