International Roughness Index Values Research Articles

Life cycle analysis of Guanxi freeway pavement maintenance interval

Life Cycle Analysis (LCA) is widely used as an environmental impact over the full life cycle of a system. This paper incorporates LCA including impacts that occur throughout the construction, operation, and, maintenance of pavement. The historical freeway database was collected to analyze the pavement life cycle responsible for substantial energy and resource consumption. The analysis was done for Guanxi section as part of Taiwan National Freeway no.3 to compare a long-cycled and short-cycle maintenances. Calibrated HDM-4 and GaBi used to simulate total life cycle conditions. Road network, climate, vehicle fleet, and traffic data were collected and analyzed. Two aspects including Pavement Condition Index (PCI) and International Roughness Index (IRI) were considered for the simulations. The result shows that short-cycle is more acceptable for maintenance in Guanxi section. Environmental analysis by Global Warming Potential (GWP) shows that short-cycle maintenance contributed the smallest GWP with 0.02% up to 0.67% lower than another maintenance type. However, Taiwan focus on increase the level of service of the freeway by setting the minimum IRI value 1.7 m/km. Likewise, this study validates that recent maintenance use in Guanxi section is better than the previous long-cycle, while reduce the maintenance frequency to 1.5 years’ might could increase the effectivity of short-cycle maintenance.

Predicting Pavement Roughness Using Deep Learning Algorithms

Transportation agencies should measure pavement performance to appropriately strategize road preservation, maintenance, and rehabilitation activities. The international roughness index (IRI), which is a means to quantify pavement roughness, is a primary performance indicator. Many attempts have been made to correlate pavement roughness to other pavement performance parameters. Most existing correlations, however, are based on traditional statistical regression, which requires a hypothesis for the data. In this study, a novel approach was developed to predict asphalt concrete (AC) pavement IRI, utilizing datasets extracted from the Long-Term Pavement Performance (LTPP) database. IRI prediction is categorized by two models: (i) IRI progression over the pavement’s service life without maintenance/rehabilitation and (ii) the drop in IRI after maintenance. The first model utilizes the recurrent neural network algorithm, which deals with time-series data. Therefore, historical traffic data, environmental information, and distress (rutting, fatigue cracking, and transverse cracking) measurements were extracted from the LTPP database. A long short-term memory network was used to solve the vanishing gradient problem. Finally, an optimal model was achieved by setting the sequence length to 2 years. The second model utilizes an artificial neural network algorithm to correlate the impacting factors to the IRI value after maintenance. The impacting factors include maintenance activities; initial (new construction), milled, and overlaid AC thicknesses; as well as IRI value before maintenance activities. Combining the two models allows for the prediction of IRI values over AC pavement’s service life.

Correlation of Vehicle Speed to Road Surface Condition Using Roadroid Application

The road condition should provide a decent level of comfort, safety and efficiency for the road users. One of the pavement performance parameters that can be determined objectively is the International Roughness Index (IRI). This research uses a relatively new technology to measure IRI value named the Roadroid, which is an android application developed from a Sweden company. Roadroid uses a built-in vibration sensor on a smart phone which is placed on the vehicle and produces output value in the form of IRI and speed values. Roadroid application obtained output data based IRI value and speed value with high accurately because used real-time data with each vehicle type which get through each road surface condition. Each type of vehicle produces an output of IRI values on different roadroid applications for each type of road surface damage which is influenced by various factors, one of which is the vibration sensitivity setting factor of the roadroid application system during the start calibration of the type of vehicle selection. The IRI and speed value based on the output of the roadroid application have a high correlation of 87.8% on medium car.

Analysis of Vehicle Type for Roadroid Application

Nowadays, the cheapest and most reliable technology method used to measure IRI (International Roughness Index) value is Roadroid which is an Android application developed from a company in Sweden by using a built-in vibration sensor on a smart phone. This smart phone is placed in the vehicle and the vibration of the vehicle will be recorded and analyze and converted to IRI. In Roadroid application, there are four choices of vehicle types namely motorcycle, small car, medium car, four-wheel drive jeep. Since this is aggregate of many types of vehicles, the accuracy of the result will vary for example different country will have different type of vehicles. This research found that medium car has the highest accuracy rate of 99,7% compare compare to small car and motorcycle

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km2 in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs.

