Functional Analysis of Double Bituminous Surface Treatments

There is a need to understand how pavement smoothness varies over space and time and with other environmental factors. Information from a large set of international roughness index (IRI) field data together with traffic and pavement design and age data is used to consider IRI variation across the lanes of multilane highways and freeways in the state of Connecticut. The objective is to determine to what extent IRI varies over all lanes through time in order to consider the amount of field data that will have to be collected for departments of transportation to adequately measure or predict pavement rideability for newer performance-based contracting agreements, particularly those involving contractor warranties. Results indicate that on the basis of IRI, small average roughness differences exist between adjacent lanes. IRI values are highest in the outer right lanes. Lateral differences are relatively consistent and random but small in magnitude. No strong effects could be found between pavement age, composition, or traffic loading and IRI. However, some preliminary evidence suggests that the influence of these factors may also vary by lane. These results have implications for future research as well as the logistics of pavement monitoring by agencies for warranty-based contract payments. First, the difference in IRI between lanes is small and consistent (0.1 to 0.2) when averaged over longer sections, and therefore it is not necessary to repeat measurements for all lanes along longer projects or whole routes. Second, the variation in these field IRI measurements is unpredictable, especially over smaller spatial areas, suggesting that IRI data should be collected in all lanes when shorter projects are being considered.

The International Roughness Index (IRI) is one of the most commonly used indicators to measure pavement surface smoothness. This paper uses the data obtained from the Specific Pavement Studies-3 (SPS-3) of the Long Term Pavement Performance (LTPP) program to study the influencing factors of the International Roughness Index of asphalt pavement. Pavement age, precipitation, freezing index, temperature, traffic volume, traffic load, and rutting depth are investigated to evaluate the effectiveness of four preventive maintenance treatments on asphalt pavement surface roughness. The pavement roughness model is established based on the XGBoost algorithm, with a training R2 of 0.96 and a testing R2 of 0.82. The results show that among the four preservation treatments, the IRI of thin overlay is the lowest. Annual Average Daily Traffic (AADT) is identified as the most significant factor for IRI evaluation, accounting for the most contribution to pavement surface roughness, followed by precipitation, rutting depth, temperature, etc.

This paper reports the findings of research conducted for the Pennsylvania Department of Transportation (PennDOT) to develop customer-based standards for ride quality on four functional classes of highway: Interstate highways, other national highway system (NHS) roads, secondary roads with average annual daily traffic (AADT) greater than 2,000, and secondary roads with AADT less than 2,000. The field work, in which subjects evaluated the ride quality of predetermined test sections of pavements, was conducted in six Pennsylvania counties to incorporate a variety of settings across the state. These subjective ratings were regressed on international roughness index (IRI) values for each of the four highway classes and revealed a fan-shaped pattern in which motorist satisfaction with ride quality dropped off with increased roughness most sharply on Interstate highways, less so for other NHS roads, and still less for secondary roads. PennDOT's current standards for what constitutes good ride quality for each of the four road types equates closely with the 70% level of motorist satisfaction, whereas the standards for excellent ride quality coincide with the 90% motorist satisfaction level for all but the lower-volume secondary roads. The results also suggest that motorist satisfaction with ride quality is extremely sensitive to IRI in rural settings, moderately sensitive to IRI in urban settings, and less so in major metropolitan suburban areas. This pattern is the reverse for NHS roads, and for secondary roads motorist satisfaction is very sensitive to IRI in rural areas and less so in urban and suburban areas. From these results, PennDOT could consider adopting more ambitious ride quality standards, targeting even higher levels of customer satisfaction. However, adopting such standards would require a careful analysis of the cost implications, which is beyond the scope of the research reported here.

