ARTIFICIAL NEURAL NETWORK MODEL FOR FLEXURAL DESIGN OF CONCRETE HYDRAULIC STRUCTURES

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 2, February 2018, pp , Article ID: IJCIET_09_02_026 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed ARTIFICIAL NEURAL NETWORK MODEL FOR FLEXURAL DESIGN OF CONCRETE HYDRAULIC STRUCTURES Oday M. Albuthbahak and Hayder H. Alkhudery Faculty of Engineering, University of Kufa, Najaf, Iraq ABSTRACT As a computer technique, Artificial Neural Networks (ANNs) have expanded in use with engineering fields. ANNs have been used in many civil engineering problems and some of them were used in the design of concrete structural elements and have shown a good degree of success. This paper presents an ANN model for the strength design of reinforced-concrete hydraulic structure according to the requirements of the Engineering Manual Structural design is a sequential process needs iteration, assumption, checking the limits,...etc, and that can be programmed with some judgments of the designer. 288 cases of design samples have been calculated using excel sheets and Microsoft visual basic programming language. 200 samples of design randomly selected have been used for training of ( ) ANN model. 50 samples have been selected for validation, and 38 for prediction processes. The predicted design outputs were the thickness of hydraulic concrete section and corresponding steel reinforcements. Visual Gene Developer software of ANN prediction for general purposes has been used with a feed-forward neural network with a standard back-propagation learning process. The suggested artificial neural network model has predicted the output data of design for concrete sections, and the results have shown a satisfactorily match with the actual output data of design. Key words: Artificial Neural Networks; Engineering Manual ; Strength design of concrete hydraulic structure; Feed-forward neural network; Backpropagation learning algorithm. Cite this Article: Oday M. Albuthbahak and Hayder H. Alkhudery, Artificial Neural Network Model for Flexural Design of Concrete Hydraulic Structures. International Journal of Civil Engineering and Technology, 9(2), 2018, pp INTRODUCTION The first to suggest the idea of neural networks came from the work of brain neurons that can be likened to a biological electrical network to process information to the brain. In these networks, Donald O. Hebb suggested that the neural synapse plays a key role in guiding the computing process and this prompted to think about the idea of connectivity and artificial neural networks. It consists neural networks artificial than a neurons or processing units, editor@iaeme.com

2 Oday M. Albuthbahak and Hayder H. Alkhudery linked together to form a network of nodes, and all communication between these nodes have a set of values called weights contribute to the identification resulting from each processing units based on the values that entered the unit [1]. In general, all the neural networks arranged in layers of artificial cells: the inner layer, the outer layer and the in between layers or hidden layers exist between inner and outer layer. Each cell in one of these layers is related to all neurons in the layer that followed, and all neurons in the layer that precede it. Each communication between one neuron and another is characterized by a binding value called weight (weighting), it constitutes the importance of the link between these two neurons. The neuron multiplying each entered value received from neurons in the previous layer weights of connection of these neurons, then collecting all the multiplication outputs, and then subjecting the result to continued converter varies depending on the type of neuron. The result of continued converter is a neuron output that carries out to the subsequent layer neurons [2]. One of the most important forms of neural networks: feed-forward neural network, which it is a group of holding neural arranged in layers. These neurons are connected with each other so that each neuron is usually associated with a layer of all neurons in the next layer (neurons are not linked with each other at the same layer). The typical arrangement for these networks is three neural layers called (input layer, hidden layer, output layer). Input layer does not carry out any computational process they simply place of the network supplying data, the input layer then supply (transfer information) to hidden layer and then hidden layer supplying the output layer. The real data processing is in the hidden layer and output layer [3]. Back-propagation is a training algorithm in which the information flows in one direction at a time, either forward or backward, that aims to adjust the weights of these network connections. When there is an adequate number of neurons, the network will be able to training to do things with the help of training algorithm [4]. There are many different architectures of neural networks, each of them has the characteristic to be used for modeling a specific problem. The feed-forward neural networks with back-propagation learning algorithms are considered very important especially in the uses intelligent classification of data not already familiar, and it is the most generally used in structural engineering [5]. Structural design is sequential steps which are restricted by selected building code requirements and the most important step is the first step. Assumptions, iteration, comparison, checking... etc., all are processes necessary for the structural design. In this days the computer programs are efficient and accurate in structural analysis, but these programs are not enough for design because they require human expertise and judgment [6]. The artificial neural network is a new technique which discovered to simulate the human brain and has been used in many applications of engineering [2], [7]. US Army Corps of Engineers has produced Engineering Manual for strength design of reinforced concrete hydraulic structures in 30 June 1992 and reviewed in 20 August The manual represents a guidance for designing reinforced concrete hydraulic structures by strength deign method. The manual provides the designer with design procedures in sufficient details and examples of their application. The procedure is consistent with ACI 318 guidance, except for load factor and reinforcement percentage. This manual has an approach similar to that of ACI 350R-89 [8] editor@iaeme.com