Development of Roughness Prediction Models for Laos National Road Network

The International Roughness Index (IRI) has been accepted globally as an essential indicator for assessing pavement condition. The Laos Road Management System (RMS) utilizes a default Highway Development and Management (HDM-4) IRI prediction model. However, developed IRI values have shown the need to calibrate the IRI prediction model. Data records are not fully available for Laos yet, making it difficult to calibrate IRI for the local conditions. This paper aims to develop an IRI prediction model for the National Road Network (NRN) based on the available Laos RMS database. The Multiple Linear Regression (MLR) analysis technique was applied to develop two new IRI prediction models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections. The final database consisted of 83 sections with 269 observations over a 1850 km length of DBST NRN and 29 sections with 122 observations over a 718 km length of AC NRN. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The model’s parameter analysis confirmed their significance, and R2 values were 0.89 and 0.84 for DBST and AC models, respectively. It can be concluded that the developed models can serve as a useful tool for engineers maintaining paved NRN.

Effects of international roughness index on vehicle emissions

Pavement conditions, such as roughness, measured by the international roughness index (IRI) play an essential role in improving vehicle fuel economy and GHG emission reduction. Considering this, the objective of this study was to investigate the relationship between IRI and vehicle emissions, derived from the HDM-4 model. IRI and speed values were collected for every 100 m for two road segments located in Brazil, State of Ceare, using a smartphone app called SmartIRI. The IRI values were inserted in the HDM-4 model, as well as the vehicle and road characteristics, in order to derive Instantaneous Fuel Consumption (IFC) and tailpipe emissions (TPE) for CO2, NOx, CO, SO2, HC and PM. The correlations between IRI and speed reached 99.8%. Regarding the relationship between IFC and the tailpipe emissions (TPE), ~49% and 63-64% of the HC and CO tailpipe emissions could be explained by fuel consumption and IRI variations, respectively.

Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

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.

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

Pavement deterioration models are a vital feature in any pavement management system since they are capable of predicting the evolution of pavement characteristics. Pavement roughness is measured by most of the highway administrations due to its relation to comfort and safety, generally by means of the International Roughness Index (IRI). The Regional Government of Biscay (Spain) has collected IRI values since 2000 on its road network. Although many models have been developed for flexible pavements, very few have been proposed for semi-rigid pavements. The paper aims to develop IRI prediction models for semi-rigid pavements in single-carriageway roads. Considering the high quantity of available information in the database, deterministic models were selected. Due to the importance of the pavement structure in IRI evolution observed in flexible models, only segments with completely known pavement details were employed, i.e., a section where the complete structure is known: materials and thickness of existing layers above the subgrade. The pavement age, as precise as practical, and the accumulated total traffic and heavy traffic through the section were identified as roughness accelerating factors. Conversely, the materials used in base and subbase layers, their thickness, and the total thickness of bituminous layers were observed as degradation reducing factors. Possible treated base and subbase materials included in the model were soil–cement, gravel-cement, and gravel and slag. The obtained model achieved a determination coefficient (R2) of 0.569. Additionally, the bituminous material of the surface layer was verified as an affecting factor too, which can be introduced to improve the model’s accuracy. Possible surface layer materials included dense (D) and semi-dense (S) asphalt concrete, with a maximum aggregate diameter of 16 and 22 mm, discontinuous mixing (BBTM 11A) and porous asphalt (PA 11). The additional model achieved a higher determination coefficient (0.645) and, hence, a more accurate IRI prediction resulted.

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

Development and Preliminary Evaluation of a Varying-Speed Road Profiler

Abstract The International Roughness Index (IRI) has been widely used to characterize the road ride quality in the highway engineering community. IRI is commonly obtained via the longitudinal profile data collected by a high-speed inertial profiler. It is well known that the IRI value based on an inertial profiler can be biased under low-speed or varying-speed conditions. This stems from the inherent limitation of the accelerometer embedded in the measurement system. This study proposes a different approach to solving the problem by employing three-dimension laser scanning technology. Without relying on the accelerometer to offset the vehicle vertical dynamics/vibration, the new approach uses the measurement by a line laser to transfer the elevation along the driving direction. Following this principle, a road profiler was developed and installed on a host vehicle. A preliminary evaluation of the newly developed profiler was conducted on two urban road sections in Beijing. The elevation was focused on wavelength, IRI index, and repeatability analysis. The results showed that the proposed profiler could effectively capture road elevation and accurately characterize ride quality. It was indicated that this new equipment could serve as a useful tool to better accommodate the ride quality measurement under varying-speed data collection conditions, particularly in the urban environment.

