ABSTRACT
Current road roughness deterioration modelling and analysis tends to focus on the prediction of roughness progression in terms of change in the IRI over time.
Since the IRI is simply a summary index of the actual roughness, which simulates the response of a specific type of vehicle (quarter-car), it is difficult to identify the factors that contribute to the deterioration of road roughness.
Understanding the factors that lead to the deterioration of roads and identifying the actual mode of road roughness deterioration will help road controlling authorities refine their specifications on road roughness requirements for road design, construction and maintenance to reduce their adverse influence on roughness.
In this work pavement condition data was collected using these techniques are referred to as automated data in this research project.

 

Abbreviations and acronyms
ASTM: American Society for Testing Materials
ARRB: Australian Road Research Board
FHWA: Federal Highway Administration
DWT: discrete wavelet transforms
GPS: general pavement studies
HDM: World Bank Highway Design and Maintenance Model, (HDM-3 and HDM4)
IRI: International Roughness Index
ISO: International Standards Organisation
LTPP:long-term pavement performance
NAASRA: National Association of Australian State Road Authorities
NZTA: New Zealand Transport Agency
OECD: Organisation for Economic Co-operation and Development

PCC: Portland Cement concrete
PI: profile index/profilograph index
PIARC: Permanent International Association of Road Congresses, known as World RoadAssociation
PSD: power spectral density

RAMM: road asset and maintenance management (database)
RMSVA: root mean square vertical acceleration
RN: ride number
RTRRMS: response type road roughness measuring system
SHRP: Strategic Highway Research Program
SPS: specific pavement studies
TRRL: Transport and Road Research Laboratory

CHAPTER ONE
1.0                                                        INTRODUCTION
1.1                                           BACKGROUND OF THE STUDY
In 2001, a long-term pavement performance (LTPP) programme was established to collect pavement condition data for 64 selected state highway sites throughout New Zealand, and was extended in 2003 to include 84 local authority sites.The data collected included:
• Longitudinal profile using the Australian Road Research Board (ARRB) walking profiler.
• Transverse profile using a purpose-built transverse profile beam.
• Texture recorded at selected sites using the NZ Transport Agency (NZTA) stationary laser profiler
• A visual assessment of the degree and extent of various pavement distress features
• Site notes detailing visible changes in site condition, the observed distress, and its likely effect on the pavement roughness and rutting
• Site-specific photos to record the various distress patterns as they occurred. Prior to this research, LTPP projects had already supplied data for most of the pavement deterioration model development; however, roughness was one of the models yet to be fully developed mainly because sufficient pavement condition data was not available to commence the type of analysis required. Consequently a comprehensive understanding of the factors that influence roughness and pavement deterioration was yet to be fully defined.

A better understanding of the roughness measurement and data processing outcomes was needed before a roughness deterioration model could be developed. To advance this understanding, the NZTA approved funding in August 2008 for a research project to enhance the application of roughness measurement and deterioration. This project attempted to clarify some of those issues through a detailed analysis of the pavement profile spectra energy, and the change in spectral energy with time. The longitudinal profile data collected on 146 state highway and local authority calibration sites formed the basis of the research and analysis for the project.

Road roughness affects road users on a daily basis because it is one of the road characteristics that influence our perception of the condition of the road.
Roughness is also the most important performance indicator for roading engineers and asset managers, and has wide-ranging applications in network management.
It is used:
• In economic analysis as a key factor in determining road user costs
• As one of the few direct measures that can define pavement condition on both an engineering or technical level, and therefore can be used in public consultation and reporting through measures such as smooth travel exposure
•As a maintenance decision driver modelled in road management systems such as TIMS.
The interesting feature of the longitudinal profile is that the wavelength of the profile may identify a number of different problems at different locations along the road.
As a general rule of thumb, longer wavelengths indicate problems deeper in the pavement, while shorter wavelengths indicate problems near the surface or in the wearing course.
It is important to realise we can get a wealth of information pavement profile measurements this has been poorly explored and only partially used in network level analysis.
Despite its usefulness, roughness is one of the most complex performance measures, both in terms of its measurement and deterioration over time.