The Indiana Department of Transportation (INDOT) is increasingly committed to the Pavement Management System. For this reason, updated simple pavement performance prediction models with the least number of explanatory (independent) variables are required to predict the performance of various pavement types for future planning of rehabilitation or replacement. In Indiana, the two main pavement types are jointed concrete pavement (JCP) and bituminous pavement (BIT). 1999 and 2000 year data were used to develop regression models for different pavement types for the Interstate and Non- Interstate Roads systems. The International Roughness Index (IRI), in inches per mile, was mainly used for dependent variables while the age (AGE) of pavement and the current average annual daily traffic (AADT) were used as independent variables in best model searching. The data from the road test sections, which were randomly selected for this study, did not yield statistically strong pavement performance prediction models more probably due to non-uniform construction and foundation of the test sections. However, a few f the following regression models with R2 close or higher than 0.50 were obtained and listed in the text for use by the INDOT. IRI=43+1.8*AGE+0.0004*AADT for Flexible pavements on Interstate Roads, R2 =0.70. IRI=65+1.9*AGE+0.0003*AADT for Jointed Concrete pavements (JCP) on Interstate Roads, R2 =0.50. IRI=37+10.4*AGE+0.0002*AADT for Thin Overlay pavements on Interstate Roads, R2 =0.34. IRI=65+8.1*AGE+0.0009*AADT for Overlay pavements on Non-Interstate Roads, R2 =0.90. IRI=93+1.1*AGE+0.0012*AADT for Jointed Concrete pavements (JCP) on Non-Interstate Roads, R2 =0.27. IRI=64+4.0*AGE+0.0008*AADT for asphalt pavements on Non-Interstate Roads, R2 =0.30. The rutting is recommended to be used as safety factors along with the pavement prediction models

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.

Modeling customer usage in vehicle applications is critical in performing durability simulations and analysis in early design stages. Currently, customer usage is typically based on road roughness (some measure of accumulated suspension travel), but vehicle damage does not vary linearly with the road roughness. Presently, a method for calculating a pseudo damage measure is developed based on the roughness of the road profile, specifically the International Roughness Index (IRI). The IRI and pseudo damage are combined to create a new measure referred to as the road roughness-insensitive pseudo damage. The road roughness-insensitive pseudo damage measure is tested using a weighted distribution of IRI values corresponding to the principal arterial (highways and freeways) road type from the Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) dataset. The weighted IRI distribution is determined using the number of unique IRI occurrences in the functional road type dataset and the Average Annual Daily Traffic (AADT) provided in the FHWA HPMS data.

Modeling the international roughness index performance on semi-rigid pavements in single carriageway roads

Pavement management system (PMS) has been considered as an essential tool for proper allocation of maintenance funds by monitoring pavement health. Research related to PMS can be found across the world, especially for flexible pavements, but very few researches have been conducted in Bangladesh, where pavements are mostly flexible. Roads in Bangladesh are being constructed for a design life of 20 years provided that a resurfacing treatment is given after 10 years since initial construction, but due to lack of proper maintenance of pavements, the life span plunges to around 5 years. Deflection is recognized by pavement researchers as a vital parameter to evaluate the structural strength of pavements. In comparison with measuring deflection, International Roughness Index (IRI) is easier and less costly to measure on the field. This research aims at developing an empirical model to predict the deflection of flexible pavements using IRI values based on practical data of flexible pavements of varying ages. Following a reconnaissance survey, road sections corresponding to different road classifications constructed and maintained by the Roads and Highways Department (RHD) have been selected for conducting the analyses. Pavement deflection, IRI data, road width, average annual daily traffic (AADT), and time since the last overlay on selected flexible pavement sections have been collected from the database of Road Maintenance Management System (RMMS) of RHD, Bangladesh. The developed empirical linear regression model explains the relationship between deflection and IRI values of flexible pavements with varying pavement age capable of explaining 61.8% variance of the outcome variable. Both IRI and age have been found to be positively correlated with deflection. The outcome of this research work will help in improving the pavement management system in Bangladesh by using IRI values to predict the deflection of pavements and take adequate rehabilitation measures at regular intervals.

