Pavement Condition Forecasting Through Artificial Neural Network Modelling

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 11, November 2012)

474

Pavement Condition Forecasting Through Artificial Neural

Network Modelling

1

S.K.Suman,

2

S.Sinha

1Research Scholar, Department of Civil Engineering, National Institute of Technology Patna, Patna 2Associate Professor, Department of Civil Engineering, National Institute of Technology Patna, Patna

Abstract-Pavement surface condition forecasting model is key component of pavement management system for long term maintenance and rehabilitation. Thispaper presents an artificial neural network (ANN) approach to pavement surface condition forecasting using three layer back propagation algorithm. As an input data, present PCI and age of the pavement was taken. Future PCI was obtained after training and testing of the selected data.ANN has showed to be able to give a considerable contribution for supporting management decisions, in particular in the area of pavement performance prediction.

Keywords-Artificial Neural Network, Back propagation algorithm, ANN modelling for forecasting pavement condition

I. INTRODUCTION

The pavement condition forecasting models predict the deterioration of the pavement over time, which is manifested in various kinds of distresses. Pavement condition deterioration estimation is an integral part of the pavement managementsystem. In a developing country like India, the funds being limited for the maintenance of the existing pavement, it is important to utilize the money in the most appropriate manner [6].To utilized the scarce resources and limited budget on right time as well as at right place there is a need offirst development of the pavement condition forecasting models.

Pavement management system based on models which predict pavement deterioration based on present condition along with deterioration factors encompassing traffic load, environmental and construction propertiesand the effects of maintenance. However, many difficulties are associated with the measurements and/or precise estimations of the inputs involved in the deterioration models. The uncertainty in the determination of associated parameters constitutes to the difficulties encountered while developing pavement condition prediction models. In this direction, one of the

soft computing techniqueslike artificial neural network model has demonstrated to be particularly appropriate for such types of predictions.

The objective of this paper is to design aarchitecture of three layer back propagation neural network for pavement condition forecasting based on base year pavement surface condition & its age, andto examine the capability of this technique.

II. ARTIFICIAL NEURAL NETWORK (ANN)

An ANN is a massively parallel distributed information processing system that has certain performance characteristics resembling biological neural networks of the human brain.ANNs have been developed as a generalisation of mathematical models of human cognition or neural biology. Relevant rules are (i) information processing occurs at many single elements called nodes, also referred to as units, cells or neurons (ii) Signals are passed between nodes through connection links (iii) each connection link has an associated weight that represents its connection strength and (iv) each node typically applies a nonlinear transformation called an activation function to its net input to determine its output signals. A neural network is characterised by its architecture that represents the pattern of connection between nodes, its method of determining the connection weights and the activation function. The basic structure of a network usually consists of three layers: the input layer, where the data are introduced to the network; the hidden layer or layers, where data are processed; and the output layer, where the results for given inputs are produced. A simple example of an artificial neuron is shown in Figure 1.

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 11, November 2012)

475

It is a gradient descent technique that minimises the network error function. The explanation here is intended to give an outline of the process involved in back propagation algorithm. Each input pattern of the training data set is passed through the hidden layer of the network from the input layer to the output layer. The network output is compared with the desired target output and an error is computed. This error is propagated backward through the network to each node and correspondingly the connection weights are adjusted.

The BPA involves two steps. The first step is a forward pass, in which the effect of the input is passed forward through network to reach the output layer. After the error is computed, a second step starts backward through the network. The errors at the output layer are propagated back toward the input layer with the weights being modified.

The training is a process by which the connection weights of an ANN are adapted through a continuous process of stimulation by the environment in which the network is embedded. Supervised training procedure involves the iterative adjustment and optimization of connection weights and threshold values for each of nodes. The primary goal of training is to minimize the error function by searching for a set of connection strengths and threshold values that cause the artificial neural network to produce outputs that are equal or close to targrts.After training has been accomplished, it is hoped the artificial neural network is then capable of generating reasonable results given new inputs. During the testing, no learning takesplace.

The feed forward of the testing data is similar to the feed forward of the training data.

IV. ANN MODEL FOR PAVEMENT

CONDITION FORECASTING

The Architecture of back propagation three layer artificial neural network model for pavement condition forecasting modelling is designed as shown in figure 2.Two input variables, first present pavement age and second present pavement condition index (PCI) has been considered. Asan output of the model, future PCI has been taken. BPA first phase namely forward pass calculates the network output by propagating the input data through the network. The network output is then compared with the desired output to calculate the error using a backward pass; during the backward pass connection weights are modified to reduce the target error. Sigmoidal transfer function was used as a neuron transfer function between input layer to hidden layer and hidden layer to output layer. Network training represents acquiring the knowledge of forecasting the PCI value.MATLAB software package was used for training and testing the ANN model. Training was stopped when the mean absolute error, root mean squared error and mean absolute relative error reached a previously specified minimum value (0.001).

