Pavement Rutting Research Articles

Assessing braking performance on wet-road through water-depth estimation and vehicle-pavement dynamic simulation

ABSTRACT The presence of water on wet roads due to rainfall has emerged as a critical factor influencing driving safety. During severe weather, the non-uniform water distribution on the road can significantly diminish pavement friction, elevating the risk of traffic accident. Furthermore, the tire-pavement friction coefficient varies with vehicle velocity, rendering wet braking analysis complex. Conventional braking risk analysis often assumes a fixed friction coefficient, neglecting the impact of uneven pavement-induced water depth irregularities. This paper introduces a novel braking performance assessment method based on water-depth estimation and vehicle-pavement simulation. The uneven water-depth distribution is first estimated using LiDAR-measured pavement geometry. A co-simulation framework is then proposed to analyze the tire-pavement friction and study the dynamic braking performance. The 85th percentile stopping distance is adopted as the evaluation index for quantifying braking risk. Results from case studies highlight the substantial influence of rainfall intensity and vehicle velocity on braking risk. Additionally, pavement rutting accumulates deeper water depth, thereby elevating braking safety risks. The proposed model offers a novel perspective on vehicle braking risk analysis and can serve as a valuable safety indicator for highway transportation management and decision-making in driving strategies during wet weather conditions.

Development and Validation of Asphalt Pavement Rutting Prediction Model with Transient Temperature Field Considered

This study developed a transient rutting analysis program based on the principles of heat transfer and the viscoelastic–viscoplastic theory of asphalt mixtures. Using the finite element model (FEM) model corresponding to the multi-layer pavement structure and the subprogram interface of ABAQUS software, along with Python programming, the program was designed to estimate rutting under transient temperature field conditions with climate conditions, traffic flow, and structure layer constitutive parameters of pavement considered. Subsequently, the program was used to validate the efficiency of the solution and analyze the influencing factors of internal temperature field and rutting depth within the asphalt pavement structure. Results showed that, based on meteorological data and recommended values of material thermal properties, the FEM of transient temperature field can accurately simulate the temperature field distribution of pavement structure. Based on loading information and dynamic modulus test, the transient temperature variable rutting with FEM proposed in this paper can simulate and calculate the rutting of pavement structure layer with some errors for different pavement conditions from 5% to 40%. Through parametric analysis of the influence factors of temperature field and rutting, the modified transient temperature field rutting prediction program can effectively simulate rutting of pavement structure, and the minor errors were less than 20%.

Rutting prediction model of asphalt pavement based on RIOHTrack full-scale ring road

The research and prediction of rutting mechanism of asphalt pavement has always been the main difficult problem for road designers all over the world, and scholars have done a lot of research on how to prevent and control rutting of asphalt pavement. Based on the measured loading data of 80 million axis times of Beijing RIOHTrack full-scale ring road, rutting evolution of asphalt pavement with various structure types is studied in this paper and proposes the Kou&Cao rutting prediction model for the first time. Compared with other mature explicit rutting prediction models, this model has the characteristics of high prediction accuracy, strong robustness. At the same time, the model can provide a basis for predicting the inflection point of rutting evolution on asphalt pavement. The research results of this model are effective, which completes the effective analysis of the rutting evolution and development law of asphalt pavement, and provides an important reference for the future road design specification research and road construction.

Pavement performance and model studies with modified bitumen binder

Flexible pavements experience premature deterioration due to several factors, such as high volumes of traffic, repeated wheel loads, overloading, adverse weather conditions, inferior quality of materials, and other related issues. One of the most commonly found types of distress is rutting, which severely affects pavement performance; hence, the prediction of rutting behaviour is crucial. Research has been done by adding various industrial wastes, chemicals, additives, and modifiers such as rubber, natural rubber, polymers, and waste plastic into the bitumen. These materials are expected to have a substantial impact on enhancing the strength, durability, and performance of pavement while simultaneously reducing the cost of construction. The present research deals with a laboratory investigation of bituminous mixes using both Marshall stability and rutting tests. The performance tests were carried out by Roller Compactor cum Rut Analyzer (RCRA) equipment and Finite Element (FE) analysis in ABAQUS. The experimental findings show that the modified bitumen exhibits 48% greater strength and 20% more resistance to rutting than the conventional bituminous mixes. The model analysis was successfully validated by experimental results. The findings of the research resulted in the development of a mathematical equation that helps to calculate in-service pavement rut depth.

