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Development of Pavement Maintenance Management System (PMMS) of Urban Road Network Using HDM-4 Model.pdf

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    ©  2017 T. Chopra, M. Parida, N. Kwatra, J. Mandhani published by International Journal of Engineering & Applied Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 14  International Journal of Engineering & Applied Sciences (IJEAS) Vol.9, Issue 1(2017) 14-31 http://dx.doi.org/10.24107/ijeas.286473 Development of Pavement Maintenance Management System (PMMS) of Urban Road Network Using HDM-4 Model Tanuj Chopra a*  , Manoranjan Parida b  , Naveen Kwatra c  , Jyoti Mandhani d    a,c,d   Department of Civil Engineering, Thapar University,Patiala,India b  Department of Civil Engineering, Indian Institute of Technology, Roorkee,India *  E-mail address : tchopra@thapar.edu  Received date: January 2017 Abstract The aim of the study is to develop Pavement Maintenance Management System (PMMS) for four road sections of urban road network (Patiala, Punjab, India) using Highway Development and Management (HDM-4) model. The HDM-4 provides a deterministic approach in data input and process data of existing road condition, traffic volume and pavement composition to predict road deterioration as per the urban road conditions in terms of  International Roughness Index (IRI) value. This study presents the use of HDM-4 model for the computation of optimum Maintenance and Rehabilitation (M&R) strategy for each road section and comparative study of  scheduled and condition responsive M&R strategies. The results of present study will be useful for gaining better support for decision-makers for adequate and timely fund allocations for preservation of the urban road network.   Keywords:  Pavement, management, maintenance, HDM-4, urban road, predict, road deterioration. 1. Introduction Construction of road network involves substantial investment and therefore proper maintenance of these assets is of paramount importance. It is found that the actual available maintenance expenditure amount is much less than what is required for urban roads. It is a complex problem of matching of resources, time, materials, labour, equipment, funds, design and decision making. Therefore, maintenance and preservation of pavements should have a great national interest. Pavement Maintenance Management System (PMMS) consists of a comprehensive, coordinated, sets of activities associated with the planning, design, construction, maintenance and evaluation. Thus, PMMS can be used in directing and controlling maintenance resources for optimum benefits. Indeed, as a developing state, Punjab (India) pavements have some threats like increase rate of deterioration, rapid traffic growth, poor maintenance, overloading of vehicles, improper design and implementation, insufficient information for decision making and inefficient current traditional management system. Therefore, it is very essential to develop PMMS for the urban roads of Punjab and in this study, Patiala city has been taken to develop PMMS. Highway Development and Management Tool (HDM-4) was developed by World Bank to offer a powerful system for road maintenance and investment alternatives analysis, Archondo-Caallao [4]. HDM-4 analytical framework is based on the concept of pavement life cycle analysis. This is applied to predict road deterioration effects, work effect, user effect etc . Many researchers are working on the development of PMMS and effect of road surface deformations (Aydin and Topal [5], Ben-Edigbe [7], Ben-Edigbe and Ferguson [6], Ghasemlou et al. [9], Kerali et al.  T. Chopra, M. Parida, N. Kwatra, J. Mandhani 15 [16], Pienaar et al. [26], Aggarwal et al. [1], Aggarwal et al. [2], Shah et al.   [29], Girimath et al.   [10], Gupta et al. [11]).   Jain et al. [14]   calibrated the HDM-4 pavement deterioration models for Indian National Highway Network located in the Uttar Pradesh and Uttaranchal states of India. They developed pavement performance prediction models for the major modes of distress, including cracking, raveling, potholes, and roughness on the basis of collected data. They also compared Indian deterioration models with HDM-4 deterioration models. Gupta et al. [11]   determined the remaining service life of three road sections of Panchkula (Haryana). They concluded that from remaining service life values, all the selected road sections (in their study) would become candidates for reconstruction within 4 to 6 years without any maintenance work assigned during analysis period.  Aggarwal et al. [3]   developed PMMS for five Indian National Highway network using HDM-4 model; five National Highways, all were within the boundaries of Dehradun & Haridwar districts of Uttarakhand state and Saharanpur & Muzaffarpur districts of Uttar Pradesh state, comprising of total length of 310 km.    Naidu et al. [24] developed maintenance management plan based on economics of life cycle costs using HDM-4 and he selected Inner Ring Road of New Delhi in his study. Dattatreya et al. [8] carried out a study on primary roads of Bangalore Metropolitan Development Authority Area. On the basis of their study, the requirements for the first stage road improvement  program were worked out, consisting of strengthening and resurfacing of pavements, improvements in the drainage system and sidewalks and adopting measures to prevent early damages to pavements due to the leakage of water from pipes underneath the pavements and cutting of pavements across the roads at frequent intervals to take the service lines. Reddy et al. [28] developed a method of allocation of maintenance and rehabilitation costs based on the volume of commercial vehicles duly considering the load carried by them and the  performance of pavements. They determined the cost allocation strategy for optimum maintenance and strengthening considering the yearly increase in vehicle operation cost due to the cumulative traffic loading during the analysis period. Reddy and Veeraragavan [27] developed a simple priority-ranking module that provided a systematic procedure to prioritize road pavement sections for maintenance depending upon the budget constraints. In their module, pavement sections under the jurisdiction of a highway agency were prioritized based on an overall