International Roughness Index Values Research Articles

Comparative Analysis of Deep Neural Networks and Graph Convolutional Networks for Road Surface Condition Prediction

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings.

Analisa Derajat Kejenuhan, Perlengkapan Jalan, dan Nilai Kerataan Pada Jalan Raya Kepandean, Kabupaten Tegal

Road safety audits are an effort to improve safety and prevent traffic accidents. The implementation of the audit can be carried out by the survey method directly in the field and can use the Hawkeye car to get the International Roughness Index (IRI) value. The results of this study show that the condition of the road on the Kepandean Highway section with the IRI value of the route from the direction of SMPN 1 Dukuhturi is 6.5 while the IRI value of the route to SMPN 1 Dukuhturi is 6.3 so that it can be concluded that the road is in the medium category (not good) and requires periodic repair and maintenance efforts

Modeling the Influence of Internal Factors on Vehicular Mobility Performance

Traffic mobility is significantly influenced by road conditions and the internal factors governing vehicular performance. Internal factors play a crucial role in vehicular mobility, influencing vehicles operate and performance. While existing vehicular mobility models primarily address external factors, external factors such as governor, engine, gear train and differential unit remain underexplored. This study formulates a comprehensive model to address this gap, focusing on the internal components that affect vehicular mobility. The proposed model employs both analytical and numerical methods to derive transfer functions for these subsystems using Matrix Laboratory (MATLAB), aiming to capture the dynamic behavior of vehicles under various conditions. The model was evaluated by examining vehicle transaction times across different road surface qualities, measured by the International Roughness Index (IRI) values of 3.5, 3.0, and 2.0, and tractive forces ranging from 4500 N to 17750 N, with applied pedal forces of 50 N, 100 N, 150 N and 200 N. Results indicated that higher tractive forces lead to reduced transaction times, with IRI values of 3.5 m/km showing a decrease from 122.0 seconds to 31.0 seconds as tractive forces increased from 4500 N to 17750 N. Similarly, for IRI values of 2.0 m/km, the transaction times reduced from 67.5 seconds to 7.5 seconds under the same conditions. The analysis further demonstrated that increased applied pedal forces correspond to higher tractive forces, thereby enhancing vehicular mobility and performance. These findings highlight the critical role of internal factors in optimizing vehicular mobility and performance, suggesting that internal subsystem dynamics should be integrated into future mobility models for more accurate and comprehensive assessments.

International Roughness Index (IRI) Assessment Based on The Application of ROADBUMP and ROADROID on Sei Duri-Mempawah Road Section

The research assessed the condition of the Sei Duri-Mempawah road segment using the International Roughness Index (IRI) to determine surface roughness. Two smartphone applications, Roadbump and Roadroid, were evaluated against traditional laser profilometers to measure IRI values. The results indicate that at a speed of 20 km/h, the Roadbump application showed that 26% of the left lane was in good condition, with moderate, slightly damaged, and severely damaged segments accounting for 68%, 4%, and 2%, respectively. For Roadroid at the same speed, the left lane's condition was 76% good, while moderate, slightly damaged, and severely damaged conditions were at 16%, 3%, and 5%. Both applications demonstrated variations in IRI values at higher speeds, with Roadroid generally yielding higher roughness levels than Roadbump. Comparative analysis revealed that Roadbump at 40 km/h provided results closer to the laser profilometer, with up to 78% similarity on the left lane. However, the correlation analysis with BPJN data revealed weak correlations for most cases, except for a stronger relationship (R = 0.5521) between the 40 km/h Roadbump application and the laser profilometer. Thus, Roadbump at 40 km/h is recommended for practical field assessments.

