Pavement Roughness Research Articles

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughness Using Smartphone and Bicycle Data

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment.

A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

Pavement damage characteristics in the permafrost regions based on UAV images and airborne LiDAR data

The rapid degradation of “Xing'an-Baikal permafrost” in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan section (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction

A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction

Statistical Analysis of Ordered Pavement Roughness Perceptions with Two-Group Random Effects

Statistical Analysis of Ordered Pavement Roughness Perceptions with Two-Group Random Effects

An enhanced indirect modal identification procedure for bridges based on the dynamic response of moving vehicles

Indirect dynamic structural identification, referred to as Vehicle Scanning Method (VSM), pertains to the estimation of bridge modal parameters by using data recorded from sensors directly mounted on a moving vehicle. This procedure has recently gained increasing attention due to its low cost and simple implementation. Nevertheless, the non-stationary and complex nature of the vehicle–bridge interaction phenomenon poses some limitations to the applicability of the method, especially in terms of accuracy. In this regard, in this paper, an enhanced procedure for the dynamic identification of bridges modal parameters based on the VSM is introduced, taking into account the effects of vehicle/bridge damping and road pavement roughness. Specifically, based on the Variational Mode Decomposition (VMD) method, the relevant Intrinsic Mode Functions (IMFs) and corresponding modal frequencies are determined from the recorded signal. Further, the Natural Excitation Technique (NExT) is adopted in conjunction with a noise-robust area ratio-based approach, for modal damping ratios estimation. Finally, mode shapes are evaluated by properly correcting the instantaneous amplitudes of each IMF considering the influence of the corresponding estimated modal damping ratio. Several numerical simulations are presented to show the validity of the proposed procedure and comparisons with the classical Hilbert Spectrum-based identification approach are employed to assess its reliability and improved accuracy.

A Kriging Surrogate Model for Ball Grid Array Electronic Packaging With Stochastic Material and Geometrical Parameters

Abstract Ball grid arrays (BGAs) offer significant advantages in the automotive industry, such as their small size and high integration density, making them a promising electronic packaging approach. However, the operating environment of automobiles is more complex compared to other applications, primarily due to vibrations generated by power engines and oscillations caused by pavement roughness. The resonant frequencies of electronic packaging structures play a crucial role in system reliability and safety. However, accurately describing the implicit relationship between system resonant frequencies and material and geometrical parameters can be challenging. A Kriging surrogate model (KSM) is proposed by the combination of the Latin Hypercube stochastic sampling with finite element computation. Four different BGA configurations are established with either the initial values in the deterministic model or the specified sampling interval ranges in the stochastic model. The results of the finite element model (FEM) for BGA electronic packaging are validated and demonstrate qualitative agreement with published literature. The impacts of material and geometrical parameters on the resonant frequencies are investigated and compared. The mean, maximum, minimum, and variance are recorded based on a large database of stochastic samples. The feasibility of KSM for the resonant frequency prediction of BGA is confirmed by its satisfactory accuracy and computational efficiency.

Optimization of Network Pavement Life-Cycle Cost: A Piecewise Linearized Approach

Optimal management of pavement assets becomes important because of the escalating challenges in this field. Managing the network of paved roadways in the United States necessitates the introduction of optimization tools, such as mathematical optimization. Although several efficient optimization techniques are available, an effective approach requires a special structure of the problem. The optimization of a pavement maintenance and rehabilitation schedule poses a complex challenge, primarily because of two factors: nonlinearity and the presence of integer decision variables. Nonlinearity exists as a result of multiple sources. One source is pavement condition, commonly measured by pavement roughness. This study introduces a method that uses piecewise linearization of the pavement roughness progression function. Circular shift was used to linearize the resulting optimization model. A hypothetical city, the size of Cook County in Chicago, was used as a case study. Both agency cost and user cost were considered in the model. Agency cost was determined from consultations with professionals and online data, whereas user data on existing models. The study demonstrated that increasing the agency cost by investing one dollar per lane mile per year has a high return on investment until a certain threshold, beyond which allocating more budget does not lead to a reduction in life-cycle cost.

Effects of pavement roughness on shakedown limits under moving traffic loadings

This study proposes a lower-bound shakedown analysis method for pavement systems under moving traffic loadings considering pavement roughness. The effects of key variables, including moving speed, wavelength irregularity, and the modulus and thickness of pavements, on the shakedown limit are discussed. The calculation results indicate that pavement roughness has a considerable effect on shakedown limits, which can be overestimated in some cases when the pavement roughness is not considered. The effect of pavement roughness on the shakedown limit of a homogeneous subgrade layer gradually decreases with increasing speed. Additionally, the shakedown limit decreases with wavelength irregularity, particularly under high-speed traffic loadings. Furthermore, the effect of pavement roughness on the shakedown limit of a two-layered pavement system is more pronounced for a larger pavement modulus and thickness and a lower moving speed.

