Pavement Sections Research Articles

Numerical simulation of the stiffness evolution with curing of pavement sections rehabilitated using cold in-place recycling technology

Cold in-place recycling (CIR) technologies are becoming one of the main bets in the road sector to promote the reduction of greenhouse gas emissions. This technique also contributes to the circular economy, reusing 100% of the RAP from worn roads. In this research, numerical simulations of CIR sections are presented. The nonlinear behaviour of the CIR-base material is modelled using three predictive models based on triaxial test results. Variations in the performance depending on the type of subbase, the curing of the CIR-base material and its thickness, and the effect of the wearing-course were analysed. The response of the sections with unbound granular subbase proved to be very sensitive to variations in the parameters studied, and the increase in CIR base thickness was beneficial, while the opposite occurred with a cement-treated subbase.

Effect of Mill and Thin Overlays on Roughness for Pavements in Cold Climate Locations

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

Warm Mix Asphalt Demonstration Projects in Louisiana: Case Study of Five to Eight Years of Field Performance

Warm mix asphalt (WMA) technology has gained popularity in recent years because of its ability to enhance mixture compaction at relatively low temperatures and to improve environmental sustainability. The role of WMA in sustainable pavement technology means that there is a need for comprehensive research in relation to its field performance. The objective of this study was to evaluate the field performance of WMA pavement sections that have been in service for five to eight years. Four rehabilitation projects were evaluated throughout the State of Louisiana. The study included field and laboratory experiments. In the field experiment, WMA test sections were evaluated along with companion conventional hot mix asphalt (HMA) mixture sections on each project. Pavement performance indicators (rutting, roughness, and cracking) from the Louisiana Pavement Management System (PMS) were analyzed. The laboratory experiment consisted of evaluation of mixtures from each project to determine mechanical performance indicators such as loaded wheel tracking (LWT) rut depth, flow number, semi-circular bend energy release rate (SCB Jc), and dissipated creep strain energy (DCSE). An analysis was conducted to rank the capability of laboratory mechanical performance properties to predict field performance. Generally, WMA test sections were found to exhibit similar or better rutting and cracking performance compared with their companion control HMA sections. In addition, the LWT rut depth was found to be a better indicator of field rutting performance than the flow number parameter. It is worth noting that the SCB Jc parameter was found to correctly rank more field cracking indicators than the DCSE parameter and therefore it may be a better indicator of field cracking performance.

Correlation of Pavement Distress and Roughness Measurement

Riyadh City established and implemented a Pavement Maintenance Management System (PMMS) through the General Directorate of Maintenance and operation. The system was created to address the difficulties that come with maintaining and reserving the pavement network. To evaluate pavement conditions, Riyadh (PMMS) uses visual checks, structural capacity roughness, and skid resistance. An Urban Distress Index (UDI) is calculated during the visual assessment process. Distressed pavement types, severity, and quantity are taken into account when calculating UDI values. As a result, the procedure gathers extensive data on the pavement’s condition. However, the procedure is time-consuming and very costly. The Automatic Road Analyzer car provides data on road roughness in accordance with the International Roughness Index (IRI). The IRI data are often generated quite quickly and at a cheap cost as compared to the distress survey. This study’s aim is to examine whether a sample of Riyadh city pavement sections can be connected to the IRI depending on the distress type. The research develops statistical models that correlate IRI values with several distress-types associated with roadway classes. Correlating the International Roughness Index values to distress type will eliminate the necessity to implement the manual inspection at a network- level. This saves money and time for PMMS employees when preparing annual maintenance requirements and setting priorities. The finding of the study, of the relationship between the pavement distresses and the International Roughness Index showed a statistically significant relationship between pavement roughness and some ride-quality distresses, like depression and patching, as well as some non-ride quality distresses like potholes and rutting. In addition, for both main and secondary streets, an analysis of variance shows the existence of a correlation between the two variables.

Field investigation and numerical analysis of an inverted pavement system in Tennessee, USA

