 Open Access
 Total Downloads : 637
 Authors : Richa Yadav, Monika Vyas, Vivekanand Vyas, Sanket Agrawal
 Paper ID : IJERTV5IS010431
 Volume & Issue : Volume 05, Issue 01 (January 2016)
 DOI : http://dx.doi.org/10.17577/IJERTV5IS010431
 Published (First Online): 21012016
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Cost Estimation Model (Cem) for Residential Building using Artificial Neural Network
Richa Yadav
Post Graduate Student, Civil Engineering Dept.,
Radharaman Engineering College, Vadodara
Vivekanada Vyas
Professor, Radharaman Engineering College,
Bhopal
Monica Vyas
Professor,
Oriental Institute of Science and Technology, Bhopal
Sanket Agrawal
Assistant Professor, Parul Institute of Technology,
Vadodara
Abstract The achievement of any project undertaking is defined by improved quantity and cost estimation technique that facilitates optimum utilization of resources. The objective of this study is to develop a cost estimation technique by using an artificial neural network (ANN) model that will be able to forecast the total structural cost of residential buildings by considering various parameters. In this study, data of last twenty three years has been collected from Schedule of rate book (SOR) and general studies. Eight input parameters, namely, cost of cement, sand, steel, aggregates, mason, skilled worker, nonskilled worker and the contractor per square feet construction were selected. The parameters were simulated in NEURO XL Version 2.1 for developing ANN architecture. The resulting ANN model reasonably predicted the total structural cost of building projects with correlation factor R0.9960 and RSquared0.9905 giving favorable training and testing phase outcomes.
Keywords Artificial Neural Network (ANN), Correlation Factor, Cost estimation, Model, Variables

INTRODUCTION
Cost is probably the first to be considered when it comes to construction projects. Accurate estimation of quantities and costs incurred in a construction project is a crucial factor in its achievement [1]. Because of the complexity of the construction industry and individuality of every project undertaking, several factors may affect the overall project cost. A number of objects, such as the structural, architectural, sanitary, electrical and airconditioning system workings conclude the total cost of buildings. Olotuah, 2002, observed that building resources incur approximately 60% of the total cost of a residential building [2]. Meanwhile, the structural casing covers 25% of total construction cost in a multistorey reinforced concrete residential building. Therefore, greatest worry must be exercised in the design of structural systems if a considerable reduction in cost is preferred. However, consistency and accuracy of available cost estimating techniques are matter of concerns presently. Thus, now there is a growing need to deal with the concerns by introducing a new and alternative approach for estimation of cost and to identify the factors responsible for variation of cost.
Usually, the ordinary least squares regression approach is applied and the replica is selected based on the coefficient of determine, R2. However, because of high correlation among a great group of variables, this technique tends to generate regression coefficients estimators that will badly perform in the presence of multicollinearity [3]. Furthermore, the variance of the usual least squares estimator become inflated, which results in the low prospect of the estimator being close to the correct value of the regression coefficient [4]. This can be rectified by determining uncorrelated variables to be included in the regression model. Lots of quantity and cost estimation models have been developed. Linear regression is a very useful statistical device for analyzing and predicting the input of a potential new item to the overall approximation. Several other methods have been applied for the cost estimation, such as, principle component study, case based reasoning and ANN.
Artificial neural networks can model complex nonlinear relationships and approximate any assessable function. ANN is a powerful means to handle nonlinear problems and subsequently map relations between complex input/output data and address uncertainties [5]. They are particularly helpful in problems where there is a complex relationship between an input and output. The main advantage ANN models have over physically based models is that they are datadriven and underlying contact using examples of the desired input output mapping [6].
Ismaail EISawy et al (2011) tried to develop a parametric costestimating model fiftytwo actual reallife cases of building projects, in Egypt, constructed during 20022009 and achieved an accuracy of 80% [7]. Jamshid Sodikov (2005) developed a more accurate estimation technique for highway projects in developing countries at the theoretical phase using artificial neural network [8]. H.Muarat et al (2004) in Turkey, used training and testing data from thirty projects of 48 storey reinforced concrete structure by neural network methodology, achieving an average cost estimation accuracy of 93% [9]. Emad Elbeltagi (2014) developed an ANN model to predict the cost of highway structure projects in Libya by considering various factors that influence the highway construction [10].
The motive of this work is to explore the ANN technique and predict the total structural cost of buildings and to determine the factors which affect the cost of buildings. Hence a cost estimation model (CEM) has been developed using artificial neural networks, particularly multilayer feed forward neural networks. The back propagation knowledge algorithm is used to instruct the network by iteratively processing a set of training sample and compare the network's prediction with the real. The variation in the estimation is propagated to the input for adjusting the coefficients. To accomplish this goal, structural cost data from past twenty three years from 1993 to 2015 were collected and were used simulated in ANN SOFTWARE.