Revealing the impact of losses on flexible pavement due to vehicle overloading

Pavement overload has become a significant concern in Indonesia due to its damaging effects on the pavement system, where most roads use flexible pavements. Meanwhile, overloading is also typical in other developing countries, causing everything from damage to accidents to the detriment of road users and operators. The current problem is that there is no definite knowledge of handling these overloaded vehicles, which has adverse effects. Therefore, this study aims to reveal the impact of losses caused by overloaded vehicles on flexible pavement. This research uses vehicle traffic load data to examine actual conditions in the field, which are converted to axle load values for each vehicle and used to determine pavement condition using the truck factor approach, mechanistic-empirical design, pavement smoothness level, and the impact of overloading due to fuel consumption by vehicles on flexible pavement. The results show that overloaded vehicles harm the flexible pavement. The increase in truck factor values for all vehicles in overloaded vehicles beyond the maximum allowable load results in more damage to the flexible pavement with an increase in truck factor values up to 83 %. The effect of overloading can be mitigated by increasing the thickness of the asphalt layer and the modulus of asphalt, so it is essential to pay attention to the quality of asphalt in anticipation of this overloading. To anticipate the overload phenomenon, it was found that the thickness of the asphalt overlay in the range of 170–205 mm could mitigate this detrimental effect. In addition, overloading affects the flexible pavement roughness and vehicle fuel consumption. Increased roughness and fuel consumption will increase road maintenance costs and affect driving comfort and safety. In addition, excessive fuel consumption can pollute the surrounding environment

Constructing Pavement Roughness of a Three-Dimensional Bridge in Abaqus for Vehicle Scanning Method

In this study, a procedure for constructing pavement roughness in a three-dimensional bridge subjected to a vehicle using Abaqus is proposed for numerical simulation of vehicle–bridge interaction problems. As the plug-in RufGen, a graphical user interface in Abaqus/CAE for surface interaction, creates a hexahedron with a rough surface and attaches it to the bridge deck, the resulting weight of a bridge model can be much larger than the original one. In the vehicle scanning method, it is essential to keep a finite element bridge model with an unchanged weight after imposing a rough surface on it in consideration of the roughness effect for bridge health monitoring. As such, the proposed procedure is aimed at resolving this issue, with the following two highlights: First, the Toeplitz matrix is introduced to generate a rough surface in three dimensions to ensure the randomness of roughness in the two perpendicular directions under a given roughness profile. Second, a shell-type rough pavement is established for directly using as the bridge pavement or attaching to the bridge deck, which minimizes weight increment in the bridge.

Novel recursive formula for removing damping distortion effect on bridge mode shape restoration using a two-axle scanning vehicle

Mode shapes recovered for bridges are often distorted by bridge damping when using the vehicle scanning method (VSM). To remedy such an effect, a novel recursive formula utilizing the spatial correlation between the front and rear contact points of a two-axle scanning vehicle is theoretically established. For a bridge subjected to a two-axle moving test vehicle, the dynamic response of the system is first derived in closed form. The vehicle-bridge contact response is used as a substitute for the vehicle response in signal processing, since it is free of the masking effect posed by vehicle frequencies. Using either the Hilbert transform (HT) or wavelet transform (WT), instantaneous amplitudes are extracted for the component response of the bridge of concern. Further, recursive formula that accounts for the spatial correlation between the two contact points is established to recover the undistorted bridge mode shapes. The following are the conclusions: (1) The distortion effect of damping on bridge mode shapes can be effectively removed by the recursive formula against various factors; (2) the WT is more effective in recovering undistorted bridge mode shapes than the HT; and (3) the proposed formula works well for restoring the mode shapes of bridges with rough pavement, by adding an accompanying truck during the vehicle scanning.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

Dynamic shakedown behaviors of flexible pavement overlying saturated ground under moving traffic load considering effect of pavement roughness

This paper presents a novel semi-analytical framework to dynamic shakedown behaviors of flexible pavement overlying saturated layered ground under moving traffic load. Distinctively, the framework accounts for the influence of dynamic component of vehicle load caused by pavement roughness on the dynamic responses and the shakedown limit analysis. By applying the Fourier transform technique and the robust global matrix method (GMM), the problem considered is formulated as a comprehensive matrix equation. This matrix equation integrates the boundary conditions, the continuity conditions at the interfaces, and the general solution to a single-layered saturated porous medium. By converting the solution to the real-time domain via the composite Gauss-Legendre integral method, the dynamic shakedown limit of pavement overlying layered half-space ground is determined using Melan’s lower-bound shakedown theorem. After verifying the present solution with two well-established solutions using other solution techniques, parametric analyses are performed to explore the dynamic responses of the layered ground at varying traffic load speeds. Special attention is paid to the effects the subsoil stiffness, traffic load moving speed, pavement friction coefficient, and pavement roughness on the dynamic shakedown limit. This newly proposed framework holds promise for designing layered road structures, factoring in dynamic responses and dynamic shakedown limits.

