Full Paper ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013
Estimation of IRI from PCI in Construction Work Zones R.Vidya1, Dr. S. Moses Santhakumar2, Dr.Samson Mathew3 National Institute of Technology/Department of Civil Engineering,Tiruchirappalli, India 1 vidyarajesh123@gmail.com,2moses@nitt.edu,3 sams@nitt.edu
Abstract—Roughness is good evaluator of performance of road. This paper presents a case study of IRI (International Roughness Index) estimation at NH 67 during four laning of Trichy - Tanjavur section. An attempt has been made to evaluate the IRI of construction work zones using LevenbergMarquardt back-propagation training algorithm. A MATLAB based model is developed, and the data from the case study are used to train and test the developed model to predict IRI. The models’ performances are evaluated through Correlation coefficient (R2) and Mean Square Error (MSE).
The objective of this study is to estimate IRI from PCI (Pavement Condition Index) for construction work zones using neural network modeling. The predicted values are compared with actual IRI values measured using MERLIN (Machine for Evaluating Roughness using Low-cost INstrumentation) along the construction work zones. Since poor pavement condition increases vehicle operating costs, accident costs and delay costs of the users, it is necessary to have certain guidelines for contracting work zones to calculate the cost incurred during reconstruction. Under the existing method of reconstruction, the traffic is invariably diverted over detoriated pavement segments and shoulders which increases the vehicle operating cost and reduces safety of the road users. The Management strategies of the construction work zones can be strengthened to ensure safety and comfort for which pay index can be formulated with help of IRI to assess the detoriated Pavement condition.
I n d e x Te r m s — R o u g h n e s s , I n t e r n a t i o n a l R o u g h n e s s IndexConstruction Work Zones.
I. INTRODUCTION Roughness is defined as the deviation of a surface from a true planar surface with characteristics dimensions that affect vehicle dynamics and ride quality (ASTM Specification E8672A). Many indices are developed for quantification of road roughness. Some widely used indices include International Roughness Index (IRI), Ride Number (RN), Profile Index (PI) etc. The International Roughness Index (IRI) was established in 1986 by the World Bank. It was first introduced in the International Road Roughness Experiment (IRRE) that was held in Brazil. The IRI is internationally accepted standard for calibration of roughness measuring instruments. The IRI is based on simulation of the roughness response of a car travelling at 80 km/h which expresses a ratio of the accumulated suspension motion of a vehicle, divided by the distance travelled during the test IRI and RN are commonly used because of their stability and reproducibility [2]. Artificial neural networks, Genetic programming and Fuzzy techniques have great variety of applications in Transportation engineering and are capable of modeling uncertain relationships. Numerous researches have been conducted to evaluate pavement condition. Rada [1] proposed a life cycle cost model and a cost effectiveness method for project level pavement management. Mactutis [4] et al had done investigations on the relationship between IRI, rutting and cracking using large database. Dewan and Smith [5] had derived a linear relationship between IRI and pavement condition based on 39 observations. Lin et al [6] had analyzed the relationships between IRI and pavement distress based on a backpropagation neural network methodology. Yousefzadeh et al [8] had discussed the capability of using neural networks for road profile estimation using neural networks. © 2013 ACEE DOI: 01.IJCEE.2.1. 22
II. STUDY AREA AND DATA COLLECTION The Thanjavur (Ch 80+000) to Trichy (Ch 136+490) section of the National Highway 67 is of 56.490 Km length and connects the two districts Tiruchirappalli and Thanjavur.The highway serves the people of industrial area, educational institution located thereon. This section contains two railway crossings, one major by pass at Vallam and around 30 other small bridges and culverts. Various test sites for the survey location with the bad pavement conditions were selected and data were collected on those sites (Fig.1).Measurements of longitudinal profiles were conducted along different sections in the four laning of Trichy–Tanjavur section of the NH – 67 using MERLIN, IRI were determined from the collected data.
Fig 1. Images depicting detoriated pavement conditions of construction work zones along NH– 67 of Trichy –Tanjavur section
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Full Paper ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013 Insight into pavement roughness definition gives the idea that longitudinal profile measurement in the wheel path yields better roughness evaluation but it is possible to take many profiles along a line, hence well-defined path encountered by most of the vehicles should be traced, but the issue is that how best sample can be taken, therefore roughness is calculated by taking average of all single lane paths, with IRI obtained separately in each wheel path at intervals of 0.5 m (Fig.2).
Fig 2. IRI determination along construction work zones of NH 67 stretch using MERLIN
The road roughness can be determined using the equation: IRI = 0.593+0.0471D (2.4 <IRI< 15.9) where IRI is the roughness in terms of the International Roughness Index (m/ km) and D is measured from the Merlin chart. Indian Road Congress, SP 16 gives the norms for Roughness for Indian conditions. Cracking, Rutting and potholing are the three parameters included in the study of pavement distress. The distress survey for the pavement condition was done by visual rating as proposed by Amarnath et al. [7].Deduct value is defined as the value that represents the condition of the existing pavement. A series of curves have been developed for correcting the Total Deduct Value (TDV).The Pavement Condition Index (PCI) for each section of road stretchis calculated asPCI = 100 – CDVwhere, CDV = Corrected or Normalised Deduct Value (Not exceeding 100). The curves for determination of corrected deduct values depending on the number andcombination of distress parameters.Pavement Condition details along NH 67 Stretch of Trichy–Tanjavur Section is given in Table I.
