Effect of Temperature Differentials on Density and Smoothness

Good road conditions are critical in ensuring the comfort, safety, and efficiency of road users' journeys. The Pandaan–Malang Toll Road, as a strategic infrastructure with a traffic volume of 43 million vehicles per year, faces challenges related to the degradation of road surface quality due to traffic dominated by light vehicles. However, previous studies have not extensively integrated the relationship between vehicle volume and the International Roughness Index (IRI) values on a segmental basis as a basis for maintenance evaluation. This study aims to analyze the IRI conditions of five segments of the Pandaan–Malang toll road during the period 2022–2024 and recommend maintenance strategies based on IRI classification in accordance with Ministry of Public Works and Public Housing Regulation No. 47 of 2015. The method used is quantitative with secondary data analysis of IRI values from PT Jasa Marga Pandaan–Malang based on RoadRoid Survey Application and processed using Microsoft Excel. The results show that the average IRI value in 2024 is 3.66, classified as Good, but Segments I and II classified as Fair with values >4 and require periodic maintenance. Other segments can still be maintained through routine maintenance. This study concludes that the IRI values show a stable trend but have the potential to increase without planned maintenance. Future research is recommended to examine the integration between IRI, PCI, and traffic load to develop a more comprehensive and applicable road damage prediction model.

The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation.

The importance of the road network in Indonesia as a vital infrastructure that connects various regions has made road maintenance a top priority in development planning. However, various challenges such as ineffective handling methods, limited experts, and minimal equipment have caused road management to not be optimal. Therefore, innovations are needed in road condition measurement, one of which is through the development of an ultrasonic sensor-based surface roughness measuring instrument as a prototype of International Roughness Index (IRI) measurement to support more accurate road maintenance evaluation and planning. The purpose of this research is to measure road roughness through IRI and pavement modulus values to improve road condition assessment.This study employs the International Roughness Index (IRI) to assess the functional condition of roads and the Pavement Modulus to evaluate the structural strength of the pavement. The IRI is measured through road surface roughness surveys using a roughness meter, with the results used to classify the severity of road damage. The IRI calculation is based on a quarter-car simulation model that utilizes vehicle dynamic parameters in response to road surface profiles, following the mathematical approach developed by Sayers, Gillespie, and Paterson (1986). The research results show that the prototype Ultrasonic Surface Roughness Meter was able to measure IRI values ranging from 4 to 8 at three different locations. These measurements fall within the "Good–Fair" classification, indicating relatively mild surface roughness. Based on these findings, the Directorate General of Highways recommends light rehabilitation and periodic maintenance, and the prototype device has the potential to serve as an effective, low-cost alternative for road condition monitoring, especially in areas with limited access to conventional IRI measurement tools.

<div class="section abstract"><div class="htmlview paragraph">The International Roughness Index (IRI) is a key indicator for evaluating the performance of road surfaces. However, traditional measurement methods only focus on the evaluation data of a single longitudinal section and do not consider the lateral difference between the actual contact area between the tire and the road surface, which may lead to inaccurate evaluation results. In recent years, with the advancement of 3D laser scanning and digital photogrammetry technology, full-section data acquisition has brought new possibilities for roughness evaluation. However, how to find a balance between data fineness and computing efficiency has become a core problem that needs to be solved. Based on the principle of interaction between vehicles and road surfaces, this paper proposes to include only the pavement height data within the tire width range into IRI analysis, and establishes an evaluation framework based on standard tire-ground contact width. This method not only retains the key information of horizontal unevenness, but also increases the computing speed by reducing the amount of data. In the experiment, a vehicle-mounted three-dimensional measurement system was used to measure the height of the actual roads, and the variation patterns of IRI values under different contact widths were analyzed. The study found that the tire-ground contact width will affect the IRI calculation results, but the degree of impact varies according to the wheel position. The IRI value of the left wheel area is not sensitive to the change in contact width, while the IRI value will fluctuate significantly due to the lateral slope of the road surface or structural asymmetry in the right wheel area. We recommend that 200 mm be used as the recommended value for the tire-ground contact width, which can not only ensure the evaluation accuracy, but also improve detection efficiency.</div></div>

ABSTRACTThe performance of asphalt pavement is highly affected by climate factors, such as temperature and precipitation. Different temperature and moisture conditions change the paving material properties, which consequently influence pavement performance. Considerable research has been conducted to study the effect of climate factors on pavement performance; however, there are no focused studies that have investigated the performance of asphalt pavements in cold regions. In this research, the effect of different design factors on asphalt pavement performance in cold regions is investigated utilising data extracted from the Long-Term Pavement Performance database. Only control sections with no historical maintenance or rehabilitation records were considered. The International Roughness Index (IRI) was adopted as the pavement performance measure. The IRI value is expected to increase gradually over time due to pavement deterioration. Initial screening of the data showed that most of the pavement sections experienced remarkable IRI increase followed by an unexpected decrease. An in-depth investigation revealed that frost heave was the main reason of this unexpected IRI trend as it was correlated in most of the cases to sudden monthly freezing index increases, especially for sections with no subsurface drainage and less layers thicknesses. An Artificial Neural Network with a Forward Sequential Feature Selection algorithm and regression analysis were employed to model pavement performance in cold regions and determine the most significant design factors prevailing in cold climate conditions. Moreover, a sensitivity analysis was performed to investigate the interrelation between the considered features and the IRI. As a result, several climate-related factors were found to have a significant impact on the performance of asphalt pavements in cold regions such as average temperature, freezing index, freeze/thaw, wind velocity and relative humidity.