3 Artificial Neural Network Model for Flexural Design of Concrete Hydraulic Structures 2. ENGINEERING MANUAL DESIGN PROCEDURE For singly reinforced concrete flexural members subjected to combined flexure and compressive axial load the ratio of tension reinforcement ρ is limited to a recommended value of 0.25ρ b, where ρ b is the tension reinforcement ratio at balanced condition. The upper limit of 0.375ρ b is permitted to avoid investigation of serviceability and economy, and maximum permitted upper limit of 0.5ρ b when excessive deflections are not predicted [8]. These limits of reinforcement ratio will be taken in consideration to avoid further investigation for serviceability requirements due to service load. A step-by-step procedure has been detailed in Appendix D of the manual. Below the summarized steps and equations of design, collected from the manual, are presented. The design equations specified for rectangular flexural member subjected to pure bending or bending moment combined with axial load. The required parameters and data are; concrete compressive strength f c, steel yield strength f y, ultimate moment M u, ultimate axial load P u, width of concrete rectangular section b, concrete cover c, and ratio of tensile reinforcement ratio to balanced one ρ/ρ b which is specified to above-mentioned values as recommended by the manual in item (3-5) of maximum tension reinforcement. Step 1: Assuming thickness h of the concrete section, taking in consideration minimum thickness limited by the manual. Step 2: Computing required nominal strength M n, and P n from the following equations: n u n u ( u h f c ) Step 3: Calculating the effective depth d, factor β 1, ratio k d, minimum effective depth that a singly reinforced member may have and maintain steel ratio requirements d d, depth of stress block at limiting value of balanced condition a d, and bending moment capacity at limiting value of balanced condition M DS from the following equations: d h c f c for f c a and for f c a ( ρ ρ ) c k d c f y E s where c is the maximum concrete strain at the extreme compression fiber = D d a d k d d n f c k d ( k d ) f c a d (d a d ) (d h ) n Step 4: For no axial load (P u =0), d should be greater than d d, and for (P u >0), M DS should be greater than M n, otherwise the thickness h of concrete section should be increased and repeating steps from step editor@iaeme.com

