International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Vol...
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20320140503011

  1. 1. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 100 NEURAL NETWORK MODEL FOR DESIGN OF ONE-WAY R.C.C SLABS USING GA/BPN Dr. B. Ramesh Babu Principal Anantha Lakshmi Institute of Technology & Sciences Anantapur, Andhra Pradesh, India. ABSTRACT The design of R.C.C slabs involves many constraints like different edge conditions, loading, geometry and I.S. 456-2000 code provisions. The design has to satisfy all the constraints. This paper demonstrates the applicability of artificial neural networks and genetic algorithms for the design of one –way slabs to satisfy all the design constraints. KEYWORDS: Artificial Neural Networks, Genetic Algorithms, One-Way Slabs, R.C.C. Slabs. 1. INTRODUCTION Due to different edge conditions, the designer has to design many number of one way slabs in any given building which becomes cumbersome. The present investigation proposes an alternative method for the easy design of one-way slabs using the principles of Genetic Algorithms and Neural Networks. Thus, the objective of the present work is to demonstrate the applicability of Genetic algorithms and Artificial Neural Networks for the structural design of one-way slabs. The networks have been trained with design data obtained from design experts in the field. 2. DESIGN OF ONE- WAY SLABS 2.1 Development of Simple and Genetic Algorithm Based Neural Network Models The development of simple Artificial Neural network model and Genetic Algorithm based neural network model for design of two-way slabs involves various steps such as INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND TECHNOLOGY (IJCIET) ISSN 0976 – 6308 (Print) ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100-106 © IAEME: www.iaeme.com/ijciet.asp Journal Impact Factor (2014): 7.9290 (Calculated by GISI) www.jifactor.com IJCIET ©IAEME
  2. 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 101 1. Generation of exemplar patterns. 2. Selection of network type. 3. Selection of input and output for the network. 4. Arriving at a suitable network configuration. 5. Training of a network. 6. Validation of the resulting network. These stages are addressed in the following sections. 2.1.1 Generation of Exemplar Patterns The objective of this work is to develop neural network models for the design of one-way slabs. This requires a comprehensive set of examples that cover various parameters influencing the design of one-way slabs. All the training examples should invariably satisfy I.S. 456-2000 code provisions. For the present work, all the training examples have been developed by presenting different one-way slab problems to various design experts. The experts were asked to provide designs satisfying I.S. code provisions. The design variables considered are the span, live load, grades of concrete and steel and diameters of reinforcements. The design values are obtained for different spans viz., 3.0m, 3.5m, 4.0m, 4.5m and 5.0m. Three different live load intensities viz., 1.5, 2.0 and 4.0 KN/m2 are considered. M20 and M25 grades of concretes have been used. Reinforcement steel of three different grades viz. FE 250, FE 415 and FE 500 have been used. For main reinforcement, two diameters viz., 8.0mm and10.0 mm are considered. For distribution reinforcement only 8.0mm diameter has been considered. For each set, the overall depth of slab required, main reinforcement spacing and distribution steel spacing are obtained. For the present problem, a total of hundred samples training examples are obtained from different experts such that these examples cover all the possible combinations of design variables considered. Out of these, seventy five examples have been used for training and twenty five examples are used for validation. 2.1.2 Selection of Network Type The present problem considered for the neural network application involves the mapping of the known parameters such as span lengths, loading, reinforcement details, So as to design the RCC slabs. The design is highly dependent on these parameters as they interact among themselves in a non-linear fashion. Therefore, while selecting a network type its ability for mapping complex non- linear relationships must be considered for application. From the literature available, it is observed that feed forward neural networks have the ability to map such non-linear relationships. Hence, the feed forward form of neural architecture is used for the design of slabs. 2.1.3 Selection of Input and Output In the present work, it is required to develop neural network models for the design of one- way slabs. This means, the models should be able to predict the values of the depth of the section, spacing of both main and distribution reinforcements for a given live load, grade of steel and concrete. The input layer for the network has been configured taking in to account the possible parameters that may influence the output. As the network is supposed to map the functional relationship between the input and output parameters, the performance of the network is highly
  3. 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 102 sensitive to the input information. In addition, proper choice of input parameters improves the net performance for unseen problems i.e. the generalization capability. Accordingly the input to the network is chosen as follows: • span of the section (L) • Live load (wl) • Grade of concrete (fck) • Grade of steel (fy) • Diameter of main reinforcement (D1) • Diameter of distribution reinforcement (d1) Thus the input vector selected for this model is IP = {L, wl, fck, fy, D,d } ------------ (1) Although the relationship between the input parameters and macroscopic behavior of the material is highly non-linear, the quantitative degree of non-linearity is not clearly known. Hence, only the linear terms have been induced in the input vector. The network is expected to establish the degree of non-linearity through the training examples in an implicit manner. The designer would like to know the depth of the section and the areas of reinforcement for the given grade of concrete, grade of steel, live load and Edge condition. Thus the model should be able to predict the following. • Overall Depth of slab (D) • Spacing of main reinforcement (S1) • Spacing of distribution reinforcement (S2) Accordingly, the output vector for the neural network model is selected as OP = { D, S1,S2 } ---------------- (2) From the literature available it is learnt that computers work better for the values lying in between 0 and 1. So the input and output parameters have been normalized in the range (0, +1) using suitable normalization or scaling factors. This has been done by dividing the greatest entry at a node by a scale factor slightly greater than it.