The National Center for Asphalt Technology (NCAT) Test Track has generated valuable information related to the installation of gauges, methodology for data collection and data analysis. However, information regarding the effect of pavement instrumentation on in situ properties is nonexistent. The objective of this study was to evaluate the effect of pavement strain gauges on the in situ density. In 2009, during construction of two test sections it was necessary to remove and replace the bottom lift since quality control targets were not met. Prior to removal, cores were taken in the gauge array and outside the array so that a two-sample T-test could be conducted for each section, respectively. The results showed that there were no significant differences between air void content and consequently no negative effect caused by the presence of pavement instrumentation. Additional density testing was conducted in and around the gauge array in these two test sections after completion of the surface layer. The data indicated no difference in measured density resulting from the presence of embedded instrumentation.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

The commonly used index for measuring pavement roughness is the International Roughness index (IRI). Traditional method for collecting road surface information is expensive and as such researchers over the years have resorted to other cheaper ways of collecting data. This study focuses on developing a deep learning model to quickly and accurately determine the IRI values of road sections at a cheaper cost. The study proposed a model that uses accelerometer data and previous year’s IRI values to predict current year IRI values. The study concludes that addition of accelerometer readings to previous year’s IRIs increased the accuracy of prediction.

Smoothness specifications for newly built Portland Cement Concrete Pavements (PCCP) in Kansas have evolved over the last two decades through applications and a number of revisions. However, some PCCP sections built under current specifications in the smoothness bonus range experience rapid loss of smoothness with time. Sometimes this happens even before the sections are opened to traffic. It is believed that this loss results from curling due to the temperature differential between the pavement top and bottom surfaces. In this study, threedimensional simulation of curling of PCCP has been presented using a finite element (FE) software, ANSYS. A number of FE models were built to simulate several newly built PCCP sections in Kansas. The sections were modeled as three-layer systems with cement-treated base and lime-treated subgrade. Layer materials were modeled as linear elastic and the properties were obtained from the tests conducted during construction. Pavement layers and steel dowel bars were modeled using 3- dimensional solid (brick) elements. The complex interaction of the slab with the dowel bars was modeled as a contact problem. Regression models were developed for the curling deflection and International Roughness Index (IRI), a roughness statistics resulting from the curled profiles, based on different simulation parameters. The results obtained from the simulations show that the curling deflection and IRI calculated from the deflected slab profiles are affected by the slab thickness, compressive strengths of the concrete slab and base layers, and the temperature differential between the pavement top and bottom surfaces. Both curling deflection and IRI increase with an increase in temperature differential and compressive strength of the stabilized base layer. Higher slab thickness and concrete compressive strength would result in lower curling deflection and IRI values.

Mill and thin asphalt overlays are a popular preservation treatment for asphalt and composite pavements owing to their ability to address a variety of distresses. Their effectiveness depends on different factors such as traffic, climate, and condition of the pavement at the time of treatment, with typical treatment lives ranging from 5 to 12 years. In cold regions, performance in relation to roughness is of particular interest, as factors like frost action and thermal cracking tend to accelerate roughness progression. The objective of this study was to evaluate the effect of thin asphalt overlays on pavement roughness in cold climate locations using a set of field performance data from the Pavement Preservation Group Study, a broader research study conducted by the National Center for Asphalt Technology and the Minnesota Department of Transportation’s Road Research Facility. The findings indicated that treatment application resulted in both short- and long-term benefits. Following construction, a performance jump was obtained, which was greater for pavement sections with higher International Roughness Index (IRI) IRIpre values. This can be interpreted as rougher sections benefiting more in the short term than smoother sections, where the IRI is already low. Conversely, pavements with lower roughness at the time of treatment obtained greater life-extending benefits. This is interpreted as smoother pavements obtaining a greater benefit over time, and reinforces the concept of preserving pavements while in good condition to maximize pavement life. The magnitude of the benefits was also dependent on the type of milling machine used.

