Roughness

Paper presenting a combined index of surface condition and IRI for identifying maintenance treatments.

Roughness is an important indicator of road deterioration and has a significant impact on road serviceability. Conventional instruments for roughness measurement, such as laser profilers, are expensive and require a complex set-up, which limits the surveying frequency and coverage. As an
alternative, embedded sensors in smartphones mounted in vehicles have been leveraged to measure roughness indirectly, and multiple smartphone-based roughness index estimation (sRIE) systems have become available recently. However, there lacks a framework to evaluate the performance of sRIE systems in a systematic and repeatable manner. This research proposed an evaluation framework to assess the performance of sRIE systems in practical settings. The framework consists of statistical measures that evaluate the consistency and accuracy of sRIE systems under various mountings, vehicle types, and survey speeds. Three popular sRIE systems were assessed using the framework to validate their validity and practicality. By standardising the performance metrics, this framework allows for performance benchmarking between sRIE systems and conventional instruments.

The International Roughness Index (IRI) was developed in the 1980s in the United States of America as a way to estimate the longitudinal road roughness in order to be able to evaluate and maintain road infrastructure in an easy and standardized way. It can be determined by driving with a prepared vehicle, often referred to as High Speed Road Profilers (HSRP), over the road, and measuring the difference in suspension in the wheels by using different sensors over time. Nowadays, it is internationally recognized as a valid measurement, both in America and also in Europe, and also the methods for measuring the necessary data and calculating the index from this data are internationally standardized, e.g. how precise and accurate the sensors must be. The company Infrafocus is using a software called Road Doctor from a Finnish company called Roadscanners which provides two methods that both use Inertial Measurement Unit (IMU) data to derive the road profile, instead of measuring it directly with conventional sensors such as a walking stick or a laser. Since it is not exactly fulfilling the requirements of the certified IRI measurement, this method is not applicable in field work. The purpose of this research will be to compare these methods and their results to find out whether the technique used by Infrafocus is still applicable to calculate the IRI.

This report identifies and evaluates technology solutions that meet asset management needs relating to road pavement performance. The increasing pace of change of technology brings considerable promise of more data, of a higher quality, captured for a lower cost. The report summarises current and emerging data collection technologies, and proposes and tests a technology evaluation framework. The evaluation of emerging technologies found existing equipment, such as mobile phones, was often repurposed to develop new methods of data collection. While this occasionally results in lower accuracy, this is consistently offset by high affordability and other strengths such as higher frequencies of data collection, data redundancy and secondary benefits. Accordingly, even emerging data collection technologies with relatively low accuracies have a role to play in addressing the data needs of road controlling authorities, and may be used to augment, rather than replace, existing data collection programs

An interesting paper. Key finding? A smarphone roughness meter is the same as visual roughness observations, if calibrated for speed. This matches my experiences ...

The measurement of road roughness is important for the management of economic road maintenance. Not only is it an indicator of road condition and ride quality, but it also is used to determine road use costs, including travel time, fuel consumption, and vehicle maintenance. Because of the importance of roughness for road asset management decision-making, road agencies spend considerable resources trying to measure road roughness in a repeatable and reproducible manner. However, many road agencies with large road networks are unable to record the condition of the entire network on a sufficiently frequent basis to determine adequately road condition to make informed preventative maintenance decisions. To address this, research has been carried out to develop low cost smartphone based technologies fitted inside vehicles to measure road condition. The trial of these systems has met with varying degrees of success. This paper presents an in-depth parametric study carried out using state-of-the-art vehicle dynamics software, informed by a review of the literature, to appreciate how and to what degree various influencing variables might affect roughness measurements using a smartphone fitted to a moving vehicle. These variables included the type and position of the smartphone; the type, speed, mass, dynamic response, suspension system, and tire pressure of the vehicle in which the smartphone is fitted; and the longitudinal road profile. The results of the parametric analysis were used to build multivariate linear regression and machine learning algorithms which predict road roughness from a measure of a vehicle’s vertical acceleration taking into account the predominant influencing variables. The multivariate linear regression equations can be used to predict road roughness with a similar degree of accuracy that is expected from a visual inspection. On the other hand, the machine learning algorithms, when suitably trained, were able to estimate reliably the road roughness on an integer-based rating scale at a level of detail which is suitable for strategic road asset management, provided that the vehicle type and speed and the type of smartphone are taken into account.

Smartphones are equipped with sensors such as accelerometers, gyroscope and GPS in one cost-effective device with an acceptable level of accuracy. There have been some research studies carried out in terms of using smartphones to measure the pavement roughness. However, a little attention has been paid to investigate the validity of the measured pavement roughness by smartphones via other subjective methods such as the user opinion. This paper aims at calculating the pavement roughness data with a smartphone using its embedded sensors and investigating its correlation with a user opinion about the ride quality. In addition, the applicability of using smartphones to assess the pavement surface distresses is examined. Furthermore, to validate the smartphone sensor outputs objectively, the Road Surface Profiler is applied. Finally, a good roughness model is developed which demonstrates an acceptable level of correlation between the pavement roughness measured by smartphones and the ride quality rated by users.

