This certifies that the dissertation entitled “Modelling of Degradation of Flexible Pressures with Modified Bitumen in Wearing Layer” submitted by Sanjay Deori to the Indian Institute of Technology Guwahati for the award of the degree of Doctor of Philosophy is a record of bona fide research work carried out under our supervision and guidance. Considering the fact that high-speed road corridors are vital for the socio-economic development of the country, adopting a scientific approach for their maintenance is imperative.
DATA BASE AND INFLUENCING FACTORS 25
INTERNATIONAL SCENARIO OF PAVEMENT
PAVEMENT DETERIORATION MODELLING UNDER
METHODOLOGY AND TIME SERIES DATA COLLECTION
CALIBRATION OF HDM-4 DETERIORATION MODELS 127-145
VALIDATION OF HDM-4 DETERIORATION MODELS 147-188
DISCUSSION ON VALIDATION OF MODELS 184
SUMMARY AND CONCLUSIONS 189-193
RECOMMENDATIONS FOR FUTURE WORKS 193
LIST OF PHOTOS
LIST OF TABLES
LIST OF ABBREVIATIONS
HDM-4 Highway Development and Management System-4 HDM-III Highway Design and Maintenance Standards Model-III HMA Hot Mix Asphalt. ROMDAS Road Measurement Data Acquisition System RSR RMSE to the standard deviation ratio of observations RSS Residual sum of squares.
- INTRODUCTION TOPAVEMENT TYPES
- INDIAN ROAD NETWORK .1 General
- National Highways
- Use of Modified Binders on Indian Highways
- Maintenance of Indian Roads
- PAVEMENT DETERIORATION MODELS .1 General
- Use of Highway Development and Management (HDM-4) Model
- NEED OF THE STUDY
- OBJECTIVES OF THE STUDY
- ORGANISATION OF THE THESIS
Keeping in view the importance of national highways in the overall road network of the country, the Government of India took a historic decision to widen and strengthen the existing NHs under the National Highway Development Program (NHDP). The total length of National Highways in India has continuously expanded from 58,112 km at the end of the 9th Five Year Plan and reached 1,00,087 km present in the 12th Five Year Plan NHAI, 2017). In the Twenty-Fourth Road Development Plan for the current period, it was decided that the high-density traffic corridors of the National Highways will be converted into Motorways. A summary of the work and the conclusions drawn on the basis of this study are presented in Chapter 7.
HISTORICAL BACKGROUND OF PAVEMENT DETERIORATION MODELS Pavements are the most important for day-to-day transportation, socio-economic
The World Bank released the third version of the HDM (HDM-III) in 1981. In 1993, an international collaborative study known as the "International Study of Highway Development and Management (ISOHDM)" was launched to expand the scope of the HDM. -III models.
PAVEMENT DETERIORATION MODELS
- Empirical Method
- Mechanistic – Empirical Method
- Probabilistic Method
- Bayesian Method
In this method, the road condition is treated as a random variable with probabilities associated with its values. In this analysis, the regression parameters are treated as random variables with an associated probability distribution.
DATA BASE AND INFLUENCING FACTORS
MODEL FORMS AND ACCURACY
Such conditions limit the model form to those suitable for the measurements of road surface condition which are modeled as follows: initial condition, initial slope, general trend, slope variation, final slope and final condition (Lytton, 1987). The first approach is to normalize the condition of the road surface to a dimensionless condition, so that a road surface in its "new" or original condition has a damage value of zero, and its "terminal" condition has a damage value of one.
INTERNATIONAL SCENARIO OF PAVEMENT DETERIORATION MODELS Karan (1977) investigated pavement deterioration functions by means of Markov
Wang (2000) calculated the pavement performance by the model depending on the pavement condition index for individual distress. The Pavement Condition Index (PCI) concept was developed for assessment of pavement condition by US Army Corps of Engineers (Shahin, 2002).
PAVEMENT DETERIORATION MODELLING UNDER INDIAN SCENARIO Central Road Research Institute (CRRI), New Delhi (CRRI, 1986) conducted a
Deflection progression models were developed for four pavement age categories based on initial deflection intervals. IRID = IRI due to distress in m/km only; RLp = low severity raveling in % of area; RMp = moderate revving in % of the area; RHp = high severity raveling in % of area; PALp = low severity patching in % of area; PAMP = moderate patching in % of area; PAHp = high severity patching in % of area; PLp = low severity holes in % of area; PMP = medium holes in % of area; PhD.
USE OF HDM-4 IN INTERNATIONAL SCENARIO
Namibia – The Namibian Roads Authority has practiced a comprehensive and formal Roads Management System over a period of ten years. Pakistan – The Communications and Works Departments of the four provinces of Pakistan: Punjab, Sindh, North West Frontier Province and Balochistan were responsible for maintaining a total of over 90,000 km of roads. HDM-III was used as the tool for preparing road deterioration and vehicle operating cost models (Vincent et al., 2000).