Exploring the relationship between the International Roughness Index (IRI) and the ISO 8608 roughness coefficient (Gd (n 0))

ABSTRACT Globally, diverse agencies employ varied metrics to gauge road roughness, complicating the exchange of information for pavement maintenance. This study addresses this challenge by establishing a theoretical connection between the International Roughness Index (IRI) and the ISO 8608 Power Spectral Density (PSD)-based roughness coefficient, namely Gd (n 0). Based on random vibration theory, this paper first deduces a closed-form explicit solution for IRI related to Gd (n 0), solidifying a theoretical link. Next, the variability and convergence of the proposed solution are examined through Monte Carlo simulations using a quarter-car vehicle model with the Golden Car parameters. Then, we compare computed results from this study with those existing solutions from past studies, verifying that the proposed solution captures the curve trend well and agrees with the average value of existing solutions based on field measurement data. Finally, this paper proposes the IRI values corresponding to various ISO road class thresholds and a new Gd (n 0) scale for ride quality assessment of highways.

Low-Cost Portable Road Smoothness Testing Method Based on Pseudo-Vibration Velocity Range

Road smoothness not only directly affects the safety and comfort of vehicle travel but also relates to the efficiency and cost-effectiveness of road maintenance. Traditional road smoothness detection methods usually require professional equipment and personnel, leading to high costs and cumbersome operations. Therefore, finding a low-cost, simple, and accurate method for detecting road smoothness is of great significance. This study uses vehicle-mounted acceleration sensors to detect road smoothness, establishing a correlation between driving vibration acceleration data and the international roughness index (IRI). For this research, a driving vibration acceleration data acquisition device was developed, and the driving acceleration data from the test sections were denoised and their feature values extracted. The pseudo-vibration velocity range was used as the characteristic index representing the road surface smoothness IRI value. Testing with different vehicle types showed that the method is applicable to both sedans and SUV models, yielding a relative error of 8.9% for the sedan smoothness test model and 6.7% for the SUV smoothness test model. This study contributes to conducting large-scale road smoothness detection at a low cost, improving the efficiency of road maintenance and operations.

Reliability-Based Pavement Roughness Progression Modeling Using Bayesian Approach

This paper aims to develop reliability-based roughness progression models for bituminous pavements across different climatic zones in India. The methodology involves dividing the Indian geographical region into six climatic zones with specific climatic characteristics that influence pavement deterioration. This delineation is achieved using K-means clustering and considers parameters such as temperature, rainfall, and humidity. The roughness progression models and reliability interpretations are developed through a Bayesian regression framework. Initially, International Roughness Index (IRI) progression models are created for three climatic zones: hot and dry, warm and humid, and moderate. These models are based on time series data from 2015 to 2016, gathered from 130 pavement sections in each zone. To validate the models, additional time series data from 2016 to 2017 are utilized. The forecast results indicate that the hot and dry zone exhibits the highest IRI progression rate for specified causal variable values, followed by the warm and humid zone and the moderate zone. The adoption of the Bayesian regression framework provides probabilistic parameter distributions for model coefficients, enabling the assessment of model-level reliability. The study demonstrates that, by adjusting the quartile of causal variables, the overall reliability of the model can be improved, thereby reducing the deviation from actual IRI values. Moreover, it is possible to evaluate variable-level reliability by examining the influence of individual variables on roughness progression.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

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.

Physics-informed neural networks to advance pavement engineering and management

Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. This paper discusses applications of physics-integrated ML to advance pavement engineering. To demonstrate an example, a PINN model was pretrained to approximate the simulation of vehicles’ suspension responses to longitudinal road profiles. The parameters of the outer layers were finetuned to adapt model output to the standard International Roughness Index (IRI), while keeping the pretrained inner layers to preserve the embedded physical knowledge of the suspension behaviour. The PINN model showed low bias and standard error in predicting IRI values on training, test, and an independent dataset from an autonomous vehicle study by the Ford Motor Company. This approach to reconcile and supplement infrequent survey data with spatiotemporally continuous data (from connected vehicles) can enhance data-driven practices for pavement design, maintenance and asset management.

