Road roughness and profiles.
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 analternative, 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 profilergain 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.
Austroads test method PT/T450 describing how to calculate the IRI from the ARRB Walking Profiler.
Manual describing calibration and survey procedures for bump integrator roughness surveys.
NHCRP report on appropriate procedures for laser profilometers.
Report describing practical operational limitations for the NAASRA response-type roughness meter.
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
Comparision of the TRL Beam and Face Dipstick for roughness calibration.
Report describing the development of the International Roughness Index (IRI) and how to calibrate roughness meters.
For the sake of history ... a scan of the plans to build the MERLIN for roughness calibration.