This study identified longitudinal road roughness limit values based on measured vibration induced in a road-vehicle-driver interaction system. Therelations between measuredvehiclevibrationresponseand theinternationalroughnessindex(IRI) were summarized. Frequency-weighted acceleration on the seat and dynamic load coefficient (DLC) were used to quantify ride comfort, ride safety, and the dynamic load of the vehicle and road. Linear relationship coefficients were identified or taken from references. The expected large range of vibration responseroot mean square (RMS) values wasobserved for the same levelof IRI. IRI limit values were derivedfor chosen threshold values of vehicle vibration response as a function of vehicle velocity. Velocity-related IRI limit curves were proposed based on the fitting of IRI limit values lower envelope. IRI limit curves were compared with threshold proposals of other authors and width thresholds used in road maintenance. Some of the estimated IRI limit curves for DLC response were below thresholds used for road maintenance.

The International Roughness Index (IRI) is an indicator used worldwide for the characterisation of longitudinal road roughness. This study summarised IRI limit values for new, reconstructed, or rehabilitated roads; for in-service (existing) roads; and road classification schemes used around the world. An overview of practices in 35 US states and 29 non-US states was provided. Limit values are a function of road surface type, road functional category, road speed limit, road construction type, or average annual daily traffic (AADT). IRI specifications are defined for a broad range of evaluation lengths from several metres to the entire length of a section. Large differences in IRI limit values were observed for the same segment length among various countries. The IRI-based methodology used in US states was compared with that used in non-US countries. Non-US countries used more often specifications as a function of road functional category and AADT, and are based on percentile of IRI observations. US states used more often pay adjustment and specifications as a function of road construction type and road speed limit.

The World Bank established the International Road Roughness Index (IRI) as a standard to measure road roughness. Although road inspection vehicles equipped with multiple sensors costing several millions of pesos can effortlessly measure IRI, developing countries find this steep price to be a challenge for evaluating and managing their road systems. This paper therefore aims to develop an alternative method to measure road roughness using ubiquitous smartphones and Geographic Information Systems (GIS). The proposed methodology is based on data collected by smartphones which are processed using GIS and compared with existing IRI measurements to estimate road roughness measurements of different road types. The paper likewise explores the proposed methodology’s application and integration to road network planning in the Philippines.

The International Roughness Index (IRI) is an indicator used worldwide for the characterisation of longitudinal road roughness. This study summarised IRI limit values for new, reconstructed, or rehabilitated roads; for in-service (existing) roads; and road classification schemes used around the world. An overview of practices in 35 US states and 29 non-US states was provided. Limit values are a function of road surface type, road functional category, road speed limit, road construction type, or average annual daily traffic (AADT). IRI specifications are defined for a broad range of evaluation lengths from several metres to the entire length of a section. Large differences in IRI limit values were observed for the same segment length among various countries. The IRI-based methodology used in US states was compared with that used in non-US countries. Non-US countries used more often specifications as a function of road functional category and AADT, and are based on percentile of IRI observations. US states used more often pay adjustment and specifications as a function of road construction type and road speed limit.

Paper to 2014 SATC Conference on evaluation of the Roadroid roughness meter.

The main objective of this study was to assess the profiler gain validation technique as an alternative method for validating laser profilers that measure road roughness. The study sought to identify a suitable reference profiler against which other profilers are assessed. A series of field tests were undertaken to address the main objective of this study, where the Walking Profiler (WP) was used as the reference device as a comparison to Laser Profilers A and B in measuring IRI roughness over five test sites with varying roughness and texture. These field tests did not conclusively confirm the profiler gain validation technique. The field test suggests that the profiler
gain validation technique should be conducted under a standard test speed for the profiler which can be maintained and allows repeatable tracking of the profiler to minimise variation in the auto-spectral density plots.

Paper on low cost roughness device which uses ultrasonics to calcuate the average vertical deviation of a vehicle and from that, the roughness. An unusual approach.

This research investigated the effect of road roughness, macrotexture and testing speed on GripTester measurements. 

Field tests were conducted by the GripTester at various test speeds on sites with varying road roughness in South Auckland. The variables – road roughness, texture and test speeds – were measured and plotted against each other along with grip number (GN) as obtained from the GripTester. Tests with the DF Tester were also carried out at one site and directly correlated with GripTester results at various towing speeds. It was found the GN might not be dependent on test speeds while testing at speeds lower than 75km/h; however, an inverse relationship occurred at higher speeds, on a limited number of test sites. Road roughness was found to have no effect on GripTester measurements and texture appeared to be a minor factor. 

