Evaluating road surface conditions using dynamic tire pressure sensor

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.

Road infrastructure is a critical component of land transportation systems, supporting both human mobility and the distribution of GOODs. To ensure user safety and comfort, roads must be maintained in optimal condition through appropriate and sustainable maintenance strategies. This study aims to formulate a road maintenance management strategy based on an analysis of the relationship between the International Roughness Index (IRI), as obtained through the Roadlab Pro application, and the extent of road damage as assessed by the Pavement Condition Index (PCI) method and the Bina Marga standard. A quantitative-descriptive method was employed, focusing on a 3-kilometer section of the Pondok–Pulau Sangkar Road in Kerinci Regency. Data were collected through a seven-day field survey, which included IRI measurements and visual damage assessments. The analysis revealed a very strong negative correlation between IRI and PCI values, indicating that higher IRI scores correspond to worse road conditions. Based on these findings, three maintenance budget planning scenarios were proposed: (1) maintenance based on actual damage, (2) routine annual maintenance, and (3) road upgrading in the first year, routine maintenance in the second year, followed by periodic maintenance from the third to the fifth year. The cost estimates encompass periodic, preventive, corrective, and routine maintenance, providing a comprehensive basis for medium-term planning. In conclusion, this study demonstrates that IRI data obtained from Roadlab Pro can serve as a reliable and efficient reference for evaluating road conditions and developing effective maintenance strategies based on the PCI approach.

Pavement conditions will reduce the level of service over time. It is characterized by the occurrence of structural damage of the road pavement layers in which the neglect for a long period can worsen the condition of the pavement layers that can affect the traffic safety and comfort. Road pavement performance is determined based on the requirements of the functional and structural condition. The functional conditional requirements relate to roughness, pavement surface aggravation, while the structural condition requirements relate to the pavement strength or carrying capacity in serving the load and the traffic flow. The aim of this research is to evaluate the functional and structural of the flexible pavement on south arterial road in Yogyakarta and to provide recommendation of maintenance and rehabilitation. The functional evaluation of pavement is based on DGH and PCI method, which combines International Roughness Index (IRI) and Pavement Condition Index (PCI) values, while structural pavement evaluation is performed by analyzing the deflection value of Falling Weight Deflectometer (FWD) measurement analyzed by AASHTO 1993 with the output being the Structural Number (SN) value, where SN effective/SNfuture ratio results Structural Condition Index (SCI) value which determines whether a pavement requires overlay or not. The results of functional analysis based on IRI and PCI values indicate that the road conditions for each segment are good, damaged and heavily damaged. The structural analysis for 2018 shows all segments yield SCI value<1, which means they require an overlay. The segments requiring functional overlay are Km 00 + 000 up to 02 + 400, Km 04 + 400 up to 05 + 600, Km 07 + 600 up to 13 + 200, Km and Km 16 + 000 up to 18 + 400. Meanwhile, the segments that require structural overlay are: Km 02 + 400 up to 04 + 400, Km 05 + 600 up to 07 + 600 and Km 13 + 200 up to 16 + 000.

In order to best prioritize road maintenance, the level of deterioration must be known for all roads in the network. International Roughness Index (IRI) is a key standardized and widely used parameter to quantify road roughness and to aid officials in making road maintenance decisions. A low IRI value indicates smooth road and a high value indicates road has distresses such as severe cracking, potholes, and rutting, etc. IRI measurement using laser profilometers with accelerometers mounted on the front or rear bumper of a vehicle along the wheel path is commonly used at a driving speed of above 40 mph. IRI values measured through laser are questionable below this speed and will not work when road is wet. This paper discusses an alternative method to estimate IRI using a dynamic tire pressure sensor (DTPS) inside the tire as vehicle is moving. This method is working at a speed of above 15 MPH suitable for both interstate highways as well as urban roadways which required a driving speed of below 40 MPH. It works at all weather conditions since the measurement is the tire pressure change inside the tire through road and tire interaction. A feasibility and repeatability test on highway and airport through a certification test which was administered by Massachusetts Department of Transportation is therefore reported. The accuracy is equivalent to laser based IRI values.