Fatigue study on improved composite box girder bridge with corrugated steel webs

The purpose of the study is to estimate the fatigue behavior of an improved composite box girder bridge with corrugated steel webs (ICBGBCSW) based on a three-dimensional vehicle-bridge coupled vibration system (VBCVS). In the VBCVS, the benchmark of the ICBGBCSW widely used in the highway bridges of Gansu Province of China was adopted as the bridge model, and a 6-axle fatigue load model established based on the traffic data of China was adopted as the fatigue vehicle loading. Based on the established VBCVS, the critical fatigue details of the ICBGBCSW were determined and the effects of pavement roughness condition (PRC) and vehicle speed on the stress ranges experienced by critical fatigue details were first investigated. Then, the fatigue live of critical fatigue details were evaluated based on the deterministic analysis method and reliability analysis method. The results show that the fatigue life of the ICBGBCSW can reach more than 375 years from a deterministic perspective under the current traffic conditions of Gansu Province of China and that the fatigue reliability index of the ICBGBCSW sharply decreases to the target fatigue reliability index of 2 if the PRC stays in the “Poor” class and the “Very poor” class for eight years and two years, respectively. The results obtained in the study can provide a technique support for the wide application of the ICBGBCSW and provide a reference for the bridge management agency to make plans for bridge pavement maintenance.

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.

Pavement Sections’ Reliability Based on Deterioration Model Using Artificial Neural Network (ANN)

Pavement distresses, such as cracks and ruts, reduce pavements’ effectiveness and serviceability and can lead to failure. This underlines the importance of predicting pavements’ deterioration in pavement management systems (PMSs) for effective maintenance and rehabilitation (M&R) strategies. Consequently, it is essential to understand the concept of service life, which represents how long a pavement will remain in service based on how reliable it is. This study introduces a pavement deterioration model using data from the Long-Term Pavement Performance program for the international roughness index (IRI) and other factors. Different machine learning methods were utilized in developing the model to incorporate eight factors that significantly affect pavement roughness; these methods are: linear regression, regression tree, Gaussian Process Regression (GPR), Support Vector Machine (SVM), Ensemble Trees, and Artificial Neural Network (ANN). For comparison, the models’ performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R squared (R2). The weights and biases of the best model and the Federal Highway Administration (FHWA) recommended IRI ranges were utilized to create the limit state function. A reliability analysis using Monte Carlo Simulation (MCS) was determined to calculate the sections’ probability of failure. This study concluded that pavement sections in the US and Canada are reliable and that the mean yearly Kilo Equivalent Single Axle Load (KESAL) significantly contributes to pavement failure. Keywords: Pavement deterioration, IRI, Machine learning, Neural network, Pavement reliability, Probability of failure.

Time-Delay Estimation Based on an Enhanced Modified MUSIC With Co-Prime Frequency Sampling for Rough Pavement

Time-Delay Estimation Based on an Enhanced Modified MUSIC With Co-Prime Frequency Sampling for Rough Pavement

Adaptive maintenance strategies to mitigate climate change impacts on asphalt pavements

This study aims to explore the potential of optimization-based maintenance strategies in adapting asphalt pavements to future climate change. Based on a highway network in Jiangsu, China, the impacts of climate change, characterized by global warming and intensified precipitation, on pavement life cycle cost (LCC) and performance were quantitively assessed, and the benefits of maintenance optimization in mitigating climate change impacts were examined. The findings indicate that climate change may increase pavement rutting depth and reduce pavement roughness and skid resistance, while its effect on transverse cracking varies over time. Adjusting the maintenance schedules, but still following the threshold-based approach, would increase the LCC by about 15.5 %∼19.1 %. The optimization-based maintenance decision-making model significantly mitigates climate change impacts, ultimately even saving 0.6 % of LCCs compared to the baseline. The outcomes will provide a quantitative understanding of the climate change impacts on asphalt pavements, as well as adaptive maintenance strategies to improve pavement resilience.