METHODOLOGY
In this study a frame work has been developed for estimating the variation & construction cost for residential building. All the models where developed using data of input variables like cost of cement, sand, steel, aggregate, mason, skilled, nonskilled, Formwork, Brick work etc (Table 1). Also the rates of material that is input variables has been taken from Schedule of rate book of last 20 years. Pentium 4 class P.C. with window XP operating system is used to run NEURO XL Version 2.1 Artificial Neural Network software. For all the models, total data set is divided into training set (68%), validation set (16%), and test set (16%).
Table 1 Input parameters for ANN model.
Year
Cement (Rs/bag)
Sand (Rs/ft3)
Steel (Rs/kg)
Aggregate (Rs/ft3)
Mason (Rs/day)
Skilled Labor (Rs/day)
1993
80
2
6
2
110
50
1994
90
2
7
2
120
55
1995
100
3
9
2
125
60
1996
110
4
10
3
130
65
1997
120
5
12
3
135
65
1998
125
5
13
4
70
1999
130
6
15
4
145
75
2000
135
7
17
5
150
75
2001
135
9
17
5
155
80
2002
140
12
18
6
155
90
2003
145
15
19
6
160
95
2004
145
18
20
7
165
95
2005
150
19
21
7
170
105
2006
155
20
22
8
175
100
2007
160
22
23
8
180
120
2008
178
23
25
9
200
120
2009
190
24
28
10
220
150
2010
200
25
30
12
250
200
2011
200
25
33
16
300
250
2012
225
30
38
18
350
250
2013
260
40
40
20
400
300
2014
260
40
48
40
425
340
2015
290
60
34
23
450
360
Selection of parsimonious ANN structure was accomplished by first fixing the number of hidden layers and then choosing the number of nodes in each of these layers. For larger networks, computational costs are high and might overfit the training data with too many nodes. The presentation of one hidden layer ANN is found to be better than two hidden layers ANN [11]. Nodes in the hidden layers, are very important for characteristic extraction from the patterns of input time series, they normally have a very small weight changes and learn very slowly [12, 13]. Here single
hidden layer is used to generate feedforward entirely connected neural network (multilayer perceptron). For both hidden and output layer hyperbolic tangent activation function is employed. This function has a sigmoid curve and is calculated by using the following formula as in equation 1.1
F(x) = (ex – ex) / (ex + ex) ————— (Eq. 1.1)
The selected output range is [1 to 1]. SumofSquare is the error function used to rate the quality of the neural network. Training is stopped when the number of iteration become 500. As iteration is a single complete presentation of the training set to the neural network. This is the simplest and the most commonly used condition to stop training. When a neural network begins to overtrain (i.e. to memorize data instead of generalizing and encoding data relationships), the validation errors increase while training errors might still reduce in that case, one has to retrain and restore best network. When the training is completed the testing operation was performed. The performance of each network model was then evaluated by computing the mean absolute error for each model.

RESULTS AND DISCUSSION
The table 1.2 shows the actual and forecasted structural cost obtained through the ANN model. The output obtained from ANN model also includes Absolute and Relative errors in prediction. From the error percentage it is clear that maximum error is 8.58%, for year 2008, which is less than 10%. Hence the result indicates good estimation values with prediction above 90%.
Table 2 Output parameters obtained through ANN model.
Year
Contractor (Rs/ft2)
Forecast
Abs. Error
Rel. Error
Estimate
1993
290
312.40957
22.409572
7.73%
Good
1994
320
325.48742
5.4874173
1.71%
Good
1995
350
355.31718
5.3171772
1.52%
Good
1996
370
378.10013
8.1001263
2.19%
Good
1997
400
409.25739
9.2573891
2.31%
Good
1998
425
425.69218
0.6921805
0.16%
Good
1999
450
460.90113
10.90113
2.42%
Good
2000
475
490.3637
15.363698
3.23%
Good
2001
500
500.56361
0.5636101
0.11%
Good
2002
525
550.93636
25.936358
4.94%
Good
2003
550
591.69713
41.697126
7.58%
Good
2004
600
614.38795
14.387947
2.40%
Good
2005
650
655.11369
5.1136869
0.79%
Good
2006
700
668.14794
31.85206
4.55%
Good
2007
750
735.36585
14.63415
1.95%
Good
2008
850
777.0985
72.9015
8.58%
Good
2009
900
881.23432
18.76568
2.09%
Good
2010
1000
980.20803
19.79197
1.98%
Good
2011
1070
1028.7587
41.24135
3.85%
Good
2012
1100
1102.7111
2.7111141
0.25%
Good
2013
1200
1238.3095
38.309463
3.19%
Good
2014
1300
1314.485
14.485034
1.11%
Good
2015
1400
1303.8944
96.1053
6.86%
Good
The results were then plotted on the scatter graph between actual and forecasted values of last 23 years. The forecasted values generated through the ANN model were found to have very high correlation coefficient. The estimated correlation coefficient and Rsquared coefficient for the results obtained were 0.9960 and 0.9905 respectively as shown in Fig. 1.
Fig. 1 The scatter graph between actual and forecasted values.
The Table 3 shows the summary of the corelation factor between actual and predicted structural cost. The average Absolute Error (AE) obtained for Training Set and Test Set were 21.436 and 27.183 respectively. The predictions for each year from 1993 to 2015 indicated Good Forecasts for both Training (100%) and Test Set (100%), where the Tolerance level was 10% for Training Set.
Table 3 Summary of the results.
Training Set Test Set
No of Rows
19
4
Average AE
21.436
27.183
Average MSE
932.1315
1477.6754
Tolerance
10%
30%
No of Good forecasts
19 (100%)
4 (100%)
No of Bad forecasts
0 (0%)
0 (0%)

CONCLUSION
The motive of this work was to explore the ANN technique and predict the total structural cost of buildings and to determine the factors which affect the cost of buildings. The developed cost estimation model (CEM) with back propagation knowledge algorithm was used to instruct the network by iteratively processing a set of training sample and compare the network's prediction with the real. The generated model fairly forecasted the structural cost, where the correlation coefficient and Rsquared coefficient were found to be 0.9960 and 0.9905 respectively. The average absolute error of training set 21.43 and that of test set was 27.18, with the error varying from 8.58% (maximum) to 0.11% (minimum), indicating good error deviation during training. The accurate conversion of practical field data into real time data can bring major change in the construction industry by forecasting the cost of any project. Artificial neural networks can model complex nonlinear relationships and approximate any assessable function. The advance prediction of overall residential building cost can help the user in decisive planning. This model can also be used in future by various stakeholders, to study variation in the project cost, if the cost of various important resources like steel, cement, labor, etc. is changed.
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