1. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
71
NEURAL NETWORK MODEL FOR DESIGN OF ONE-WAY R.C.C SLABS
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 for the design of one –way slabs so as to
satisfy all the design constraints.
KEYWORDS: Artificial Neural Networks, 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 Neural Networks. Thus, the
objective of the present work is to demonstrate the applicability of 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 NEURAL NETWORK MODEL
The development of simple Artificial Neural network model for design of two-way slabs
involves various steps such as
1. Generation of exemplar patterns.
2. Selection of network type.
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ISSN 0976 – 6308 (Print)
ISSN 0976 – 6316(Online)
Volume 5, Issue 3, March (2014), pp. 71-76
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2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
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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 and 10.0 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 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
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:

3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
73
• 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.
2.1.4 SELECTING A SUITABLE NETWORK CONFIGURATION
2.1.4.1 SIMPLE BPN MODEL
As mentioned earlier, the network configuration is defined in terms of the number, size, nodal
properties, etc, of the input/output vectors and the intermediate hidden layers, once the input and
output vectors are decided to cater the present investigation requirements, the task of selecting a
suitable configuration has to be taken up. There is no direct method to select number of nodes in
hidden layers. Generally a trial and error method is adopted for arriving at the network configuration.
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 (6-10-10-3) has been selected for this network model.
The architecture is depicted in Figure 1.

4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
74
Input layer Hidden layer1 Hidden layer 2 Output layer
(6Neurons) (10 Neurons) (10 Neurons) (3 Neurons)
Figure 1: The Final BPN Configuration for one way slabs
2.1.5. TRAINING OF SIMPLE BPN MODEL
After choosing the network architecture, the training of the network for mapping the desired
relationship between the input and the corresponding output has to be carried out. Initially, the
weights and the thresholds matrix have been randomly generated using the facility in the software
ANNS. With this weight and threshold matrix, the network is subjected to the traditional back
propagation algorithm for training. A constant learning rate of 0.6 and a momentum factor of 0.9
have been adopted during the training.
A number of trials have been done for finding the number of hidden layers and to find the
number of neurons in each layer. After doing many trials it is decided to have a network with 2
hidden layers with 10 neurons in each layer. The performance of the network after every 1000
epochs is evaluated. One epoch consists of the presentation of the training examples and back
propagating the error for each training pair once. During the training cycles, it has been observed that
the weighted sum coming as input to a particular neuron does not change drastically. The thresholds
on the other hand, change rapidly, taking the output of the neuron towards the desired values. In back
propagation, the major factors in controlling the learning of a network is the learning rate and the
threshold values of different neurons. As the number of neurons in the neighborhood of a typical
neuron increases, the total weighted sum coming to it as its input also increases. And it takes little
D
S1
S2
L
wl
fck
fy
D1
d1
▪
▪
▪
▪

5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
75
time for back propagation algorithm to adjust the values of different thresholds for various neurons.
The network was trained for 20000cycles. As it can be seen from the graphs, after 20000 cycles, the
performance of the network is acceptable. At this stage the training of the network is terminated to
avoid over training. Such an over training may hamper the generalization capabilities of the network.
The training of the network accepted at this stage is depicted in figures 3(a-c). From these figures, it
is observed that, the ANN model predicted the output which closely matches with analytical results.
Thus, it can be concluded at this stage the network has learnt the relationship between input and
output parameters successfully.
Fig 3. (a). Learning of BPN Model for Overall Depth of Slab, (b). Learning of BPN Model for
Spacing of Main Reinforcement, (c). Learning of BPN Model for Spacing of Distribution
Reinforcement
2.1.6. VALIDATION OF THE 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 figures 5(a-c) that the values predicted by ANN 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 5. (a). Validation of BPN Model for Overall Depth of Slab, (b).Validation of BPN Model for
Spacing of Main Reinforcement, (c). Validation of BPN Model for Spacing of Distribution
Reinforcement
(a) (b) (c)
(a) (b) (c)

6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 71-76 © IAEME
76
3. CONCLUSION
In this chapter the application of simple BPN network model for the design of one-way slab
problem has been demonstrated. The network model has 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 the model learned the design of one way
slabs with good accuracy satisfying I.S. 456-2000 provisions. In the future work we consider the
design of two-way slabs.
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