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Element Method and Machine Learning Models

Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Element Method and Machine Learning Models

Gradation Optimization Based on Micro-Analysis of Rutting Behavior of Asphalt Mixture

This study investigates the microscopic mechanism of the force on particles of different particle sizes in the asphalt mixture during rutting formation. The gradation was optimized by analyzing the particle force results. The enhanced discrete element method (EDEM) was used to simulate the rutting test, study the correlation state between different particle sizes in the rutting process, and analyze the rutting of asphalt pavement from the aggregate level. From a microscopic perspective, the specific forces acting on particles at different times were determined to investigate the particle size range of stressed particles in two types of asphalt mixtures. Furthermore, the role of particles with different sizes in the rutting process was analyzed. The force limit values of particles with different particle sizes are fitted, and the force of particles in two types of asphalt mixtures is compared and analyzed. After that, the gradation of the asphalt mixture is optimized, and the feasibility of the gradation optimization method is verified by laboratory experiments. The results show that the change rule of the rutting simulation test is gradually transformed from compacted rutting to unstable rutting. The force of the asphalt concrete-13 (AC-13) asphalt mixture is borne by the particles with a radius greater than 1.8 mm. The force of the stone matrix asphalt-13 (SMA-13) asphalt mixture is borne by the particles with a radius greater than 3.6 mm, and the small particle size particles play a filling role. When the particle radius is less than 5.1 mm, the force value of AC-13 asphalt mixture particles is greater than that of SMA-13. When the particle radius exceeds 5.1 mm, the force value of SMA-13 asphalt mixture particles is greater than that of AC-13. The force of particles with a radius of 5.7 mm and 7.3 mm in the SMA-13 asphalt mixture is 30% higher than that in AC-13, and the force limit of particles is proportional to the particle size. The dynamic stability, flexural tensile strength, water immersion residual stability, and freeze-thaw splitting strength ratios of the optimized asphalt mixture are improved compared with those before optimization. The AC-13 asphalt mixture is increased by 8.5%, 9.2%, 1.6%, and 1.9%, respectively, and the SMA-13 asphalt mixture is increased by 10.6%, 7.3%, 0.7%, and 2.1%, respectively. It shows that the grading optimization method is feasible.

Regional variations of climate change impacts on asphalt pavement rutting distress

The global climate is currently changing remarkably. Rising temperatures intensify the likelihood of rutting in asphalt pavements. This research, covering seven Chinese provinces from 2021 to 2100, predicts asphalt pavement rutting depth on freeways and secondary roadways for the next 80 years. The study introduced the Rutting Climate Change Index to gauge climate change impact on pavement rutting. It examined regional variations in climate's effect on pavement rutting and associated factors. The results indicate that both longitude and latitude significantly influence the Rutting Climate Change Index for asphalt pavement rutting distress. With increasing longitude, the impact of climate change on rutting distress in asphalt pavement exhibits diverse patterns across different regions. The influence of climate change on rutting distress in asphalt pavement increases with higher latitudes. The regional variations in the impact of climate change on rutting distress in asphalt pavements are more pronounced along the latitude.

A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters.

Predictive Modeling for Highway Pavement Rutting: A Comparative Analysis of Auto-Machine Learning and Structural Equation Models

Highway pavements deteriorate over time as successive wheel loads cause rutting, cracking, texture loss, and so forth. Design standards and pavement performance models account for some of the known contributory factors, such as levels of traffic and vehicle composition. However, such models are limited in their predictive power, and highway authorities must conduct regular pavement condition surveys rather than relying on the standard deterioration models alone. The ways in which multiple factors affect pavement deterioration, including rutting, are complex and are believed to include feedback loops where rutting then influences driving position, exacerbating the rutting levels. Standard regression models are not well suited to representing such complex causal mechanisms. This paper compares two alternative modeling approaches, structural equation models and auto-machine learning, and evaluates the predictive ability and practicalities of each. The findings indicate that auto-machine learning (AutoML) may be superior in its predictive ability. However, the “black box” nature of AutoML results makes them potentially less useful to practitioners. A process of using machine learning to help inform a structural equation model is proposed.