4 Oday M. Albuthbahak and Hayder H. Alkhudery Step 5:Calculating ratio of stress block depth to the effective depth k u, and the required area of steel reinforcement A s taking in consideration the minimum tension reinforcement ratio ρ min equal to k u s n n (d h ) f c k u d f c d f y Step 6: Checking that ϕp n is less than the lesser of 0.1bh f c and ϕp b. Otherwise, the thickness h of concrete section should be increased and repeating steps from step 2 again, where; k E s c E s c f y ( f c k d sf y ) It is obvious that the design procedure needs assumption as a first step, checking for some conditions, and an iteration processes to reach the optimum design for the thickness of the concrete section, and then specifying the required area of steel reinforcement. 3. DEVELOPMENT OF NEURAL NETWORK MODEL The aim of this research is to construct an artificial neural network mode to strength design of reinforced-concrete hydraulic structures using EM To build such network a set of design examples should be prepared. These examples should have design parameters each one has a range of values Creation of Design Cases The design parameters taken into account were; ultimate moment Mu ranging from 150 kn.m to 400 kn.m with 50 kn.m steps (6 cases), concrete compressive strength f c ranging from 20 MPa to 35 MPa with 5 MPa steps (4 cases), ultimate axial load P u ranging from 0 to 450 kn with 150 kn steps (4 cases), and ratio of tensile reinforcement ratio to balanced one ρ/ρ b with values of 0.25, 0.375, and 0.5 (3 cases). These values have been chosen to cover a wide range of possible design cases. The total number of design examples are equal to 6*4*4*3=288 cases. The width of the concrete structure has been taken equal to 1m (1000mm) as a unit strip, the concrete cover c is constant and equal to 58mm, and the yield strength of steel reinforcement is constant and equal to 420 MPa, because other steel grades are not recommended by the EM for the reasons mentioned in item (2-2. Quality) of the manual [8]. For each set of input data, the minimum design thickness h of the concrete section and area of steel reinforcement A s have to be calculated. An Excel sheets have been constructed to calculate the required h and A s. The first sheet was built to calculate the design parameters described-above in design procedure steps, see Fig.1. The initial value of concrete section thickness was assumed to be 200mm. The iteration processes have been done with aid of Microsoft Visual Basic for Applications (micro). The incremental value of thickness h was 10mm for each iteration. For each new value of h, checking the differences between d and d d or M n and M DS have to be achieved, as mentioned in step 4 of the design procedure. Final checking for the applied loads, if they within limits n editor@iaeme.com

5 Artificial Neural Network Model for Flexural Design of Concrete Hydraulic Structures specified in step 6, was done in the last column of the sheet, see Fig.1. A thorough review for each design case has been achieved to make the final adjustments. Figure 1 EM Part of Excel Sheet of Design Other sheets were constructed to separate pure input and output data without formulas and to make randomization to them. Finally, the randomized data have been divided into 3 groups, (Training data, validation data, and Prediction data). The total examples of design cases were 288 cases. Two hundred case were selected randomly for training samples, and fifty cases for validation samples and the rest (38 case) were used as a prediction samples Artificial Neural Network software Visual Gene Developer is one of the free software that contains artificial neural network prediction package for general purposes. The software can be used for gene design, optimization, or artificial neural network. Each package is able to work independently. Artificial neural network package has software environment and tools which can be easily used. The learning algorithm is a feed-forward neural network with a back-propagation. This algorithm is used to train networks and it provide some different transfer functions [9] Input and Output vectors The input vector was carefully chosen for this model which was [Input1=f c, Input2=M u, Input3=P u, Input4=ρ/ρ b ]. Consequently, the output vector for the neural network model was [Output1=h, Output2=A s ]. Each vector has been normalized by dividing all values of a single parameter by a number slightly larger than the maximum value of the parameter. Therefore, all values of input and output vectors will range between 0 to 1. The normalization processes are mandatory for the artificial neural network software Network topology and setting of training parameters To set up the most suitable network configuration no specified method known yet [10], [11], [12]. A trial and error processes have been used to provide fast training and the most reliable predictions. After examining some network configurations, it has been observed that the network with 10 neurons each in two hidden layers has the best behavior in training and predicting. The behavior can be monitored by the regression analysis table and chart. Therefore, a topology of ( ) has been selected for this network model from a number of examined configurations editor@iaeme.com