  4. 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 103 2.1.4 Selecting a Suitable Network Configuration 2.1.4.1 GA/ BPN Model After doing a few trials, it is observed that the network with 10 neurons each in two hidden layer is behaving well. Accordingly a configuration of (6-10-3) has been selected for this network model. The architecture is depicted in Figure 1. Input layer Hidden layer Output layer (6Neurons) (10 Neurons) (3 Neurons) Figure 1. The Final GA/ BPN Configuration for one way slabs. 2.1.5 Training of the Network 2.1.5.1 Training of GA/BPN Model The training of the present network has been accomplished using the Back Propagation algorithm (BP). Conventionally, a BPN determines its weights based on a gradient search technique and hence runs the risk of encountering a local-minima . GA on the other hand is found to be good at finding ‘acceptably good’ solutions. The idea to hybridize the two networks has been successful to D S1 S2 L wl fck fy D1 d1 ▪ ▪
  5. 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 104 enhance the speed of training . In the present work, the weights for the BPN have been obtained by using a GA. Genetic Algorithms (GAs) which use a direct analogy of natural behavior work with a population of individual strings, each representing a possible solution to the problem considered. Each individual string is assigned a fitness value which is an assessment of how good a solution is to a problem. The high-fit individuals participate in “reproduction” by cross-breeding with other individuals in the population. This yields new individual strings as offspring which share some features with each parent. The least-fit individuals are kept out from reproduction and so they “die out”. A whole new population of possible solutions to the problem is generated by selecting the high- fit individuals from the current generation. This new generation contains characteristics which are better than their ancestors. The parameters which represent a potential solution to the problem, genes, are joined together to form a string of values referred as a chromosome. A decimal coding system has been adopted for coding the chromosomes in the present work. The network configuration chosen for the present work is 6-10-3. Therefore, the number of weights (genes) that are to be determined are 6X10+10X3= 90. With each gene being a real number, and taking the gene length as 5, the string representing the chromosomes of weights will have a length of 90X5=450. This string represents the weight matrices of the input-hidden layer-output layers. An initial population of chromosomes is randomly generated. Weights from each chromosome have been extracted then using the procedure suggested in reference. The fitness function has been devised using FITGEN algorithm. A constant learning rate of 0.6 and a momentum factor of 0.9 have been adopted during the training. Satisfactory training has been obtained after just 1500 training cycles. This indicates the efficiency of the Genetic Algorithm. The progress of the learning of the network is presented in Table 4.4. The training of the network accepted at this stage is depicted in figures 4(a-c). From these figures, it is observed that, the ANN model predicted the output which closely matches with analytical results. Fig 2. (a). Learning of GA/BPN Model for Overall Depth of Slab, (b). Learning of GA/BPN Model for Spacing of Main Reinforcement, (c). Learning of GA/ BPN Model for Spacing of Distribution Reinforcement (a) (b) (c)
  6. 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 105 2.1.6 Validation of the Network Models 2.1.6.1validation of GA/BPN Model Validation of the network is to test the network for the parameters that are not used in training of the network. The network was asked to predict the overall depth, main reinforcement spacing and distribution steel spacing of 25 sets which are not included in the training set. It can be seen from the figures 6 (a-c) that the values predicted by GA/BPN model for new sets match satisfactorily with results of design experts. It can also be noticed that the designs provided by the neural network model satisfy all the provisions of I.S. 456-2000. Hence it can be concluded that this neural network model can be