Inverted pavement system has been introduced for many years with field investigations, laboratory studies and numerical simulation works. Field investigations of inverted pavement structures have not been widely conducted and are very limited in the USA. This study presents a comprehensive field study on a full-scale inverted pavement as well as a conventional control pavement section constructed in Tennessee, USA. A combination of multiple non-destructive testing (NDT) methods, including ground penetration radar (GPR), Benkelman beam test, 3D road profiling test by laser crack measurement system (LCMS), and falling weight deflectometer (FWD) tests were utilized to assess the actual thickness, structural capacity and surface conditions of the pavement structures. The road surface profiling test results showed that the inverted pavement outperformed the conventional section in roughness, cracking and rutting conditions. Deflection Basin Parameters (DBPs) such as surface curvature index (SCI), base damage index (BDI), base curvature index (BCI) and W7 (7th sensor’s value of FWD) values were compared to study the layers’ condition of the inverted and conventional pavement sections. In addition, a preliminary simulation analysis by a Finite Element Method (FEM) model was conducted to compare the performance of inverted and conventional pavement sections under FWD load. The simulation results showed that the inverted pavement structure could reduce the potential of crack initiation and propagation due to smaller tensile stress at the bottom of the asphalt concrete layer (AC). Also, the smaller deflection was measured at the top of subgrade soil (SG), which reduced the total deflection of the inverted pavement structure. Furthermore, the cement-treated base (CTB)-constrained unbound aggregate base (UAB) acted as a cushion to relieve the tension from CTB and support the AC layer. The stress-dependent property of UAB in the inverted pavement contributed to the performance and ductility of inverted pavement but had little influence on the conventional pavement.

Impact of the Great Flood of 2016 on the Asphaltic Concrete Road Infrastructure in Louisiana

Extreme weather events, including inundation, are the major causes of weather-related disruption to the transportation sector. Considerable research has been conducted in recent years to evaluate the impacts of inundation on pavement functional and structural conditions. Yet, limited studies quantified how inundation may affect the long-term pavement performance and pavement life-cycle costs. As such, the main objective of this study was to quantify the impact of the 2016 inundation event in Louisiana, U.S., on the long-term pavement performance (in relation to reduction in the pavement service life [PSL]) and on pavement life-cycle costs. To achieve this objective, a total of 10 inundated pavement sections were evaluated. Pavement conditions, fatigue, and rutting indices for these sections were analyzed over a monitoring period of up to 15 years (11 years before and 4 years after inundation). Results indicated that the 2016 inundation event reduced the PSL of the inundated sections to an extent that depended on their pre-flooding pavement condition, traffic level, and asphalt concrete thickness. This reduction was attributed to the accelerated fatigue, rutting damage, or both, that resulted from prolonged water submergence and heavy traffic loading during debris hauling that took place after the inundation event. A logistic regression model with adequate accuracy was developed to predict whether an inundated pavement would experience reduction in its PSL based on the project conditions. The study findings were incorporated into a simple tool to assist transportation agencies make effective decisions for the maintenance and rehabilitation of inundated pavements.

Prioritization of Flexible Pavement Sections for Maintenance Using Multi-criteria FAHP Integrated with Multi-attribute Utility Theory

Prioritization of Flexible Pavement Sections for Maintenance Using Multi-criteria FAHP Integrated with Multi-attribute Utility Theory

Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos

Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.

MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK

The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.

Introducing mathematical modeling to estimate pavement quality index of flexible pavements based on genetic algorithm and artificial neural networks

This study explored the outcomes of utilizing genetic algorithms and artificial neural networks to assess the pavement quality index on the principal road by analyzing 500 flexible pavement sections in Amman, Jordan. Pavement sections are selected in areas that are exposed to many variables, such as traffic, pavement materials, and different climatic zones. Pavement deterioration is determined by a number of factors, including cumulative equivalent single axle load, pavement structure, and material properties. The study aims to develop a performance model of PQI based on using surface rating (SR), present serviceability rating (PSR), and pavement age. Several techniques were used to propose the PQI model, such as multiple linear regression, genetic algorithm, and artificial neural network. Multiple linear regression showed that PSR and SR had a statistically significant influence only on PQI (P = 0.0001). However, age is less significant on PQI (p = 0.506). The genetic algorithm and the artificial neural network techniques were applied to propose two PQI models with an R2 value for the training model of 0.98 and 0.94, respectively. The study results show means that the genetic algorithm model performs better than the neural network.

Factors Contributing to Fatigue Cracking in Flexible Pavements in Oklahoma: A Case Study Using Laboratory and Field Investigation and AASHTOWare Simulation

Abstract Fatigue cracking is one of the major structural distresses in flexible pavements. In this study, probable causes of fatigue cracking were investigated using field and laboratory testing and In this study, probable causes of fatigue cracking were investigated using field ang laboratory testing and AASHTOWare Pavement ME Design (PMED) simulations. Field tests including Ground Penetrating Radar (GPR), Falling Weight Deflectometer (FWD), and Dynamic Cone Penetration (DCP) were performed on a flexible pavement section of US 412 located in Noble County, Oklahoma. Also, asphalt cores and soil samples were collected for laboratory testing. The GPR test results revealed significant delamination in the asphalt layer. Also, the GPR images indicated that the disturbance zone was confined within the asphalt layer and cracks were generated from surface as well as from existing pavement layers below. The DCP and FWD test results indicated that the pavement section was not structurally adequate to support traffic and needed rehabilitation in the near future. The moduli of the asphalt layers were found to be quite low, indicating improper compaction during construction. Also, the densities of the top-lifts of the asphalt cores were found to be low. Moreover, the cracking resistance of the extracted asphalt cores was poor based on the Illinois flexibility index test results. Superpave Performance Grade of the extracted binder indicated excessive aging of the binder because of long exposure to the environment. The brittleness of mix resulting from aging was considered a potential contributor to fatigue cracking of the pavement at this site. A parametric study was conducted to understand the variation of fatigue cracking with the changes in input properties in PMED, namely pavement structural components and material properties. Pavement thickness, roadway densities, and layer moduli of existing underlying pavement were found as the most influential factors. The findings of the parametric study supported the findings of the field and laboratory investigation.