6 Oday M. Albuthbahak and Hayder H. Alkhudery Also, the values and selections for the parameters of training-setting have been specified using the method of try and error. As an example, when the training is stuck as the sum of error is oscillated. In that case, learning rate should be reduced. Thus, the parameters of training setting were; Learning rate equal to , Momentum coefficient equal to 0.1, Hyperbolic tangent selected to be the Transfer function, as maximum training cycles, and Target error of The initialization method of threshold and weight factor was set to be random. The used Artificial Neural Network topology and all other settings are depicted in Fig.2. Figure 2 Used Artificial Neural Network configuration 3.5. Training and Validation Data of the Network The training of this network has been completed using the Back-Propagation algorithm (BP). Also, a validation process of the network has been carried out to assess the network for the other set of data that are not used in training of the network. Visual Gene Developer software has a separate window for regression analysis. The regression coefficients (R 2 ), slope, and y-intercept of output variables for training and validation data were monitored with each learning cycle. With this window, the convergence rate with each cycle of training can be traced. Figs. 3 shows the convergence level at 24,255 cycles of training with elapsed time of 1 minute and 54 seconds. The regression analysis shows that the regression coefficients for training and validation data were above 95%. In regression analysis window, Out1 and Out2 represent h and A s, respectively. Fig. 4 shows the data set of the network at 140,771 cycles of training, which was the last training cycle. Actually, the program has been stopped at this point of training. The total processing time was 11 minutes and 7 seconds. The regression coefficients for training and validation data were all a ove % The training can e continued y pressing continue training utton But no more advantages with the continuity in this stage because of the slow rate of change of convergence editor@iaeme.com

7 Artificial Neural Network Model for Flexural Design of Concrete Hydraulic Structures Figure 3 Convergence rate by regression analysis at training cycle of Figure 4 Convergence rate by regression analysis at training cycle of (Last cycle) After stopping the training and validation processes, the output data of validation were denormalized and saved. Figs.5 (a)-(b) represents a graph for the validation output data versus the design data, for h and A s respectively. The linear fitting results of Figs.5 (a)-(b) coincide with the data shown in regression analysis window of last training cycle. The two graphs show a high level of confidence of trained network editor@iaeme.com

8 Oday M. Albuthbahak and Hayder H. Alkhudery 3.6. Prediction Data of the Network As a prediction step, the neural network has been tested with 38 randomly selected samples that were not used in training and validation process. Here, the trained network model should capable of prediction of the depth of the concrete section h, and the required area of steel reinforcement A s. To investigate the confidence level in the relation between the actual designed value of h and A s and the predicted ones, two charts have been constructed for this purpose as illustrated in Figs. 6(a)-(b). The results of the linear fit, for the two charts, were presented from which the high values of coefficients of regression can be seen. So, it can be said that the trained network gave excellent prediction values for the design outputs. a h A s Figure 5 Regression analysis for output validation data versus design data a h A s Figure 6 Regression analysis of prediction versus actual design outputs editor@iaeme.com

9 Artificial Neural Network Model for Flexural Design of Concrete Hydraulic Structures The relationship between the designed depth of concrete section h and the required area of steel reinforcement A s for that section is not linear, and this can be observed from the actual design outputs. To see this nonlinearity and how much the actual design outputs coincide with the validated and predicted ones, a sample of twenty cases were randomly selected and then represented in Figs. 7(a)-(b). It is obvious that the values, predicted by the trained network model, of design outputs coincide satisfactorily with results of actual design cases. a Validation output rediction output Figure 7 Sample of Prediction and Validation output with actual output of design 4. CONCLUSIONS Hydraulic concrete structures need special requirements in strength design and serviceability. Engineering Manual is an important manual and exhibits a clear and sequential procedure for the strength design of the hydraulic concrete structures. It has a similarity in design approach with ACI 350R-89. It is not difficult to program such a design procedure, but the design outputs can t e accepted without designer judgment The artificial neural network is a new computer technology that has gained a wide range of use in civil engineering fields, especially for structural analysis and design. In this paper, an artificial neural network model has been developed to strength design of hydraulic reinforced-concrete structures with the requirements of Engineering Manual In this manual, the design steps should start with an assumption of a thickness of the concrete section, and then an iteration process has to be used to find the optimal thickness of the concrete section of the hydraulic structure. Consequently, the required area of steel reinforcement can be calculated, as part of the strength requirements. The designer should do a review of the output design data and may make a redesign to some parameters according to his judgment from practice. In literature and as having observed in this work, no specific method to choose the number of hidden layers, learning rate, momentum coefficient, transfer function, initialization method of the threshold, and initialization method of weight factor other than trial and error method. The developed neural network has been trained with 200 samples, that cover a wide range of design parameters, in training cycle in less than 12 minutes. With excellent accuracy, the developed neural network has shown the ability to predict the optimal thickness editor@iaeme.com

