Estimation of IRI from PCI in Construction Work Zones

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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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