Net=∑wixi

Sigmoid-Transfer function

Output (j)

Typical(j) Neuron Synaptic

weights Inputs

Wnj

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 11, November 2012)

476

After the several trials, optimum back propagation neural network (BPNN) has been obtained and, the BPNN is used to forecast the next PCI value of the pavement section. This forecasted PCI value is then used as the preceding PCI value to forecast the next consecutive PCI value; the process is repeated until the desired forecasting period is reached.

V. APPLICATION OF BBNN

BPNN designed model was implemented on the pavement surface condition data and their age in years collected on national highways in Bihar. Distress manifestation was identified in terms of pavement condition index (PCI) using PAVER system methods.

Visual condition survey was carried out on seventy five selected sections of the highways have bituminous topped pavement and each section length of hundred meter was taken in the view of reaching towards effectiveness of the data.PCI was calculated for every sections and their age was obtained from the road construction department, Biharin the beginning of the year 2011.After one year, again in the beginning of the year 2012, PCI was calculated for the same selected pavement sections.

Collected data sets were trained using ten neurons and sigmoid transfer function BPNN. After hundred epochs, performance value is 7.3216e-006 which nearer to the goal as shown in

Figure3.

Target

Output

MSE

Back Propagation Algorithm for adjusting weight

F0RWARD PASS

BACKWARD PASS

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jk

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Hidden Layer (j)

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Pavement

Age

Present

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 11, November 2012)

477

Figure 3: Training of BPNN

The comparison of observed data sets and the estimated data sets are shown in Figure 4 which is almost same .About fifty percentages estimated data are higher than the observed data while fifty percent estimated data are lower than the observed data as indicated in Figure 5.

Figure 4: Comparison between observed and estimated value

Figure 5: Differences in observed & estimated PCI values

Performance error of the model are Mean absolute error = 0.184, Root mean squared error = 0.271 and Mean absolute relative error = 0.233%.It was found that the errors are much lower. From Figure 6 it can be observed that R2 = 0.999 is almost same during the ANN

training phase as well as testing phase.

Figure 6: Estimated versus observed PCI

Therefore designed BPNN model is capable for forecasting the pavement condition when age and present PCI is known. Figure 7 shows the forecasted PCI over fifteen years which indicates the trends similar to other techniques.

30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90

1 5 9 13172125293337414549535761656973

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1 5 9 13172125293337414549535761656973

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 11, November 2012)

478

Figure 7: Forecasted PCI over fifteen years

VI. CONCLUSION

In this paper, an overview of ANN has been presented along with design and functionaries of BPA.PCI was calculated for base year and future year based on visual surface condition survey.PCI estimated from the adopted model is almost same to the observed PCI in future year. Forecasted PCI over fifteen years indicates the trend of deterioration of the pavement surface condition that may be used for planning of maintenance and repair works in long run. However, the results indicate that the proposed model has good capability to be used to predict the surface condition in future of pavement sections.

This work was carried out on limited number of sections of the bituminous topped pavement. To get the best result of the pavement deterioration model, the range of parameter that used in BPNN training must be adopted in the entire range of variables that could be encountered a particular pavement family.

REFERENCES

[1] Kaur D. & Datta D., 2007, Soft computing technique in prediction of pavement condition,6th WSEAS Int. Conference Computational

Intelligence, Man-Machine Systems and Cybernetics,Tenerife,Spain,December 14-16,USA

[2] Arliansyah, J., Maruyama, T., and Takahashi, O., 2004, A pavement deterioration model using radial basis function neural networks, J. Materials Conc.Struc. Pavements,JSCE,No.753/Vol 62,165,177

[3] Shahin M.Y., 1994, Pavement Management for Airports Roads and Parking Lots, Kluwer Academic Publishers, Boston, London

[4] Ministry of Road Transport & Highways (MORTH),2004, Guidelines for Maintenance Management of Primary, Secondary and Urban Roads, Indian Roads Congress Publications, New Delhi

[5] A van der Gryp, Bredenhann S.J., Henderson M.G. and Rohde G.T., 1998, Determining the visual condition index of flexible pavements using artificial neural networks,4th International

conference on managing pavements

[6] Sandra A.K.,Vinayaka Rao V.R.,Raju K.S.,Sarkar A.K.,2010,Priotization of Pavement Stretches using Fuzzy MCDM Approach - A Case Study, Online at www.cs.armstrong.edu/wsc11/pdf,Accessed on February 21,2012.

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Figure 6: Estimated versus observed PCI

Figure 6:

Estimated versus observed PCI p.4
Figure 5: Differences in observed & estimated PCI values

Figure 5:

Differences in observed & estimated PCI values p.4
Figure 3: Training of BPNN

Figure 3:

Training of BPNN p.4
Figure 4: Comparison between observed and estimated value

Figure 4:

Comparison between observed and estimated value p.4
Figure 7: Forecasted PCI over fifteen years

Figure 7:

Forecasted PCI over fifteen years p.5

References

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