successfully used to design different one-way slabs Fig 3. (a). Validation of GA/BPN Model for Overall Depth of Slab, (b).Validation of GA/BPN Model for Spacing of Main Reinforcement, (c). Validation of GA/BPN Model for Spacing of Distribution Reinforcement 3. CONCLUSION In this chapter the application of Genetic Algorithm based Neural Network model for the design of two-way slab problem have been demonstrated. The network model have been trained using one hundred examples obtained from different design experts. The training examples are so chosen that they will cover all the design variables involved in the problem. It is observed that both the model learned the design of one way slabs with good accuracy satisfying I.S. 456-2000 provisions. REFEENCES [1]. Rajasekharan, S., & Vijayalakshmi Pai,G.A., Neural Networks, Fuzzy Logic and genetic Algorithms, pp. 34-56,305-314, Prentice Hall of India , New Delhi,2003 . [2]. Davis, L. Hand book of genetic algorithms. Van Nostrand Reinholt, New York. 1991. (a) (b) (c)
  7. 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 100- 106 © IAEME 106 [3]. Ni Hong-Guang , & Wang Ji-Zong., Prediction of Compressive Strength of Concrete by Neural Networks, Cement and Concrete Research, Vol.30, 2000, pp. 245-1250. [4]. Sanad, A., & Saka, M.P., Prediction of Ultimate Strength of Reinforced Concrete Deep Beams by Neural Networks, ASCE Journal of Structural Engineering, Vol. 127, No. 7, 2001, pp. 818-828. [5]. Cladera, A., & Mari, A.R., Shear Design Procedure for Reinforced Normal and High Strength Concrete Beams using Artificial Neural Networks Part II: Beams With Stirrups, Engineering Structures. Vol. 26, 2004, pp. 917-926. [6]. Cladera, A., & Mari, A.R., Shear Design Procedure for Reinforced Normal and High Strength Concrete Beams using Artificial Neural Networks. Part I: Beams Without Stirrups, Engineering Structures, Vol. 26, 2004, pp. 927-936. [7]. Hadi, N.S., Neural Networks Applications in Concrete structures, Computers and Structures. Vol. 81, 2002, pp.373-381. [8]. Ghaboussi, J., & Joghatie., Active Control of Structures Using Networks, A.S.C.E Journal of structural engineering. Vol. 121, No. 4, 1995, pp.555-567. [9]. Mukharjee, A., & Deshpande., Application of Artificial Neural Networks in Structural Design Expert Systems, Computers and Structures, Vol. 54, No. 3,1995,pp. 367-375. [10]. Mishra, A.K., & Akhil Upadhyay., Column Design using ANN, ICI Journal, 2004, pp. 17-19. [11]. Rumel hart, D.E., Hinton, G.E., & Williams, R.J., Learning into representations by error propogation. In D.E Rumelhart. & McClelland (Eds), Parallel distributed processing: explorations in microstructure of cognition Cambridge. MA: Press. , 1986, pp. 318-362. [12]. Jenkins, W.M., Plane Frame Optimum Design Environment Based On Genetic Algorithm, Journal of Structural Engineering. Vol. 118, No.11, 1992, pp.3103-3112. [13]. Jenkins, W.M., Towards Structural Optimization Via The Genetic Algorithm, Computers and Structures.Vol. 40, 1991, pp. 1321-1327. [14]. Leite, J.P.B., & Topping, B.H.V., Improved Genetic Operators For Structural Engineering Optimization Advances in engineering software, Vol. 29, No. 7-9, 1998, pp. 529-562. [15]. Topping, B.H.V., & de Barros Leite, J.P., Parallel genetic Models for Structural Optimization. International Journal of Engineering Optimization, Vol. 31, No.1, 1998, pp. 65-99. [16]. Code of Practice for Plain and Reinforced Concrete I.S 456 – 2000. [17]. Santosh Patil and Shriniwas Valunjkar, “Forecasting of Daily Runoff Using Artificial Neural Networks”, International Journal of Civil Engineering & Technology (IJCIET), Volume 5, Issue 1, 2014, pp. 13 - 20, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. [18]. Hyeong-Joo Kim, Jose Leo Mission and Jeong-Hee Ko, “Artificial Neural Network (ANN) Prediction of One-Dimensional Consolidation in Homogeneous Clay Under Uniform Applied Load”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 4, 2013, pp. 135 - 145, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. [19]. Mohammed S. Al-Ansari, “Flexural Safety Cost of Optimized Reinforced Concrete Slabs”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 289 - 310, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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