The international roughness index (IRI) is a measurement of pavement roughness that is widely accepted for evaluating pavement serviceability, especially its riding quality. Generally, as the age of pavement increases, its condition deteriorates and its IRI value increases. However, the IRI data collected from the Indiana highway system indicate that the IRI values vary considerably for similar pavements and traffic conditions at any given pavement age. This makes it difficult to establish the relationship between IRI and pavement age. In this study, the gray system theory was used to estimate the maximum, mean, and minimum IRI values at different pavement ages. It is believed that the three IRI values are essential for evaluating pavement serviceability. This paper presents the process of the gray system modeling for IRI estimation and discusses the effects of traffic volume on pavement roughness and the estimation accuracy of the gray system models.

Pavement performance evaluation models for South Carolina

In order to best prioritize road maintenance, the level of deterioration must be known for all roads in a city’s network. Pavement Condition Index (PCI) and International Roughness Index (IRI) are two standard methods for obtaining this information. However, IRI is substantially easier to measure. Significant time and money could be saved if a method were developed to estimate PCI from IRI. This research introduces a new method to estimate IRI and correlate IRI with PCI. A vehicle-mounted dynamic tire pressure sensor (DTPS) system is used. The DTPS measures the signals generated from the tire/road interaction while driving. The tire/road interaction excites surface waves that travel through the road. DTPS, which is mounted on the tire’s valve stem, measures tire/road interaction by analyzing the pressure change inside the tire due to the road vibration, road geometry and tire wall vibration. The road conditions are sensible to sensors in a similar way to human beings in a car. When driving on a smooth road, tire pressure stays almost constant and there are minimal changes in the DTPS data. When driving on a rough road, DTPS data changes drastically. IRI is estimated from the reconstructed road profile using DTPS data. In order to correlate IRI with PCI, field tests were conducted on roads with known PCI values in the city of Brockton, MA. Results show a high correlation between the estimated IRI values and the known PCI values, which suggests that DTPS-based IRI can provide accurate predictions of PCI.

International Roughness Index (IRI) is a key parameter in pavement performance evaluation. This study investigates developing a reliable prediction model that can be used to estimate IRI of rigid pavements using innovative machine learning techniques. Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis are integrated to improve the OP-ELM results and design a novel hybrid Wavelet-OPELM (WOPELM) model for the IRI prediction. The proposed model is compared statistically to the OP-ELM and conventional feed-forward Artificial Neural Network (ANN) as well as regression model with respect to their efficiency to predict IRI of jointed plain concrete pavement (JPCP) sections in USA. The relevant data was collected from the Long-Term Pavement Performance (LTPP) database. Eight input variables, initial IRI, pavement age, transverse cracks, percent joints spalled, flexible and rigid patching areas, total joint faulting, freeze index, and percent subgrade passing No. 200 U.S. sieve, are assessed and used to predict the IRI. The results show that the initial IRI, total joint faulting, and freezing index are the most significant parameters for IRI prediction. The WOPELM is found to be a robust and more accurate modelling technique compared to OP-ELM, ANN, and regression for IRI prediction with only a 7% prediction error.

Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for the pavements. This study aims to develop IRI models that can successfully estimate the IRI values for Jointed Plain Concrete Pavement (JPCP) considering the M&R history of the pavements using Artificial Neural Networks (ANNs) approach. The study was carried out with the database collected from Long Term Pavement Performance (LTPP) program. The variables used for the ANN model development are initial IRI, pavement age, concrete pavement thickness, equivalent single axle load (ESAL), climatic region (wet-freeze, wet non-freeze, dry-freeze, dry non-freeze), construction number (CN), and several climatological data. After utilizing various ANN model structures, the best performing ANN model resulted in promising statistical measures (i.e. R2 = 0.87). The IRI prediction model can successfully estimate the increase of IRI values with the increase of ESAL value over time. The IRI prediction model can also estimate the decrease of IRI value after maintenance and rehabilitation. The predicted IRI values with good accuracy will help the local and state agencies to prepare for M&R programs for JPCP pavements and allocate a projected budget accordingly.