PREDICTING TEMPERATURE DISTRIBUTION IN HANOI REGION'S ASPHALT PAVEMENT THROUGH NUMERICAL SIMULATION

This article presents determining the temperature distribution in asphalt pavement in the Hanoi area based on numerical simulation using FEM. Climate data were collected from freely available online data from the Federal Highway Administration of the United States. From this data, the surface temperature of the pavement was calculated, which served as the boundary condition for the heat transfer problem of the pavement. The results show that the pavement surface temperature is typically highest around 11 a.m. to 12 p.m., while the lowest temperature is typically around 4 a.m. to 6 a.m. The temperature inside the pavement layer is usually high between 10 a.m. and 4 p.m. when the strength and deformation resistance of the pavement are relatively low. In contrast, the temperature inside the pavement is low during the period between midnight and 6 a.m., when the asphalt concrete becomes brittle and is easy to crack. The analysis of the temperature distribution in the pavement structure can be used for calculating and designing the pavement, as well as predicting pavement rutting.

Multi-scale asphalt pavement deformation detection and measurement based on machine learning of full field-of-view digital surface data

Asphalt pavement deformation is a common phenomenon due to the material property and traffic loads. Aiming at ensuring traffic comfort and safety, it is essential to constantly monitor the multi-scale pavement deformation with a non-destructive and automatic system. This paper presents a full field-of-view asphalt pavement deformation inspection framework based on multi-dimensional surface data and machine learning, which can detect and measure pavement rutting, roughness and large-span deformation simultaneously. In this integrated system, one-dimensional convolutional neural network (1D CNN) classification and localization models are developed for two-step rutting detection. The quarter-car model with multiple measuring lines is employed for full-lane international roughness index (IRI) measurement and spatial analysis. The unsupervised K-means convolutional neural network (K-CNN) model is proposed for large-span deformation detection. The results show that the overall F1 scores of rutting classification and localization are 99.56% and 97.24%, respectively. The full-lane measurement suggests that IRI presents a bimodal distribution in transverse space due to the concentrated traffic loads on the wheel path. In addition, the unsupervised K-CNN achieves an average consistency index (CI) of 91.61% on large-span sunken and heave deformation segmentation.

Effect of the Field-Stress State on the Subgrade Resilient Modulus for Pavement Rutting and IRI

The new Mechanistic-Empirical Pavement Design Guide (MEPDG) uses the subgrade resilient modulus (MR) as the key input parameter to represent the subgrade soil behavior for pavement design. The resilient modulus increases with an increase in confining pressure, whereas, for an increase in deviatoric stress, it increases for granular soils and decreases for fine-grained soils. The value of MR is highly stress dependent, with the stress state (i.e., bulk stress) a function of the position of the materials in the pavement structure and applied traffic loading. Applying excessive vertical stress at the top of the subgrade without knowing the appropriate stress state can result in permanent deformation. In situ stress must be calculated so the correct resilient modulus can be determined. To facilitate the implementation of MEPDG, this study develops a methodology to select the appropriate subgrade resilient modulus for predicting rutting and IRI. A comprehensive research methodology was undertaken to study the effect of in situ or undisturbed subgrade MR on pavement performance using the MEPDG. Results show that MR obtained from in situ stress is approximately 1.4 times higher than the MR estimate from NCHRP-285. Thus, the in situ stress significantly affects the calculation of subgrade MR and, subsequently, the use of MR in the predicted rutting, with IRI using the AASHTOWare pavement mechanistic-empirical design. Results also show that the pavement sections were classified as in “Good” and “Fair” conditions for rutting and IRI, respectively, considering in situ MR.