10 Oday M. Albuthbahak and Hayder H. Alkhudery of concrete section and the corresponding area of steel reinforcement. Thus, it could be concluded that the developed neural network model can be a safe and powerful alternative for structural design of reinforced-concrete hydraulic structures with requirements of EM REFERENCES [1] ohammed hmed and E Karrar rtificial Neural Networks nd Their Interaction With Information Processing Artificial Neural Networks ( Artificial Neural Network NN Int. J. Technol. Enhanc. Emerg. Eng. Res., vol. 4, no. 5, pp , [2] M. Lazarevska, M. Knezevic, M. Cvetkovska, and A. Trombeva-Gavriloska, pplications of rtificial Neural Networks in Civil Engineering Teh. Vjesn., vol. 21, no. 6, pp , [3] J J and Jayalekshmi Review on rtificial Neural Network Concepts in tructural Engineering pplications Int. J. Appl. Civ. Environ. Eng., vol. 1, no. 4, pp. 6 11, [4] R mardeep and D T wamy Training Feed forward Neural Network With Backpropogation lgorithm Int. J. Eng. Comput. Sci., vol. 6, no. 1, pp , [5] Tully neural network approach for predicting the structural ehavior of concrete sla s emorial University of Newfoundlan H R and B R Ba u Hy rid neural network model for the design of beam subjected to ending and shear Sadhana, vol. 32, no. 5, pp , [6] Deepak and K Ramakrishnan NN modelling for prediction of compressive strength of concrete having silica fume and metakaolin Int. J. ChemTech Res., vol. 8, no. 1, pp , [7] EM trength Design for Reinforced-Concrete Hydraulic tructures Washington, DC , USA, [8] S.-K Jung and K cdonald Visual gene developer: a fully programma le bioinformatics software for synthetic gene optimization BMC Bioinformatics, vol. 12, no. 1, p. 340, [9] T Jepsen redicting concrete dura ility y using artificial neural network in Proceedings of Durability of Exposed Concrete containing Secondary Cementitious Materials, [10] J mani and R oeini rediction of shear strength of reinforced concrete eams using adaptive neuro-fuzzy inference system and artificial neural network Sci. Iran., vol. 19, no. 2, pp , [11] R B Bandi and R Hanchate Hybrid Neural Network Model for the Design of Footing Int. J. Eng. Res. Dev., vol. 4, no. 2, pp , [12] C. Mahesh and E. Kannan, An Intelligent System wit h A Novel Approach for Diagnosing Hepatitis Viruses using Gen eralized Regression Artificial Neural Network, International Journal of Civil Engi neering and Technology, 8(9), 2017, pp [13] Upendra R.S, Pratima Khandelwal, Veeresh A V, Appli cation of artificial neural network statistical design (ann) in enhanced production of biopharmaceuticals, International Journal of Computer Engineering & Technology (IJCET), Volume 6, Issue 3, March (2015), pp [14] Dharmendra Kumar singh, Pragya Patel, Anjali Karsh, Dr.A.S.Zadgaonkar, Analysis of Generated Harmonics Due to Cfl Load on Power System Using Artificial Neural Network, International Journal of Electrical Engineering & Technology (IJEET), Volume 5, Issue 3, March (2014), pp editor@iaeme.com