Applications of using connected vehicle data for pavement quality analysis

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

Modeling of road damage prediction due to overload using HDM-4 method

The road use load should essentially conform to the plan load as stated in the design guidelines. But in reality, the load received by the road is more than standard, so it has the potential to accelerate road damage. This study aims to predict the increase in the value of the International Roughness Index (IRI) and model the increase in value (IRI) due to overload. The approach method used in the analysis is the prediction of an increase in the IRI value due to excess load. The data collection method is carried out on road sections which include IRI values, vehicle load testing, CBR values and environmental conditions. The location of data collection is carried out on BTS Provincial Road. Surabaya City – Legundi. The analysis method for the prediction of the IRI value is the HDM-4 method, while to model the prediction of the increase in the IRI value is used exponential method with various parameters related to the results. The prediction results show that overload affects the increase in the IRI value. From the initial IRI value of 2.37, if the road is traversed by vehicles with standard loads for 20 years, the IRI value increases to 10.42. While the road traveled by overloaded vehicles within 20 years from an IRI value of 2.37 will increase to 16.88. With overload, the IRI value increases very sharply with the exponential model y=2.3583e0.0982x. While the standard load by generating an exponential model y=2.3825e0.0739x. From the results of the exponential model, load overload resulted in a significant increase in the IRI value with a growth rate of 0.0982. When compared to normal expenses which only experienced a growth rate of 0.0739. The modeling results show that overload will increase the growth of very high IRI values with the exponential model increase graph

Feasibility of Modernizing the Acceptable International Roughness Index Value

The U.S. National Highway System has set a standard for acceptable pavement roughness, measured by the international roughness index (IRI) at less than 2.68 m/km (170 in./mi). This value corresponds to a present serviceability rating (PSR) of 2.5, where half of the highway drivers perceive the pavement ride quality as unacceptable with a repair need. For over 3 decades, public transportation agencies in the U.S. have used this IRI value to determine when pavement surface rehabilitation is needed. However, with advancements in vehicle suspension systems improving ride quality, the current acceptable IRI value corresponding to 2.5 PSR has been questioned. To our knowledge, studies in the literature have yet to explore this issue. This study aims to evaluate the feasibility of modernizing the acceptable IRI value through nationwide, large-scale field testing, which is typically expensive and time-consuming. The IRI-present serviceability index (PSI) equation that is capable of converting the acceptable IRI value into the estimated 2.5 PSR (i.e., the PSI) was selected and calibrated by applying test quarter-car parameters synthesized through a literature review. Then, the calibrated IRI-PSI equation was used to find a new acceptable IRI value that was evaluated for the statistical significance and the financial benefits of the new value. The evaluation results suggest a need for a nationwide field test to modernize the current acceptable IRI value. Public transportation agencies can easily adapt the technical approach we used to calibrate the IRI-PSI equation without altering their current IRI measurement systems.

New Approach to Determining the Roughness of Bicycle Roads

The international roughness index (IRI) is one of the important criterion used to determine the roughness of roads. Various devices are available to determine IRI, however, they are mostly used to determine the condition of highways. Studies to determine the roughness of nonmotorized roads, such as bicycle roads, which have become increasingly important in recent years, are limited. Therefore, this study presents a cost-effective roughness measurement method that can measure both highways and nonmotorized roads. For this, a mountain bike was used as the test vehicle, and for the first time, the stiffness and damping coefficients of the bicycle tire were taken into account when calculating the IRI. First, the stiffness and damping coefficients of the bicycle’s front tire were calculated. These coefficients were then used as input data for the equation of motion with one degree of freedom, and the IRI was calculated. The 660-m long divided highway (1,320 m in total) on the Bursa Uludagğ University campus was chosen as the test road. A laser profilometer device was used to quantify the reference IRI values of this test road. The same road was then measured by bicycle. The obtained results were evaluated using mean absolute percent error, mean absolute error, root mean square error, and mean square error metrics. All these indicators showed very promising results, suggesting that this new method could be a cost-effective and reliable approach to determining the IRI of highways, especially nonmotorized roads.