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Road quality degradation affecting on safety and comfort of road users. It is necessary to carry out regular assessments of the functional and structural condition of the pavement. Evaluation of functional and structural conditions is carried out to maintain the good quality of road services. This research aims to evaluate the functional and structural conditions using the Pavement Condition Index (PCI) method and the functional condition of the road using the International Roughness Index (IRI) method. The tool used in testing functional conditions is the Hawkeye 2000 car which generates Pavement Condition Index data for each road segment, then calculates the average PCI value and the conditions on the road segment. From the analysis of the functional condition of the road pavement, the Pavement Condition Index value was obtained for Kudus East Ring Road Section at 57.05 (Fair), Pati Ring Road Section at 60.57 (Fair), and Wangon-Menganti Road Section at 78.35 (Satisfactory). From the results of the analysis of the functional condition of the road pavement, the International Roughness Index value was obtained for Kudus East Ring Road Section at 5.16 (Medium), Pati Ring Road Section at 4.94 (Medium), and Wangon-Menganti Road Section at 4.64 (Medium). The results of the correlation of the 2 parameters using SPSS on each successive road section show "weak correlation" with r = 0.2754; "moderate correlation" with r = 0.5534; and "no correlation" with r = 0.0435.

There are two methods of road assessment, ie, visually and using tools. Visual assessment makes use of the PCI (Pavement Condition Index), while assessment with the Roadroid app can be used to obtain the value of IRI (International Roughness Index) with less cost. Functional assessment of roads in the field more use of visual methods. This method is influenced by the subjectivity of surveyors. Therefore, the assessment using the visual method should be correlated with the assessment method using tools, in order to reduce the subjectivity of road assessment. The research location used is Magetan District Road consisting of 5 road segments. The result shows that the r road assessment using the PCI method has a very good condition, and using IRI Roadroid has a Medium condition. There is a negative (r) correlation between PCI and IRI Roadroid, valued at -0.23. The negative correlation shows that both judgments reversed. Comparison of PCI assessment with IRI Roadroid has a low correlation value and with ttest, yields no comparison of correlation. This result is because the PCI and IRI equally assess the pavement, using different methods.

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

The international roughness index (IRI) is one of the most popular indices to measure pavement roughness. State agencies and cities with plenty of resources often collect IRI and pavement distresses every year or every other year, but some others with fewer resources will collect this information every 3 to 5 years. Collecting IRI is much more affordable than collecting pavement distresses. With this in mind, the objective of this paper was to establish a relationship between IRI and pavement condition index (PCI) using pavement deterioration models for both PCI and IRI based on the concept of time–deterioration superposition similar to the time–temperature superposition principle, and then combine both models to establish this relationship. Additionally, this study was used to establish threshold limits for IRI measurements that can be used as a general reference for pavement condition. Data from the Long-Term Pavement Performance InfoPave was used to perform the analysis for three network samples from Arizona, California, and Wisconsin. This analysis only included flexible pavements. The results from Arizona, California, and Wisconsin showed a good relationship between IRI and PCI using the proposed approach with a coefficient of determination ranging from 0.71 to 0.85. Furthermore, the analysis showed that the change in IRI over time can be related to the change in PCI over time. The general thresholds developed in this study apply to the sections evaluated but the approach can be used to set limits for other networks.

The data about the road conditions must be managed properly to ensure the safety and accessibility of information in the context of road maintenance. The present study aims to create a reliable and accessible database which able to store road conditions data. The method was by combining the Pavement Condition Index (PCI) method with Google Maps, XAMPP, Notepad ++ and Botstrap software. The results of the analysis obtained several parameters of road conditions, including road damages, PCI values, density, deduct value and corrected deduct value. The developed website design is a GIS-based database application with the display of two main menus, namely the "ROAD DATA" menu and the "SURVEY ROAD MAP" menu. The first menu contains data on road conditions from the analysis using the PCI method, while the second menu contains a map of the distribution of points of the road segments for every distance of 1 km. Information related to how to use the database is displayed in the application manual which contains how to activate XAMPP, Apache, and MySQL and also the website on localhost, as well as how to display road condition data.