The objective of this study is to apply the Backpropagation Neural (BPN) network with Generalised Delta Rule learning algorithm for reducing the measurement errors of pavement performance modelling. The Multi-Layer Perceptron network and sigmoid activation function are applied to build the BPN network of Pavement Condition Index (PCI). Collector and arterial roads of both flexible and rigid pavements in Montreal City are taken as a case study. The input variables of the PCI are Average Annual Daily Traffic (AADT), Equivalent Single Axle Loads (ESALs), Structural Number (SN), pavement age, slab thickness and difference in the PCI between the current and preceding year (ΔPCI). The BPN networks estimate that the PCI has inverse relationships with AADT, ESALs and pavement age. The PCI has positive relationships with these variables for roads that have recent treatment operations. The PCI has positive relationships with SN and slab thickness that imply the increase in pavement condition with increasing structural strength and slab thickness. The ΔPCI significantly influences the estimation of PCI values. The AADT and ESALs have considerable importance; however, pavement age and structural characteristics of the pavement have an insignificant influence in determining the PCI values, except in the case of flexible arterial roads.

The United States is facing grave infrastructure challenges related to repairing aging roads with limited resource allocation. Every year, large sums of money are invested into repairs, but these repairs are still inadequate. In order to best prioritize road maintenance investment, city officials should know the grade of all roads prior to any action of repair. Two major parameters are widely used to assess road surface conditions: International Roughness Index (IRI) and Pavement Condition Index (PCI). IRI is a standardized and widely used parameter to quantify road roughness and riders' comfort level for major highways. A low IRI value indicates a smooth road and a high value indicates that the road has distresses, such as potholes or deep depressions. One limitation of the current IRI measurement is that the laser profilometers used with accelerometers are incapable of operating on a wet road surface. Another is IRI's inaccuracy due to the speed effect and complicated road conditions (potholes, manholes, etc.). PCI has been used widely on urban roads. A high PCI value indicates a good road and a low value indicates a poor road. The limitations of current PCI measurement include the high cost, difficulty of manually gathering measurements without traffic interruptions , and low efficiency on data processing due to large amounts of pictures. To overcome these limitations, this dissertation develops two new sensor systems for IRI measurement: a directional microphone and dynamic tire pressure sensor (DTPS) with an axle accelerometer. These sensors are all mounted on a moving vehicle. This research develops that IRI measurement use a directional microphone and a probabilistic method to analyze the probability density function (PDF) of acoustic data collected while driving. Acoustic response of tire/road interactions was measured. Weibull distribution of the acoustic data was applied to the IRI estimation on Superpave, Stone Matrix Asphalt (SMA), and Open Grade Friction Coarse (OGFC) roads with IRI values less than 2 m/km. IRI measurement using DTPS with an axle accelerometer analyzes the tire pressure change inside the tire with an axle accelerometer. Speed effect has been minimized in the derivation. Therefore, it is suitable for both urban roads and state/interstate highways. Field road tests have been conducted to validate the accuracy for the cities of Brockton and Boston, MA, and for part of interstate highway I-95 MA, state highway US-1 and US-128. A certification test was also completed at New Bedford Regional Airport administered by the Massachusetts Department of Transportation. Results showed that the higher the IRI values, the lower the PCI values. It is possible to use IRI to assess road conditions for both urban roads and highways and indirectly infer PCI values of urban roads. The advantages of this method are that it works under all weather conditions since the sensor is inside the tire, and it does not interrupt the traffic. The speed effect, which was encountered in the method which used laser profilometers with accelerometers, is considerably minimized in the DTPS approach since this approach does not require the systematic integration of acceleration data. Therefore, it works for both highways and urban roads. Meanwhile, a miniature fixture was designed to further the simplification of the mounting process for easy installation. An energy harvesting system was also designed and tested at a speed of up to 52 km/h in the lab in order to power the DTPS sensing system. Not only can this energy harvester be used to power the DTPS system, but it can also potentially be used as an independent energy harvester to recharge car batteries and to power vehicle based sensors, including their wireless transmitters and vehicle computer chips. This new development will enable continuous, network-wide assessments of roadway conditions to effectively and efficiently make the right repair, at the right time, in the right place.