Development of an Unpiloted Aircraft System–Based Sensing Approach to Detect and Measure Pavement Frost Heaves

Abstract The combination of low temperatures, precipitation, and high-water table causes the formation of subsurface frost in pavements leading to the formation of frost heaves. This leads to significant damage to the structural integrity of the pavements and causes extensive surface roughness. Frost heaving is a common type of distress in cold climate regions, especially for road and airport infrastructures located in remote areas. Research discussed in this article focuses on determining the efficiency and accuracy of photogrammetry-based sensing systems to measure the extent of pavement frost heaves. The sensors are mounted on an unpiloted aircraft system (UAS) capable of providing measurements over large spatial domains in a single flight and are well suited for difficult-to-access regions. Experiments have been conducted on simulated heaves as well as actual cold climate pavement sections. The research described herein provides suitable UAS flight parameters for conducting frost heave measurements. Flight at an altitude of 50 m above the ground surface and a flight speed of 5 m/s with capturing images every 2 s resulted in more than 80 % front and side image overlap. The pavement surface profile has been constructed from extracted photogrammetry elevation data collected in three different seasons. Pavement surface roughness has been measured in terms of the international roughness index (IRI), and variation between measured IRI values has been evaluated during different seasons because of the formation of the frost heave distress. As it is expected, the IRI values increase as the cold season approaches. Moreover, the measured IRI values have been compared with the most recent available IRI data collected by an instrumented vehicle. The key outcomes of this work demonstrate that photogrammetry can reliably detect pavement frost heaves and provide high confidence in the future development of an automated system to measure pavement roughness that is attributable to frost heave distresses.

Reconciling Pavement Condition Data from Connected Vehicles with the International Roughness Index from Standard Monitoring Equipment Using Physics-Integrated Machine Learning

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.

Using NDT Data to Assess the Effect of Pavement Thickness Variability on Ride Quality

Pavement condition largely determines its long-term behavior and is of paramount importance for rehabilitation and maintenance management. The use of non-destructive testing (NDT) systems to assess pavement condition has gained much popularity. Often, well-known NDT systems are combined to take full advantage of the capabilities of each system. Combining independent NDT systems to optimize the assessment process is a scientific challenge. With this in mind, the purpose of this paper is to investigate the extent to which data from two independent NDT systems can be combined: pavement thickness obtained with ground penetrating radar (GPR) and roughness data obtained with a road surface profiler (RSP). In particular, the objective of this study is to determine whether the expected variations in asphalt layer thickness, due to the construction process and the different pavement cross sections along the same road/highway road, may have an impact on pavement roughness as expressed in International Roughness Index (IRI) values. GPR and roughness data are collected, processed, and analyzed. The analysis results show that thickness variations are reflected in pavement roughness. The greater the variation in asphalt layer thickness, the greater the IRI values. Furthermore, it is argued that the GPR capabilities can be used for an initial assessment of the expected pavement quality.

Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

Continuous road-pavement condition monitoring is essential to maintain the effectiveness and performance of road infrastructure. It also helps to optimize resource allocation and prioritization for pavement rehabilitation programs. The traditional methods that have been used for assessing road pavement condition are high cost and time consuming. Contrary to conventional methods, low-cost and new emerging technologies such as smartphones and drones can be used. The study aims to investigate the merits of using two different types of device for assessing road roughness: the smartphone as a response-type and the drone as a non-contact-type device. To this end, firstly, linear vertical acceleration data is collected by using a smartphone that is mounted on a car dashboard. Then, the collected data is used for calculating vertical displacement data using a fast Fourier transform. The displacement data is used for calculating international roughness index (IRI) as a measure of road roughness. Secondly, a drone flies over the studied road to construct a 3D model, extract the longitudinal profile, and eventually calculate the accurate IRI values of the road. Finally, the results of the approaches above will be compared with the results of a road surface profiler (RSP) as a reference method. Although the results reveal a significant correlation (above 0.8) of both methods with the RSP method, from an operational point of view, the smartphone was more cost and time effective, and also an easy-to-use approach for the purpose.