Pavement evaluation is the most significant procedure to minimize the degradation of the pavement both functionally and structurally. Proper evaluation of pavement is hence required to prolong the life year of the pavement, which thus needs to be addressed in the policy level. By this, the development of genuine indices are to be formulated and used for the evaluation. In context of evaluating the pavement indices for measuring the pavement roughness, International Roughness Index (IRI) is used, whereas for calculating the surface distress, indices as such Surface Distress Index (SDI) and Pavement Condition Index (PCI) are used. Past evaluating schemes used by Department of Roads (DOR) were limited to IRI for evaluating the pavement roughness and SDI for measuring the surface distress, which has least variability in categorizing the pavement according to the deformation. Apart from these, PCI which has wide range of categories for evaluating pavement, is not seen in practice in Nepal due to its cumbersome field work and calculations. In this paper the relationship is developed relating PCI with IRI and SDI using regression analysis by using Microsoft excel. In the other words, the pavement roughness index is compared with the surface distress indices. In 2017, 23.6Km of feeder roads in various locations of Kathmandu and Lalitpur districts were taken for this study which comprised of 236 sample data, each segmented to 100m. For this, IRI was sourced as secondary data, obtained from Highway Maintenance and Information System (HMIS) unit, Kathmandu, whereas, PCI and SDI were calculated from the field data obtained from the survey carried out in those sections manually. Then after, among 236 samples, 189 samples were taken for the relationship development which was then validated using 47 remaining samples. Furthermore, in the year, 2019 additional 3 Km of data was taken for validating the obtained relationships. It was done to improve the numerical predictions of data with such variation and thus satisfactory relationships were developed among the indices discussed in this study. The regression relationships between the two indices, IRI-PCI and IRI-SDI were thus significantly obtained. It has been found that the R² value for these relationships developed were statistically significant with 5% level of significance. The R² value for all the relationships showed that these relationships could be used for predicting the indices which would help in evaluating the pavement.

Examining the relationship between two road performance indicators: Pavement condition index and international roughness index

The condition of the road pavement can be judged from the functional performance by measuring the surface condition of the pavement and the value of surface roughness. Pavement surface condition measurements are usually carried out using the Pavement Condition Index (PCI) survey method. While the measurement of surface roughness values is usually carried out with the Roughness survey method so that the International Roughness Index (IRI) is obtained. This study tries to evaluate the value of a pavement condition by reviewing functional performance to identify the value of the pavement condition. Checking road conditions in this study was carried out using two survey methods, namely the International Roughness Index (IRI) method, the Pavement Condition Index (PCI) method. The results of the examination are analyzed so that the correlation between each method can be known. The results of the study explained that based on the results of observations of the functional condition of road pavements with the Pavement Condition Index (PCI) method using the Hawkeye 2000 tool on the Demak Ring Road section, a PCI value of 65.34 was obtained with the “Fair” category, the functional condition of the road pavement with the International Roughness Index (IRI) method of the Hawkeye 2000 tool on the Demak Ring Road section obtained a IRI value of 6.60 with the category “Medium”, and the correlation of the functional condition of road work using the Pavement Condition Index (PCI) method with the International Roughness Index (IRI) on the Demak Ring Road section has no correlation.

Road damages might affect pavement condition which leads to reducing the remaining service life of the pavement. Two methods widely known to measure the pavement condition are among others: the Pavement Condition Index (PCI) and the International Roughness Index (IRI). Both intended to measure the functional condition of the pavement. This study aims to show the relationship between PCI and IRI, hence the road roughness can be predicted from on-foot survey measurements. It will start by collecting the distress and its severity parameter as well as the roughness of 6 road sections using Hawkeye mobile car. The car is a complete modular system to measure roughness (using profilometer), capture images and measure the severity of the road distresses and else. The results show that the pavement condition of the 6 sections fall into the category of poor and above. While 44% of the segments are fair. The roughness conditions of all road sections are good or acceptable. The correlation analysis shows that the PCI cannot necessarily explain the IRI, even though both are used to express the functional condition of road pavements, because what Hawkeye measures for the two indices is different.

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.