Fig 3. Methodology Flowchart
shown in Fig. 4.The Sigmoid transfer function is chosen. Various network combinations were tried and numbers of trial runs were performed. Different number of hidden layer neurons and learning rate 0.001, 0.01 and 0.05 are examined and it is trained by Levenberg-Marquardt back-propagation training algorithm, of which 0.001 was found to be optimum for 1000 epochs. The output layer has only one node representing the IRI. The performance of the networks 3-4-1 is reasonably good. The correlation coefficient, mean square error and mean absolute error are determined. The model were developed using MATLAB software (The Math works Inc, 2008).
III. METHODOLOGY A. Model Development Input variables of the model include cracking, potholes and rutting and the output is IRI. The Input and Output values were normalized. Levenberg-Marquardt backpropagation training algorithm was used. The methodology of the model development is as shown in Fig.3.
Fig 4. Schematic Representation of neural network
IV. NEURAL NETWORK MODEL FOR ESTIMATION OF IRI
V. RESULTS AND DISCUSSION
The schematic representation of the network with three inputs, 1 hidden layer with 4 neurons and 1 output layer is
IRI were found manually for 43 test sections. PCI of these sections were also calculated using the variables namely
© 2013 ACEE DOI: 01.IJCEE.2.1.22
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Full Paper ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013 TABLE I. PAVEMENT
© 2013 ACEE DOI: 01.IJCEE.2.1.22
CONDITION DETAILS ALONG
NH 67
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STRETCH OF
TRICHY TANJAVUR
SECTION
Full Paper ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013 cracking, rutting and potholes by visual rating.The samples of IRI collected in construction work zones were divided into two parts, one for training and another for testing. Out of 43 samples, 70% of the samples were taken as training set and the remaining 30% were used for testing.The performance during training and testing is evaluated by performance indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE).They are computed as shown in (1)and (2)
where N is the number of observations, and Xi are the IRI Values. The performance of the model with their indices is shown in Table II with RMSE, MAE and R2 statistics.Coefficient of correlation for the training data of 0.94 and testing data of 0.88 was obtained as shown in Fig. 5. From the performance indices, it is evident that the model developed is reasonably good and it shows a good correlation between Predicted and actual IRI as shown in Fig. 6. TABLE II.PERFORMANCE INDICES OF
Fig 6. Scatter plot of predicted and Actual IRI
The null hypothesis cannot be rejected as t statwere lesser than tcritical value which indicates that the Predicted IRI are not significantly different from the actual one found by field surveys. The Model estimates IRI which is difficult to obtain from the field measurements in construction work sites These road segments during construction incur additional vehicle operating costs, due to pavement distress thereby increasing the road users costs. The predicted IRI from the model created exclusively for work zones can be used to find the monetary value of costs as well as the pay adjustment factor which will serve as condition evaluator and will help in effective management of pavement maintenance.
THE MODEL
CONCLUSIONS In this study, the potential of artificial neural network methods, Levenberg-Marquardt back-propagation is employed for the estimation of IRI from pavement condition for effective management of construction work zones. The developed model were trained and tested and the statistical criteria of performance evaluation were calculated. The model yields an R2 value of 0.86 and MSE of 0.041.The results indicate that the performance of neural network is satisfactoryand it is feasible for IRI prediction. Higherprecision can be obtained with large database and with more Input variables. REFERENCES Fig 5. Coefficient of correlation for the training data and testing data
[1] G. R. Rada, J. Perl, and Witczak, “Integrated model for projectlevelmanagement of flexible pavements,”Journal of Transport Engineering, vol. 112(4), pp. 381–399, 1985. [2] M.W. Sayers, D. T. Gillespie, and W. D. O. Paterson, “Guidelines for Conducting and Calibrating Road Roughness Measurements,”World bank technical paper No. 46, The World Bank Washington, D.C., U.S.A., 1986. [3] A. Tavakoli, M.L. Lapin, and F. Ludwig, “Pavement management system for small communities,”Journal of Transport Engineering, vol. 118(2),pp. 270–281, 1992. [4] A. J.Mactutis, S. H. Alavi, and W.C. Ott,Investigation of Relationship Between Roughness and Pavement Surface Distress Based on WesTrack Project,”Transportation Research
The accuracy of the system proposed is determined by ttest.This is done to validate to show the closeness between actual and Predicted IRI values at 0.05significancelevel.The result of the t-test is summarized in Table III. TABLE III. RESULTS OF STATISTICAL -TEST FOR PREDICTED AND ACTUAL IRI
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Full Paper ACEE Int. J. on Civil and Environmental Engineering, Vol. 2, No. 1, Aug 2013 Record, Journal of the Transportation Research Board, No. 1699,TRB, National Research Council, Washington, D.C, 2000. [5] S. A. Dewan, and R. E. Smith, “Estimating International Roughness Index from Pavement Distresses to Calculate Vehicle Operating Costs for the San Francisco Bay Area,” Transportation Research Record, Journal of the Transportation Research Board, No. 1816, TRB, National Research Council, Washington, D.C , 2002.
© 2013 ACEE DOI: 01.IJCEE.2.1.22
[6] Jyh-Dong Lin, Jyh-Tyng Yau, and Liang-Hao Hsiao, “Correlation Analysis Between International Roughness Index (IRI) and Pavement Distress by Neural Network,” 82th Annual Meeting of the Transportation Research Board, Washington, D.C, January 2003. [7] M.S Amarnath, Vivian Robert, L.Udaya kumar , “Arterial Roads for maintenance management,”Indian Highways Journal,pp. 41-51, 2008 [8] Mahdi Yousefzadeh, “Road profile estimation using neural network algorithm,” Journal of Mechanical Science and Technology,vol. 24 (3),pp. 743-754, 2010. [9] Howard Demuth, Mark Beale, and Martin Hagan,Neural Network Toolbox™ 6 User’s Guide.
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