In conclusion, the explanatory variables on the GN are test speed and perhaps texture. However, unaccounted factors that are specific to test sites proved to have some degree of effect. Future research recommendations include searching for better controlled test sites and larger samples to clarify the effect of texture on the GN and to expose unidentified factors that can influence GripTester output.

In this paper, the authors present a method to automate data collection and mapping of pavement roughness on various functional classification roadways and to quantify fuel consumption for enhanced benefit-to-cost analysis, asset management and infrastructure investment strategies. A new low-cost and efficient technology is presented to measure and contrast vehicles' vertical accelerations over smooth and rough pavement sections. Fuel consumption is then correlated with the vehicle's vertical acceleration over both pavement conditions. The results of the study validate the relationship between acceleration/road roughness and fuel consumption. Finally, using the described technology, a pothole detection algorithm is devised to automatically map road roughness, a step towards a better asset management system.

This paper presents the results of an extensive study to identify the source of the discrepancy, and formulating a procedure for adjusting the historical measurements to ensure continuity. The paper discusses the original calibration procedures that were based on precise rod and level measurements, the inherent variability and the current procedures that use a Dipstick. Furthermore, historically the response type roughness measuring devices were calibrated whereas profilometers measure the actual road profile from which the riding quality statistics are developed. These issues are also discussed.

It was found that the riding quality on the calibration sections were consistent over time, which permitted conversion between measuring systems. With the changed technology a historical IRI of 2 would be an IRI of 1.59 with the dipstick, and a historic measurement of 4.5 would now give a value of 4.21 (all IRI values are in m/km). These conclusions hold important implications for long-term monitoring as well as for international road user cost relations developed prior to 2000 when profilometers became the norm for roughness measurements.

This is an Excel workbook for calibrating response-type roughness meters. It is based on the approach from the World Bank guidelines, as well as experience in developing and calibrating ROMDAS roughness meters. The work book takes the raw roughness data from each run on a test site, and tests the standard error to ensure that the results of individual runs are consistent enough for the data to be used for calibration. If not, it indicates 'Fail' so you should do additional runs. For further information on calibrating roughness meters check out the ROMDAS user guide annex, available for download from www.romdas.com.

Paper analyzing causes of bias in RTRRMS instruments and ways of correcting for it

Austroads test method PT/T450 describing how to calculate the IRI from the ARRB Walking Profiler.

Paper by Morrow that assesses and evaluates the accuracy of these different instruments, which range from low to high cost methods, for establishing the reference roughness on selected calibration sites.

Paper comparing different roughness calibration instruments - from MERLIN to the accurate Z-250.

Validation study of automated pavement crack detection system

In this report, The Road Information Program (TRIP) examines the condition of major roads in the nation's most populous metropolitan areas, recent trends in urban travel, and the latest developments in repairing and building roads to last longer.

Appendices to the LTPP Manual for Profile Measurements

This manual describes operational procedures to be followed when measuring pavement profiles for the Long Term Pavement Performance (LTPP) Program using the International Cybernetics Corporation (ICC) road profiler, Face Company Dipstick®, and the rod and level. Field testing procedures, data collection procedures, calibration of equipment, record keeping and maintenance of equipment for each of the profiling methods are described in this manual.

This document is intended to inform the non-pavement specialist of the various devices used for measuring pavement smoothness. This synopsis provides information on the equipment, characteristics, capabilities, and costs. It is not meant to elaborate on the technical aspects of measuring pavement smoothness.

Study on road surface condition and comparison of roughness measurement devices.

Revision to the procedure for measuring ride quality, of road pavements, determined from the direct measurement of the longitudinal profile of the road surface using a vehicle mounted laser based non-contact device.

The development of a mini-Merlin for calibrating roughness meters.

Study that focussed on the subjective experience of roughness on roads with low IRI-values. Using the available roughness data it was also studied how much a random error, added to the IRI-values, would influence the calculated correlations with the subjective estimates. The investigation was carried out as a magnitude extimation experiment, in which some 20 observers made their estimates while travelling as passengers first in a car, and later in a lorry.

Manual describing calibration and survey procedures for bump integrator roughness surveys.

NHCRP report on appropriate procedures for laser profilometers.

Research brief by Virginia Dept of Transport on the accuracy of road roughness measurements.

Paper on Canadian Strategic Highway Research Program (C-SHRP). The overall goal of the program is to increase pavement life through the development of cost effective pavement rehabilitation procedures, based upon a systematic observation of in-service pavement performance. 24 test sites in Canada were selected to cover a wide range of environmental and traffic conditions. This technical brief summarizes the findings of the report.