Application of Lightweight Deflectometer to Assess Structural Competency of Nontraditional Airfield Pavements During Contingency Aircraft Operations

This paper discusses the use of the heavy and light weight deflectometer devices, hereafter referred to as HWD and LWD, respectively, to monitor the structural condition of airfield pavements during aircraft operations at contingency environments. Four airfield pavements like those encountered at contingency environments, referred to as nontraditional airfield pavements, were selected to conduct accelerated pavement traffic testing with simulated C-17 and C-130 aircraft. Pavement rut development was monitored, and both HWD and LWD tests were also conducted at selected traffic intervals. Both HWD and LWD were capable of tracking the pavement deterioration under accelerated traffic testing, which indicates that the LWD could be considered a potential rapid and practical pavement evaluation tool for nontraditional pavements during contingency aircraft operations. Based on current rutting failure threshold, a termination criterion in relation to a reduction in the stiffness parameter from the LWD test could be preliminarily used to aid in expediently determining acceptable aircraft operations on nontraditional airfield pavements. The influence of key operational parameters such as base plate dimension, the depth of influence from the impact force, and pavement rut formation must be further investigated to improve the effectiveness and applicability of the LWD test for nontraditional airfield pavements. The results from this study could be of interest to other low-volume road applications such as in agriculture, oilfield, large construction sites, and mining where pavement evaluation and management practices could be improved with the use of practical tools such as the LWD during the operations of heavy equipment or machinery.

Towards an improved prediction of asphalt pavement rutting through thermal-mechanical coupled constitutive modelling

ABSTRACT To predict accurately the evolution of asphalt pavement rutting, an advanced numerical modelling process was proposed and successfully implemented through the finite element software ABAQUS. In the process, a thermal-visco-elastic-plastic constitutive model for asphalt mixture was derived into numerical form and compiled using the user-defined material subroutine (UMAT). To apply realistic traffic loading, a mathematical form of loading model was proposed according to the tire-pavement interaction and realised through the user-defined load subroutine (DLOAD). Besides, a heat transfer model was calculated before the mechanical calculation, and the results of the pavement temperature field were used as the boundary condition for the mechanical calculation. A few case simulations were conducted using the numerical process and the results indicate that the severe rutting at pavement intersections or parking lots is due to not only the longer loading time but also the deeper plastic deformation region. In addition, the numerical model is capable of quantitatively identifying the critical temperature at which the pavement rutting starts to grow rapidly; accordingly, a new criterion for taking strategies to avoid or slow down the rutting distress of the pavement can be proposed, which is of great significance for the practice.

Effect of Moisture Content on Subgrade Soils Resilient Modulus for Predicting Pavement Rutting

The subgrade soil stiffness, which depends on the in-situ moisture content and soil index characteristics, is a key factor in pavement rutting. Due to variations in the compaction process used during construction and seasonal changes, the subgrade soil moisture content may deviate from the desired condition. The resilient modulus (MR), an important parameter of the Mechanistic-Empirical (M-E) pavement design process, is used to specify the subgrade soil stiffness. Repeated load triaxial tests, which can be challenging and time-consuming to execute, are often used to determine MR. As a result, correlations between MR and more accessible stiffness metrics and index qualities are frequently used. California bearing ratio (CBR) and repeated load triaxial tests were carried out in this investigation. Soil specimens were fabricated at moisture levels that were both above and below the optimum moisture content (wopt). The results of the two tests were correlated, and statistical models were created to correlate the parameters of the generalized constitutive resilient modulus model with the characteristics of the soil index. Additionally, utilizing the MR found for subgrade soils compacted at wopt and ±2%wopt, pavement rutting was analyzed for three base layer types. The results demonstrated that a laboratory-measured MR (MR(Lab)) decreases as the moisture content increases. Specimens compacted at −2%wopt showed higher MR(Lab) than specimens compacted at wopt. Specimens compacted at +2%wopt showed lower MR(Lab) than specimens compacted at wopt. Results also indicated that the MR(Lab) predicted higher pavement rutting compared to field measured MR (MR(Lab)). If a stabilized aggregate foundation layer was employed instead of an untreated granular base, subgrade soil moisture condition showed a significant impact on rutting.

Evaluation on Lateral Stability of Vehicle: Impacts of Pavement Rutting, Road Alignment, and Adverse Weather