Smartphone-Based Pavement Roughness Estimation Using Deep Learning with Entity Embedding

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.

Long-term performance evaluation of Iowa concrete overlays

ABSTRACT The use of concrete overlays has long been recognised as a cost-effective pavement maintenance and rehabilitation strategy. However, the long-term performance of various types of concrete overlays has not been fully investigated since there has not been enough performance data available to conduct such an evaluation. Concrete overlays have been regularly constructed on Iowa roadways since the late 1970s and many older projects are still in use. Performance-related data for in-service concrete overlays have been acquired from the available resources to evaluate long-term performance of concrete overlays in Iowa. The information collected includes Pavement Condition Index (PCI), International Roughness Index (IRI), overlay type, construction year, overlay thickness, joint spacing, traffic, and other construction and design-related data. Based on an evaluation of PCI and IRI changes during service life, it is observed that concrete overlays can provide at least 20 years of service life. In terms of PCI ratings, 89% of concrete overlays investigated have PCI values greater than 60% as of the time of the analysis. Similarly, 93% of concrete overlays have IRI values lower than 2.7 m/km (170 in/mile). The effects of overlays type and design features on long-term performance of Iowa concrete overlays are also discussed.

Comparison of road inequality values using roughometer III and roadroid applications

Roads are essential in the development of an area, where driving safety and comfort are a measure of road construction. We can check road construction by getting an IRI value on a road segment. IRI (International Roughness Index) is the roughness index that most commonly obtained from measured longitudinal road profiles. Roughometer III and Roadroid applications are the tolls to collect IRI data. Roughometer III has more precision than Roadroid, but the Roughometer III is more expensive and more complex than Roadroid. This study tries to make a comparison of IRI values and their correlation between the Roughometer III and Roadroid. The results show that the comparison of IRI Roughometer III values and Roadroid Applications is slightly different. The linear correlation analysis of IRI values on Roughometer III and eIRI values on Roadroid is IRI = 0.8999eIRI + 0.5786 with a correlation level of = 0.8391.

Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions

H ighway safety is a major priority for public use and for transportation agencies. Pavement roughness indirectly influence drivers' concentration, vehicle operation, and road traffic accidents, and it directly affect ride quality. This study focuses on analyzing the influence of pavement roughness on traffic safety using traffic, pavement and accident data on dual and single carriageway operated under heterogeneous traffic conditions in South-west, Nigeria. Traffic crash data between 2012 and 2015 was obtained from the Federal Road Safety Commission (FRSC) and International Roughness Index (IRI) data from the Pavement Evaluation Unit of the Federal Ministry of Works, Kaduna. Crash road segments represented 63 percent of the total length of roads. IRI values for crash and non-crash segments was a close difference of 0.3,This indicates that roughness is not the only factors affecting occurrence of traffic crashes but a combination with other factors such as human error, geometric characteristics and vehicle conditions. Crash severity was categorized into Fatal, serious and minor injury crashes. In all cases, the total crash rate increases with increase in IRI value up to a critical IRI value of 4.4 and 6.15 for Sagamu-Ore road and Ilesha-Akure-Owo road respectively, wherein the crash rate dropped. The conclusion is key in improving safety concerns, if transportation agencies keep their road network below these critical pavement conditions, the crash rate would largely decrease. The study concluded that ride quality does not directly affect traffic crash rate.
 Keywords: Pavement conditions, traffic safety, International Roughness Index, crash rate, carriageway.

Development of pavement roughness master curves using Markov Chain

ABSTRACT Nowadays, probabilistic prediction models commonly represented by Markov Chain Process (MCP) attract more attention in pavement management systems. Additionally, roughness condition indices provide a broad judgment. One element that contributes the most to the roughness progression is the initial roughness of pavements which has not been considered in the prediction models developed by MCP. On the other hand, the prediction results of MCP, which address the whole pavement network as an average, are inconvenient to be deployed in decision-making programmes. This paper utilised MCP to forecast pavement roughness regarding its initial value. International Roughness Index (IRI), extracted from the long-term pavement performance (LTPP) database, was selected to be analyzed. Based on M&R history, four major families, including 1770 pavement sections in total, were introduced. The prediction process of MCP was modified led to the direct prediction of IRI values instead of the deteriorated portion of the pavement sections. A framework, which had derived from the concept of Master Curves, is proposed to pave the way for the optimisation programmes to be applied to the MCP results. The Root Mean Squared Errors (RMSE) of the composed master curves were significantly low; the average RMSE was 0.00675.