pavement performance index derived from a combination of pavement surface distresses, traffic information and expert opinion. Jain et al. [15] developed optimum maintenance and rehabilitation strategy for multilane national highways by using programme analysis component of HDM-4 software. They had selected one expressway (from Noida to Greater Noida) divided into five sub-sections and one National Highway (NH-24, Ghaziabad-Hapur) divided into eight sub-sections. They concluded that M&R strategy which had higher  NPV/CAP ratio was considered as optimum for the road section. On the basis of the economic analysis summary, they selected '25 mm SDBC Reseal and 40 mm BC overlay' for Expressway sections and for NH- 24 sections 'Thick Overlay of 40 mm BC' as the optimum M&R strategy having the maximum NPV/Cost among other alternatives. The literature of PMMS for Indian road conditions revealed that there is no any PMMS developed for Indian urban roads and all the related studies of PMMS have been done for high category roads like national highways and expressways. The aims of the present study are to develop PMMS for urban road network of Patiala (Punjab, India) using HDM-4 model to determine optimum Maintenance and Rehabilitation (M&R) strategy for road sections of Patiala city using Project Analysis in HDM-4 model, prioritize Patiala city road sections  based on optimum M&R strategy and to perform the comparative analysis of scheduled and condition responsive M&R strategies. The analysis period of 12 years (2017-2028) and for optimum M&R strategy Net Present Value (NPV)/Cost ratio have been taken.  T. Chopra, M. Parida, N. Kwatra, J. Mandhani 16 2. Materials and Methods 2.1 Selection of Urban Road Sections Patiala city (Punjab, India) has urban road network comprising of 52 road sections. The whole road network comes under the jurisdiction of Punjab Roads and Bridges Development Board (PR & BDB). In the present study, four road sections of Patiala each comprise of one km stretch have been selected and details of selected road sections of city have been shown in Table 1. 2.2 Data Collection and Analysis  Primary data for PMMS include pavement condition ratings, costs, roadway construction and maintenance history as well as traffic loading. To identify and evaluate pavement conditions and determine the causes of deterioration, a pavement evaluation system should be developed that is rapid, economical and easily repeatable. For this, pavement condition data has been collected periodically to document the changes of pavement condition. The process of data collection was classified under following four categories:    Road Network Data    Traffic Volume and Vehicle Composition Data    Maintenance and Rehabilitation (M&R) Works Data    Road User Cost (RUC) Data Road Network data refer to inventory data, pavement history data, and pavement condition data etc.  Vehicle Fleet data include representation of vehicles with their basic characteristics. Maintenance and Rehabilitation Works data refer to details of maintenance activities for the road section. Costs data include road use cost data and data of cost of maintenance works. Table 1. Details of selected road sections of Patiala city Section ID Section  Name Description Section Length (Km) Road Properties Classification of Road PR-01 Bhadson Road From Central Jail to Sarabha  Nagar 1.00 Two-Lane Wide Road Collector Street PR-02 Bhupinder Road From Thapar University to Sahni’s Bakery  1.00 Four-Lane divided Road Collector Street PR-03 Passey Road From Thapar University to Charan Bagh 1.00  Narrow Two-Lane Road Collector Street PR-04 Ghuman Road From Passey Road to Civil Lines 1.00 Two-Lane Standard Road Local Street 2.2.1 Road Network Data 2.2.1.1 Road Inventory Data Road inventory data collection consists of road length (m), lane width (m), shoulder width (m), geometries of road sections, traffic flow pattern, design speed (km/h), flow direction and climate zone. Road sections have been visually inspected to get relevant information. Details of road section inventory data has been presented in Table 2(a) and Table 2(b).  T. Chopra, M. Parida, N. Kwatra, J. Mandhani 17 Table 2(a). Details of Inventory Data - 1 Section ID Section Length (km) Lane width (m) Shoulder Width (m) PR-01 1.00 8.4 1.4 PR-02 1.00 6.7 1.9 PR-03 1.00 6.5 1.6 PR-04 1.00 7.2 1.9 2.2.1.2 Pavement History Data Pavement history data (type of pavement, year of last construction, surfacing and maintenance) has been collected from Public Works Department (PWD) office and Municipal Corporation of Patiala records from the year 2011 and 2015, Government of Punjab [17]. Details of pavement history data of road are presented in Table 3. Table 2(b). Details of Inventory Data - 2 Section ID Traffic Flow Pattern Flow Direction Design Speed (km/hr) Climate Zone Drainage condition PR-01 Inter- Urban Two-way 50 North India Plain Good PR-02 Inter-Urban Two-way 50 North India Plain Fair PR-03 Inter-Urban Two-way 50 North India Plain Excellent PR-04 Inter-Urban Two-way 30 North India Plain Poor Table 3. Pavement history data Section ID Surfacing Material Type Current Surface Thickness (mm) Previous Surface Thickness (mm) Last Construction Year Last Rehabilitation Year Last Surfacing Year Last Preventive Treatment Year PR-01    B   i   t  u  m   i  n  o  u  s   C  o  n  c  r  e   t  e   (   B   C   ) 75 50 2003 2008 2013 2014 PR-02 75 50 2003 2009 2013 2014 PR-03 75 50 2004 2010 2014 2014 PR-04 75 50 2004 2009 2012 2012 2.2.1.3 Functional and Structural Evaluation of Road Pavements The functional evaluation like roughness measurement survey has been conducted to assess the riding comfort and safety over the pavement section as experienced by road users. Road roughness refers to surface irregularities in the longitudinal direction and has been measured with fifth wheel bump integrator or simply known as 'Roughometer'. The equipment has been towed by pick-up and operated with speed of 30 kmph and shown in Figure 1. Accumulated  bumps (in cms) has been noted down corresponding to length travelled (in km).   Unevenness Index (UI) = Bumps in cm/ Length travelled in km (1) UI value has been converted into International Roughness Index (IRI in m/km) by using the following equation given by Odoki and Kerali [25]:

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