The combination of pavement rutting, poor road alignment, and extreme adverse weather will seriously threaten the driving safety of vehicles, whereas only a few of these factors are commonly concerned. This study aims to efficiently evaluate the impacts of various driving conditions on the lateral stability of the vehicle and produce a practical recommendation for pavement maintenance in what concerns rutting. A systematic framework was, thus, developed to conduct a comprehensive evaluation of the lateral stability of the vehicle, which incorporates a single-factor test and multi-factor test based on the stability indicators obtained from Carsim simulations. The vehicle road weather model was established in the Carsim software by considering seven factors, including driving speed, width–height ratio (WHR) of rutting sidewall, radius of circular curve, superelevation, crosswind angle, crosswind speed, and friction coefficient, respectively. The results show that the established framework behaves with satisfactory performance, regarding evaluating the effect of various impact factors on the lateral stability of the vehicle while driving across rutting. Stability indicators suddenly fluctuate in a short time, due to the instantaneous wandering behavior of crossing rutting. Additionally, the sudden fluctuation phenomenon is greatly enlarged, and the vehicle is inclined to occur with lateral instability when WHR equals 5, particularly in roll-over instability. It is recommended to concurrently confine the WHR greater than 10 and friction coefficient greater than 0.4, in order to ensuring driving stability. The multi-factor test revealed that the vehicle speed and WHR of the rutting are leading factors that affect driving stability, followed by the radius of circular curve, superelevation, crosswind angle, crosswind speed and friction coefficient, respectively, which are both essential factors for driving stability. The outcomes of this study may contribute to supplying guidelines for controlling key adverse conditions and making decisions on pavement maintenance.

Bitumen stabilized materials as pavement overlay: Laboratory and field study

Bitumen stabilized materials applied as base courses have been used in many countries. In the southern Brazilian State of Rio Grande do Sul, two test sections were built using such mixes (in 2011 and 2015) as overlay asphalt emulsion base courses and have been under monitoring since then. In 2019, a new test section was built to evaluate mixes of granular materials and reclaimed asphalt pavement stabilized with emulsion and Portland cement as overlay base courses. The main objective of the research reported here was to evaluate the mechanical behavior of such materials and to assess their performance in the field during the years after construction. Indirect tensile strength, monotonic triaxial, and repeated load triaxial tests were carried out to obtain the necessary mechanical parameters. The data collected during the monitoring of the test sections were used to analyze the field performance in terms of permanent deformation. Besides, in situ moduli were obtained using backanalysis of deflection basins. The number of equivalent single axle loads (ESALs) for the analysis period was computed using data collected in toll plazas near the test sections. Using field and laboratory results and the South African TG2 performance model, it was possible to estimate the life of the studied pavements regarding permanent deformation. The TG2 model seems to underestimate the life of pavements with thick (>40 mm) asphalt layers. A model relating rutting to the number of ESALs was obtained, allowing the prediction of rutting of asphalt pavements overlaid by a bitumen stabilized base layer and asphalt wearing courses. Considering the test sections’ performances, the mixes proved to be efficient to improve structural capacity, also reducing the influence of the water level variation on pavement performance.

Calibration of Inverted Asphalt Pavement Rut Prediction Model, Based on Full-Scale Accelerated Pavement Testing.

This study investigates the establishment and calibration method of the rut depth (RD) prediction model of inverted asphalt pavements (IAPs), based on full-scale accelerated pavement testing (APT), which facilitates the accurate and reliable design or maintenance of IAPs. A power function is adopted for the prediction model construction of the rut progression before the failure stage, based on the typical permanent deformation progression curve of flexible pavements. The APT loading history is divided into units, according to the difference in physical conditions, providing the basis for a cumulative RD analysis and model calibration. The nonlinear incremental recursive (IR) principle is applied in the RD analysis to consider the influence of the nonlinear material property, performance deterioration, and loading history on the RD development. Further, the rut shift function relating prediction models obtained from laboratory tests and full-scale APT is established to introduce the APT data in the calibration process. Accordingly, the mechanistic-empirical RD prediction model calibration method, based on APT and the IR principle, is proposed and applied in a case study of a IAP RD prediction model calibration. Four 3.5 m × 4 m IAP test sections S1-S4 are constructed and instrumented and 700,000- and 900,000-wheel loads are applied on test sections S1-S2 and S3-S4, respectively, using the heavy vehicle simulator. The test data from the different APT load units are utilized for the model calibration, and the resultant prediction errors range from -2.16 mm to 1.18 mm. The calibrated model can also be used for the RD prediction of IAPs with other design schemes, by updating the corresponding material-related coefficients and the finite element model, which is essential for the design and maintenance of IAPs. The proposed calibration method could be a useful reference for the establishment of flexible pavement performance prediction models.