Portugal – Portugal's Maintenance Optimization System (MOS) PMS used a global deterministic pavement performance prediction model. South Africa - Botswana was the first county in South Africa to actively pursue the incorporation of HDM-III pavement performance models into their road management system (Rohde et al., 1998). United Kingdom - Kerala et al. 1998) introduced the newly incorporated features of HDM-4 over HDM-III.
INDIAN SCENARIO OF HDM-4 APPLICATION
These models were validated using percent variability and coefficient of determination (R2) to check the accuracy of the calibrated models. KENLAYER computer program was used for determining the damage ratio using emergency models, while HDM-4 software was used for predicting the pavement performance using pavement failure models. It was observed that the results of ruts and cracks distress models in the KENLAYER program differ from the results of ruts and cracks degradation from HDM-4 software.
The results of the study showed that the pavement life predicted by HDM-4 was less than that predicted by the KENLAYER program. 2010) developed pavement deterioration models using the HDM-4 tool for four rural road test sections identified in Warangal district of Andhra Pradesh state. Road inventory data, pavement condition data, and traffic volume data were used for HDM-4 analysis for responsive and planned maintenance. The optimal calibration factor for each model was determined based on the average absolute error (AAE), root mean square error (RMSE) and coefficient of determination (R2) values.
OVERVIEW OF HDM-4 PARAMETERS
HIGHWAY DEVELOPMENT AND MANAGEMENT (HDM) SYSTEM .1 General
- Background of HDM-4 Developments
- Objectives of the HDM-4 Development
- Enhancements in HDM-4
Renowned research organizations and academic institutions of the world have contributed immensely to its development over the last four decades. Default data and calibration coefficients can be defined in a flexible way to minimize the amount of data that needs to be changed for each HDM-4 application. Different series of HDM versions have been developed to properly organize road maintenance and road rehabilitation according to available budget.
Early versions of HDM were based on simple empirical regression models based on field data collected from specific case studies. By specifying standard data sets, the local adjustment and calibration of HDM-4 models can be achieved. In HDM-4, new approaches were developed for applying field data and current knowledge of the technical problems and management needs of different countries (Odoki and Kerali, 2000).
- Input Data
- Technical Models
- Application Modules
- Project analysis –The project analysis deals with the evaluation of one or more road project proposals. It examines road links or sections with user defined
- Strategy analysis –This is medium to long term planning of funding needed for road network development and maintenance. This strategy is
- Interface to External Systems
- Life-cycle Analysis
Vehicle fleet – It includes vehicle type and load, vehicle speed, operating costs, travel time costs and other vehicle effects. The application modules for HDM-4 models are used for project analysis, program analysis and strategy analysis (Kerali et al., 2000). It examines road links or sections with custom maintenance and rehabilitation treatments with associated costs and benefits, which are the most important factors in project analysis.
The main difference between strategy analysis and program analysis is the way the road connections and trajectories are identified. The life cycle analysis consists of costs for the road agency for maintenance and improvement of the road network and costs for road users for vehicle operation, travel time for passengers and. For each road section, the model simulates the road condition and data used for maintenance.
MODELLING IN HDM-4
- Modelling Concepts and Approach
- Information Quality Level Concept
- Pavement Classification System
- Pavement Strength
- Structural number –The structural number concept is based on AASHO road test, which is essentially based on the measure of total thickness of the road
- Modified structural number – To extend the scope of subgrade contribution to pavement strength, a modified structural number has been derived
- Adjusted structural number–In HDM-4, pavement strength is characterised by the Adjusted Structural Number (SNP), an index representing the
- Pavement Distress
- Deformation distress – This category of distress comprises of rutting and roughness. Deformation distress modes are continuous and represented by only
- Distresses Modelled in HDM-4
- Ravelling –Ravelling is a common distress in poorly constructed, thin bituminous layer such as surface treatment, but is rarely seen in high quality, hot-
- Rutting –The HDM-4 rut-depth model is based on four components of rutting described below
- Key Variables Affecting Pavement Deterioration
- Climatic and environment characteristics –It is well known that pavement strength changes during the course of a year due to climatic effects
- Traffic characteristics –The primary traffic related variables that effect road deterioration include the number and types of vehicles using the road, and
- Traffic data types– Traffic data types can be considered under the following headings
- Axle loading –The following measures of axle load are required to predict the impacts of traffic on pavement deterioration and maintenance effects
- Pavement structural characteristics –These include measures of pavement strength, layer thickness, material types, construction quality, and
Traffic Composition – Traffic composition is defined as the proportion of different types of vehicles using the road. Traffic Volume - The traffic volume entered for each vehicle type is expressed as Average Annual Daily Traffic (AADT) as given in Equation 3.4. Number of vehicle axles (YAX) - The number of vehicle axles, YAXk, crossing a given road section in a given year is calculated as the volume of traffic multiplied by the number of axles per vehicle type in question, as given by equation 3.6. .
Number of equivalent standard axle loads (ESAL) – The expression for calculating ESALF is given by equation 3.8. Cumulative traffic load – The cumulative number of equivalent standard axle loads since the last rehabilitation, reconstruction or new construction is given by equation 3.10. The Vehicle Damage Factor (VDF) is a measure of the damage that a heavy vehicle causes to the pavement.