Pavement Deterioration Model Using Markov Chain and International Roughness Index

Pavement deterioration leads to drop in serviceability and possibly failure of pavement sections due to initiation and expansion of distresses such as cracks and rutting. This paper aimed at predicting the future condition of pavement sections based on Markov chain model and the international roughness index (IRI). Developing this model can facilitate life cycle analysis and selecting the correct treatment at the right time. The historical IRI data of Canadian pavement sections were collected from Long term pavement performance (LTPP) database. IRI values were used to assess condition of the pavement sections based on the recommended ranges by the Federal highway administration (FHWA). The transition probabilities were estimated using the percentage prediction method based on historical condition data extracted from the LTPP. These probabilities are assembled in a transition probability matrix essential for the Markov chain model. The developed matrix can be used to forecast pavement conditions after any number of transition periods. The developed method assists in predicting pavement performance and facilitates the decision-making process. The method is applied to a real case study to examine its validity. The model can be expanded further by considering additional data from additional pavement networks.

Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data

Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in pavement evaluation. High correlation between International Roughness Index (IRI) and user comfort led to modeling pavement deterioration based on IRI during PMS history. On the other hand, in recent years Artificial Neural Network (ANN) which is a valuable tool of soft computing is used in pavement modeling, widely. This study assessed the development of an ANN pavement deterioration model based on IRI using Back-Propagation Neural Networks (BPNN) technique. The Long-Term Pavement Performance (LTPP) data was extracted from two General Pavement Study (GPS) sections including GPS-1 and GPS-2. After training and testing the developed model, results were compared with a polynomial regression model. Results showed that predicted IRI values with developed ANN model have a good correlation with measured values rather than the polynomial regression model for both GPS-1 and GPS-2 sections.

Investigating the Effects of Pavement Roughness on Freeway Safety using Data from Five States

Traffic crashes occur usually because of a combination of human, roadway/environment, and vehicle factors. Pavement condition is closely related to these three factors as it is one of the most important roadway/environment factors and it affects driving behavior and vehicle performance at the same time. Previous studies have shown that pavement condition plays an important role in safety. This study develops four different safety performance functions to evaluate the effect of pavement roughness, measured by the International Roughness Index (IRI), on the number of crashes using the interstate highway data from five U.S. states representing different geographical and weather regions: Arizona, Colorado, Florida, Maryland, and Michigan. The modeling results identify many significant variables including traffic volume and proportion of trucks, through lane count, shoulder type, median width, high-occupancy vehicle (HOV) lane operation and HOV lane count, speed limit, area type along with IRI-related factors. The results indicate that increased IRI (deterioration of pavement quality) contributes to large numbers of total crashes. On interstate highways with speed limits of 70 mph and higher, the effects of IRI are relatively smaller. On the other hand, the effects of IRI increase with a larger traffic volume. Based on the modeling results, seven crash modification functions of IRI values by crash type and speed limit were estimated. The findings from this study are expected to be useful for both pavement and safety engineers to understand the relationship between IRI and safety on freeways.

Analysis of accident fatalities on South Sulawesi Inter-urban Roads

Traffic accidents are the fundamental yardstick for road safety. In South Sulawesi, particularly the four road sections which become the main focus of this study are Jeneponto-Bantaeng, Bantaeng-Bulukumba, Bulukumba-Tondong, and Sengkang-Impa-impa. Topographic conditions in South Sulawesi generally have various surfaces, including road sections area of this study focus. These various topographical conditions cause a chance of a traffic accident to occur at a certain point on the geometric features of the road, either on a straight or bend road/ curve road, grad (+) or grad (-), or the influence of the average International Roughness Index (IRI) value of a road. This study aims to analyze significant road geometric features that determine the fatality rate of the traffic accident. The results obtained from this study are significant factors of geometric road features that statistically affect and determine the fatality rate of traffic accidents; namely road geometry, road gradient, and IRI value, or with the conclusion is that all predictor variables are significant factors in influencing fatality rate of the traffic accident.