World Bank paper on subjectively estimating roughness

Final Report. This study was initiated with the goal of identifying the predominant factors affecting the achievable smoothness of asphalt overlays. In addition, it chronicles the evolution of Virginia’s innovative special provision for smoothness, which was developed specifically for maintenance type resurfacing. It further provides a critical assessment of the non-traditional equipment and methods as used to administer this smoothness special provision. Finally, it includes a rational economic justification for the continued and expanded use of the pilot specification.

This document presents a discussion of the uses of and limitations of LTPP data in pavement performance analyses, general data availability, and data update schedule.

Report from seminar on roughness calibration

This paper outlines the concept and work to date from the road authorities’ perspective on the processes for validating high-speed laser profilers used for the measurement of road roughness.

This study revisits pavement roughness and examines the suitability of the International Roughness Index (IRI) to describe passenger car ride quality, as well as heavy vehicle excitation and dynamic loads. It describes the development of a new pavement roughness index (RIDE).

Report describing practical operational limitations for the NAASRA response-type roughness meter.

Directive regarding procedures to be used on all test sections requiring a manual measurement of longitudinal profile.

TRB Conference. The calibration of response-type roughness meters

The use of the MERLIN for calibrating response-type roughness meters.

PLEASE NOTE: It is not recommended that MERLIN be used for roughness calibration. As a Class III roughness device, it gives an approximate estimate of roughness. It was developed at a time when there were few alternatives for calibration. It is only suitable when approximate estimates of roughness are required since it does not pick up all wavelengths and can lead to systematic mis-estimation of roughness. For proper calibration a Class I/II method must be used.

Testing of the ARAN vehicle's roughness measurements

Testing of the ARAN vehicle's roughness measurements

This report compares pavement profile data collected by four Profilometers used by SHRP's Long Term Pavement Performance Program (LTPP).

Summary of issues with measuring roughness

Information on annual surveys and roughness calibration.

Report on independent assessment made of the inaccuracies of the different road roughness measurement systems. Overview of roughness measurement techniques, accepted practices for calibrating roughness meters and recommendations on operating practices.

Comparison of calibrations between three roughness measurement vehicles in New Zealand

Paper on differences in roughness measurements between two different roughness vehicles

Paper on the accuracy of road roughness measurement that can be achieved by calibrated response-type measuring systems. Evaluated using Mays meter data from a major road costs study in Brazil.

Comparision of the TRL Beam and Face Dipstick for roughness calibration.

Estimation of road roughness following shape correction treatment. Report on investigation and analysis of actual jobs to ascertain if significant changes have occurred in shape correction standards etc.

Discussion paper on possible interpretations of road profile data other than car ride and addresses the form of IRI to be adopted for reporting purposes.

Paper by Michael W. Sayers relating to road roughness measurements.

Report on evaluation of the applicability of the Siometer as a device for profile measurements and comparison with those from a profilmeter.

Report on road profile deterioration from 18 different test sites with bitumous, cement bound and granular road bases in the UK.

RRU Techincal Recommendation 12 explaining how roughness is measured and outlines the causes and effects of increasing road roughness, as well as explaining the implications of roughness on road user costs. It promotes the wide use of roughness measurement and provides examples of roughness surveys.

Annex to RRU Technical Recommendation 12

Report on the IRRE and the selection of a standard index. Included in this paper is background on the fundamental of road roughness characterisation that guided the selection of the standard road roughness index.

Paper by William D.O. Paterson. Comparison of roughness data based on analysis of data from the International Road Roughness Experiment and other sources.

Paper by Bertrand, Harrison and Hudson relating to response-type roughness measuring devices, particularly the Face dipstick (profiling device).

Paper from M.B. Gorski on the consequences of the International Road Roughness Experiment for Belgium.

Report describing the development of the International Roughness Index (IRI) and how to calibrate roughness meters.

Paper on the examination and comparison of road roughness statistics with particular emphasis on their potential effects on rider comfort and their use as a standard calibration statistics for response-type road roughness meters.

Paper by J.C. Wambold on the effects of road roughness on vehicle dynamics.

Abstract from report by Michael Sayers (Transportation Research Institute, Univ. of Michigan) regarding Quarter Car Simulation for determining road roughness

For the sake of history ... a scan of the plans to build the MERLIN for roughness calibration. 

Report on the four types of measuring techniques evaluated, these being: (1) Response-Type Road Roughness Measuring Systems; (2) Direct Profile Measurements; (3) Indirect Profile Measurements; and (4) Subjective Rating Panel.

Report by Ministry of Works and Development on Road Roughness Survey of Featherstone St and Tinakori Rd, Wellington City

Report on field trials of the PCA road meter and the Mays road meter, and the apparatus developed that combines features of both meters. Discussion on current use of roughness data in Australia and possible future applications.