Assessment to the performance of road pavement is deemed necessary to improve the management quality of road maintenance and rehabilitation. This research to evaluate the road base on functional and structural and recommendations handling done. Assessing the pavement performance is conducted with functional and structural evaluation. Functional evaluation of pavement is based on the value of IRI (International Roughness Index) which among others is derived from reading NAASRA for analysis and recommended road handling. Meanwhile, structural evaluation of pavement is done by analyzing deflection value based on FWD (Falling Weight Deflectometer) data resulting in SN (Structural Number) value. The analysis will result in SNeff (Structural Number Effective) and SNf (Structural Number Future) value obtained from comparing SNeff to SNf value that leads to SCI (Structural Condition Index) value. SCI value implies the possible recommendation for handling pavement. The study done to Simpang Tuan-Batas Kota Jambi road segment was based on functional analysis. The study indicated that the road segment split into 12 segments in which segment 1, 3, 5, 7, 9, and 11 were of regular maintenance, segment 2, 4, 8, 10, 12 belonged to periodic maintenance, and segment 6 was of rehabilitation. The structural analysis resulted in 8 segments consisting of segment 1 and 2 recommended for regular maintenance, segment 3, 4, 5, and 7 for functional overlay, and 6 and 8 were of structural overlay.

Practitioners have often wondered whether, during ride measurement with inertial devices, the motion of the laser through pavement texture introduces nonrepresentative values of international roughness index (IRI), particularly in certain textures. In response to this problem, a special texture study created a nontextured strip by a recession of the middle 4 ft of a texturing broom dragged longitudinally behind the paver. The study measured IRI and other surface properties in adjacent textured and nontextured strips by using a lightweight profiler outfitted with a line laser and a triple laser arranged in tandem. IRI measurements were performed after sufficient concrete strength gain and repeated as soon as the joints were sawn. The same measurements were repeated after the joints were deployed. Results showed a significant difference between the IRI of a textured strip and that of an nontextured strip. Further analysis indicated that, although texture appears to affect IRI, this effect was amplified by the type of laser used, as the triple laser appeared to indicate higher IRIs in comparison with the RoLine laser. Although the RoLine is not a reference profiler for IRI values unaffected by texture, the prevalence of the RoLine and the triple laser in construction acceptance testing is sufficient reason to be concerned about the difference inherent in the obtained results. Chi-square and t-test statistical analysis showed that laser type induced comparable and even higher IRI anomalies than did the experimental drag texture. In addition, the texture-induced IRI anomaly can be minimized by measuring smoothness for acceptance at least 2 weeks after paving.

Accelerated pavement testing (APT) applies intensive traffic loading for the purpose of observing long-term performance of pavements in a short time. The testing is beneficial for researchers as a timely and valuable data resource. As Mechanistic-Empirical (M-E) pavement design approaches emerge as a favorable alternative to traditional empirical approaches, there is a need for conducting calibration of the performance models in the M-E design framework. Data from APT testing may be considered advantageous for calibration due to research-grade accuracy conducted over shorter time intervals, but even so, M-E calibration is still time-consuming because many combinations of the respective calibration coefficients need to be evaluated against observed performance from pavement sections. The current version of the AASHTOWare™ Pavement ME Design software, though serving as a powerful pavement design and analysis tool, is not necessarily conducive to calibration studies as many interactions are needed by the user to populate the software with trial calibration coefficients and extract performance predictions after simulations have been completed. This study proposes using automation to facilitate calibration studies whereby the repetitive manual operations required by the software are recorded and built into a macro that greatly reduces the required human interactions. The software automation is demonstrated through a calibration study conducted at the National Center for Asphalt Technology (NCAT) Test Track that examined fatigue cracking calibration. The efficiency of the automated approach is compared against conducting calibration requiring human interaction with the software.

This study aims to evaluate working road pavement conditions, the road pavement conditions value based on the International Roughness Index (IRI) method which is used as a basis for knowing the type of road maintenance handling type. For taking IRI, a vehicle that has good performance is used, while IRI value recording uses Roadroid software that has been installed on an Android-based mobile. The data results from this study indicate that the Perintis Kemerdekaan Road produces IRI values of 2.607 and 2.408 for each direction. From this value, it is converted in road stability level which shows that the road is in a steady category so that in general this segment for handling requires regular maintenance periodically to achieve maintaining the service level.

This study investigates the possibility for augmenting infrequent standard pavement survey data with spatiotemporally continuous sensor data crowdsourced from connected vehicles. A framework is proposed to leverage physics-integrated machine learning (ML) models to reconcile the two disparate data sources. The proposed approach is demonstrated for the International Roughness Index (IRI). Using the quarter-car simulation, vertical acceleration data records were generated based on random stratified sampling from probability distributions of vehicle suspension properties at different speed levels on elevation profiles collected from various pavement sections. An ML model was pretrained to approximate the quarter-car simulation. Using transfer learning, the parameters of the outer layers of the model were finetuned to adapt model output to the measured values, while the parameters of the inner layers were fixed to preserve the embedded knowledge of the physical behavior of the suspension. The resulting physics-integrated ML model can predict the standard IRI value of a pavement section using speed, suspension properties, and features of the acceleration response from vehicles traversing that road section. The evaluation results on independent training and test datasets showed high accuracy, precision, and generalization capability, especially on smoother road profiles. This is attributed to the limited training data for rougher roads. After averaging the predicted IRI from all vehicles passing a certain road segment, a correction function is used to rectify the model’s under-predicted output and estimate the actual IRI value for that segment. High-frequency data resulting from the proposed approach can enhance pavement design, maintenance management, and asset management practices.