HSNEW 1 + HSOLD 1 – MLLD (3.13) where,
- Material properties –Different materials are used in different pavement layers. The strength requirements of the materials used decreases from top to
- Construction quality –Poor construction quality of pavement results in greater variability in material properties and performance. The initiation and
- DETERIORATION MODELS IN HDM-4
- Features of HDM-4 Road Deterioration Models
- Model Forms of HDM-4 Road Deterioration Models
- ROAD WORKS EFFECTS .1 Roadworks
- Roadworks Modelling
- Roadworks Classification
- Work Standards
- Intervention Criteria
- Overall Computational Procedure
- OUTLINE OF DETERIORATION MODELLING METHODOLOGY
- IDENTIFICATION OF HIGH-SPEED ROAD CORRIDOR SECTIONS
- Formulation of Section Matrix
- Criteria for Preliminary Identification of Study Sections
- SELECTION OF TEST SECTIONS UNDER DIFFERENT CLIMATIC AND ENVIRONMENTAL CONDITIONS
- Indian High Speed Road Network and Climate
- Climatic and Environmental Zones of High-Speed Road Corridors Network
- PRELIMINARY DETAILS OF TEST SECTIONS
- Description of Selected High-Speed Road Corridor Networks
- Types of collected data
- ROAD NETWORK DATA COLLECTION .1 General
- Road network surveys
- Definition of Road Network Elements
- Inventory Data
- Structural Evaluation
- Functional Evaluation
- Evaluation of Pavement Materials
- VEHICLE FLEET DATA .1 Categories of Vehicles
- Traffic Volume Counts
- Axle Load Survey
- GROUPING OF SECTIONS
ARVa = landslide area at the beginning of the analysis year (% of the total area of the carriageway). Periodic maintenance – Periodic maintenance works on bituminous roads include the following: resealing or resurfacing, preventive treatment, overlay, grinding and replacement of layers and reconstruction. Reference System – The 'Kilometer-Knot' reference system is used for positioning data relating to pavement sections.
Traffic Flow Pattern - The traffic flow pattern in case of each pavement section is defined as 'Inter City' type according to the temporal distribution of the traffic. The temperature and precipitation characteristics of the study sections according to HDM-4 are shown in Figure 4.7. Geometry Class – The geometry class for each pavement section was defined in terms of the various parameters that reflect the horizontal and vertical curvature. Measurement of crack area – The pavement sections were divided into a number of representative test sections of length 50m for crack measurement.
Laser Profile Straight Edge Calibration – Straight Edge Calibration will calculate offsets relative to a straight surface. Laser Profile Straight Edge Confirmation Test – The straight edge confirmation test measures whether there is any bowing or bending in the straight edge used for the calibration test.
CALIBRATION OF HDM-4 DETERIORATION MODELS
- NEED FOR CALIBRATION
- Steps for Calibration
- Levels of Calibration
- Calibration Level of the Present Study
- METHODOLOGY ADOPTED FOR CALIBRATION
- Determination of Distress Initiation Calibration Factors
- Determination of Distress Progression Calibration Factors
- RESULTS OF CALIBRATION FACTORS
- DISCUSSION ON CALIBRATION RESULTS
These sections were continuously monitored for 3 years (2011 to 2013) for pavement surface condition time series data to conduct a Level 2 study. The time series data collected included pavement distress data, traffic and axle load data, pavement crust composition data, pavement material characterization data, temperature and precipitation data, and maintenance history. Based on this method, the initiation calibration factors for all the six groups consisting of 23 pavement sections were determined for the following emergencies:. The range of calibration factors as suggested by HDM-4 ranges from 0 to 20 with a default value of 1.
The HDM-4 calibration factors of each disturbance mode were determined for all twenty-three (23) pavement sections classified into six (6) individual homogeneous groups. Calibration factors were determined for pavement deterioration models of various disturbances integrated into HDM-4 for each of the six groups. Based on the values obtained for the calibration factors; interpretations are drawn regarding the delayed or advanced onset or progression of pavement disturbances compared to HDM-4 models with default values.
VALIDATION OF HDM-4 DETERIORATION MODELS
VALIDATION OF DISTRESS MODELS FOR DIFFERENT GROUPS
- Validation for Sections in Group-1 Cracking Progression
- Validation for Sections in Group-2 Cracking Progression
- Validation for Sections in Group-4 Cracking Progression
- Validation for Sections in Group-5 Cracking Progression
The observed and predicted values of texture depth at the end of the year 2013 and their variations are given in Table 6.5. The observed and predicted values for slip resistance at the end of the year 2013 and their variations are given in Table 6.6. The observed and predicted values of crack area at the end of the year 2013 and their variations are given in Table 6.7.
The observed and predicted values of pothole at the end of the year 2013 and their variations are given in Table 6.9. The observed and predicted values of roughness at the end of the year 2013 and their variations are given in Table 6.10. The observed and predicted values of roughness at the end of the year 2013 and their variations are given in Table 6.16.