1.2                                              OBJECTIVES OF THE STUDY
This research aimed to better understand roughness, the way it is reported, and its effect on pavement performance and deterioration. This was to be achieved by identifying factors which masked or diluted the effectiveness of roughness analysis in determining pavement deterioration.
The research aimed to:
• develop a method to characterize the roughness deterioration modes of different pavement sections of the New Zealand road network by analyzing the characteristics of the pavement longitudinal profile, i.e. to identify why the road failed by analyzing the longitudinal profile of the pavement
•apply the proposed method to analyze the effects of pavement type, traffic loading, environment and maintenance regime on the deterioration of road roughness, ie to get a quantitative indication of how much roughness would be due to, for example, the environment vs traffic loading
• take the first steps in developing a model that would predict road deterioration over time by gaining a more comprehensive understanding of the technology and deterioration mechanisms.

Literature review Set the context for the research and provides a brief summary of available technology. LTPP site categorization Identify an appropriate categorization method that could be used in this research.
The aim was to be able to compare pavements in different failure modes with corresponding roughness characteristics. For example, would this be based on pavement condition or profiles?
Wavelength analysis
Understand the IRI in describing roughness trends over time. Establish alternative reporting techniques that better quantified roughness decay over time.
Understand more fully condition-based roughness and environmental-induced roughness.

Factorial analysis Investigate different factor analysis techniques to ultimately demonstrate: whether the roughness measure could identify factors influencing roughness change over time the respective results for the different analysis techniques.

1.3                                   PROBLEM STATEMENT OF THE STUDY
It is well known that the International Roughness Index (IRI) was primarily developed to account for what the driver feels when driving on a particular pavement.
Although this approach makes perfect sense there are two limitations to the current IRI measure:

1 IRI development was based on vehicle technology of the mid-1980s (Sayers 1986), which is vastly different from the suspension characteristics
2 Because the development focused on vehicle response it excluded many of the components of the roughness profile of pavements.

Therefore, although it may give an indication of how the perceived roughness deteriorates, it is not an effective measure of how the road profile changes over time.
In order to address these problems, this research considered the following questions:

1 Does an alternative approach demonstrate that it is more capable of explaining changes in pavement condition over time?

2 Can the outputs from this alternative approach be correlated with the factors that influence the change?

3 Is it a practical measure that could calculate a repeatable and accurate process?

1.4                                                   SCOPE OF THE STUDY
Because different types of roughness are associated with different wavelengths, the subdivision of the roughness profile into individual sub-bands can assist with the interpretation of roughness deterioration.
Wavelet analysis, which is not yet widely used, provides a means of splitting out the different wavelength components found within the pavement profile and facilitates the analysis of each part of the composite separately.

This research project developed a procedure to analyze pavement profiles using wavelet analysis of the longitudinal profile data collected on 146 New Zealand calibration sites. Each pavement profile was analyzed and the energy content was split into six different wave bands (0.51m, 12m, 24m, 48m, 816m and 1632m).
The relative energy within each wave band was calculated for each year of data and trends in the change in energy from year to year and site to site were recorded. The results obtained were statistically analysed to highlight trends and identify failure modes.
A separate review of the physical deterioration the observed deterioration characteristics noted over the past six to eight years was also undertaken and these observations were analysed in tandem with and compared to the results obtained from the wavelet analysis.

The research was expected to provide an additional analysis process which would lead to identifying the source of the problems affecting pavement roughness. The research project was divided into the following four separate phases:
1 Review: A literature review was undertaken to ensure the research did not replicate work already completed, and also to give it focus.
2 Data processing: Collation and processing of the individual calibration site longitudinal profile data. Analysis and classification of the wavelet time domain transform. This included a pilot programme to ensure the adopted process could provide the anticipated results.
3 Analysis: Statistical analysis of the results obtained from the wavelet process, and a review of the observable deterioration occurring on the calibration sites.
4 Summary: A summary of the findings and how they might influence future roughness deterioration analysis and pavement maintenance.

 

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