Comprehensive Investigation of Commercial Motor Vehicle Crashes at Signalized and Unsignalized Intersections in Kentucky

This study develops safety performance functions (SPFs) for “fatal and injury” (FI) commercial motor vehicle (CMV) crashes (here, a CMV is defined as a large truck or a bus) at both signalized and unsignalized intersections in Kentucky. Five count-response regression models—negative binomial, Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP)—were developed and compared at each of the selected signalized and unsignalized intersections. FI CMV intersection-related crash data in Kentucky over a period of five years (2015 to 2019) were used. Information on road-specific characteristics, for example, shoulder width, median width and type, International Roughness Index (IRI) value, and traffic volume (including annual average daily traffic [AADT] and heavy vehicle percentage [HVP]) was collected. Additionally, information on intersection-specific characteristics, for example, number of through lanes and presence of exclusive left- and right-turn lanes on major and minor roads, was collected using Google Maps’ street view time slider back to the crash year. For both signalized and unsignalized intersections, the ZI-HTCMP model (with a varying dispersion parameter) outperformed the other comparative models. With regard to signalized intersections, a speed limit of greater than 45 mph on the major road, a speed limit greater than 45 mph on the minor road, and a median on the major road wider than 3 ft were significantly associated with increased FI CMV crashes. As far as unsignalized intersections were concerned, higher major road AADT, 4-legged configuration, a speed limit greater than 45 mph on the major road, and an IRI value of greater than 100 for the major road were significantly associated with increased FI CMV crashes. The SPF results (for each of the signalized and unsignalized intersections) were then used to identify the top 10 hazardous locations for each intersection type.

THE ASSESSMENT OF NATIONAL ROAD SURFACE CONDITIONS ANALYSIS BASED ON THE INTERNATIONAL ROUGHNESS INDEX (IRI) METHOD USING ROADBUMP APPLICATION

Roads are infrastructure that has a vital role for the community in supporting social and economic activities. Therefore, appropriate handling information is needed for road maintenance and improvement to be on target. One of the methods used is the International Roughness Index (IRI) method with the help of the Road bump application. The purpose of this study is to analyze and recommend handling road surface conditions, as well as to test the validity of the Road bump application with a Laser Profilometer. This research was conducted on the 10 KM section of Trans Kalimantan Road. Comparative data is obtained from secondary data issued by BPJN in 2021 using the Laser Profilometer tool. This study found that the condition of the Trans Kalimantan Road was 80% in the excellent category, 18% in the moderate category, and 2% in the slightly damaged category, and had similarities with the BPJN IRI value of 95%. Road bump also tends to the IRI value to be greater than the BPJN IRI value. Based on the results of this study, the Road bump application can be used as an alternative tool for measuring road surface conditions using the International Roughness Index (IRI) method.

International Roughness Index prediction for flexible pavements using novel machine learning techniques

International Roughness Index (IRI) is an important pavement performance indicator that is widely used to reflect existing pavement condition and ride quality. Due to the importance of this significant index, the current research aims to develop a precise IRI prediction model using the Gaussian Process Regression (GPR) and Locally Weighted Polynomials (LWP). The long-term pavement performance (LTPP) datasets of pavement age, initial IRI, alligator, longitudinal and transverse cracks, standard deviation of rutting, and subgrade plasticity index variables are employed in predicting IRI. These datasets are collected from 126 different flexible pavement sections of the LTPP specific pavement studies (SPS-1) located in different climatic zones in the US. The total number of IRI measurements in the collected database is 925 which covers a wide range of IRI values. Multiple linear regression (MLR) is firstly applied to classify the input variables. Then the MLR model is compared with four machine learning techniques which are GPR, LWP, Particle Swarm Optimization-Adaptive Network based Fuzzy Inference System (PSO-ANFIS) and PSO-Artificial Neural Networks (PSO-ANN). The developed models’ performance is validated using different statistical indices, including the coefficient of determination (R2). The results demonstrate that the GPR (R2=0.93) and LWP (R2=0.90) outperformed the PSO-ANFIS (R2=0.65) and PSO-ANN (R2=0.52) in predicting IRI. Thus, the GPR model is found to be more accurate for IRI modeling compared to the hybrid investigated models.