Assessment of Performance and Maintenance of Flexible Pavement Using KENLAYER and HDM-4

This paper describes the performance of flexible pavement using the KENLAYER and Highway Development and Management (HDM-4) software for urban roads. This study includes the study area of four-lane and two-lane roads at Hyderabad city, such as Gandi Maissama to Bachupally (GB), Bachupally to Nizampet (BN), Miyapur to Bachuapally (MB), JNTU to Pragathi Nagar (JP). The distress data like rutting, fatigue, pothole, patches, and edge drop data of each roads section were taken by adopting the manual methods. The pavement deterioration model was selected and developed based on the available distress using both the software KENLAYER and HDM-4. The current study aims to forecast the performance of specific roads in order to calculate the damage ratio. It also provides the design life of the roads as per the current and future traffic based on the distress model. Further, the maintenance of the particular roads is carried out using the HDM-4 models. It is also noticed that major damage was found in Gandi Maissama to Bachupally than the other road section. This study concluded that the comparative study of different roads section and governing factors for distress along with the allowable limits of distress in each section of the flexible pavement were found. Finally, the output of KENLAYER and HDM-4 has been compared.

Field calibration and validation of a pavement aging model

ABSTRACT A pavement aging model (PAM) is established by calibrating and validating the NCHRP 09–54 kinetics model's predictions against field core measurements. Field cores, laboratory-mixed loose mixtures, and binders from a wide range of pavement sections are used, including both conventional hot mix asphalt (HMA) mixtures and modern materials (i.e. reclaimed asphalt pavement (RAP), warm mix asphalt (WMA), and polymer modified asphalt (PMA)). Field aging levels are measured at different depths from binder extracted and recovered from in-service field cores. The original component materials of these pavement sections are aged in the laboratory and used to calibrate the kinetics model. The measured field aging levels are compared against those predicted from the kinetics model to inform a depth and time dependent field calibration function for the PAM. The validation of PAM using an independent set of field sections shows that, within the limited data used in this study, the calibrated PAM may be able to predict the aging of the conventional HMA, RAP, WMA, and PMA mixtures. The PAM predictions are lastly found to outmatch the predictions by the Global Aging System (GAS) model.

Impact of Autonomous and Human-Driven Trucks on Flexible Pavement Design

Truck platooning with autonomous and connected vehicles has several advantages compared with traditional trucking. Platooning improves overall road safety and reduces fuel consumption by up to 15%. This is a result of the advanced control systems and high steering accuracy of autonomous vehicles. These advanced control systems present an opportunity to pavement design engineers, as the lateral positions of the vehicles could be altered to create less damaging loading scenarios. This study introduces an expected response framework to quantify the impact of lateral position on pavement performance. Using the expected response framework, any mixture of human-driven and autonomous vehicles can be analyzed by characterizing lane position as a mixture probability distribution instead of point loads. Pavement damage can subsequently be computed by using the expectation of the responses. This approach requires little computational effort and is easily incorporated in any mechanistic–empirical design or optimization framework. The approach is illustrated by analyzing four flexible pavement sections using the expected response framework. Compared with human-driven trucks, optimized lateral position could decrease pavement damage by 40%. Channelized traffic on the other hand could increase pavement damage by 60%. A simplified approach is introduced alongside a reliability analysis and fragility curves for various pavement structures. Distributed traffic was found to have the lowest probability of failure among all traffic scenarios.

Effects of flooding on pavement performance: a machine learning-based network-level assessment

ABSTRACT Catastrophic flood and hurricane events cause enormous adversarial impacts on roadways and their long-term performance. Despite the abundance of research attempting to understand the flood-induced damages, a critical knowledge gap in assessing roadway flood damage at a network-level exists. This study develops a holistic assessment model that evaluates network-level flood damage of roadways based on historic pavement distress data along with historic flood data. A rich volume of high-confidence historical pavement distress data was obtained from the Louisiana pavement management system. After a rigorous data pre-processing process by cross-referencing the flooded areas using the 2016 Louisiana flood map data, it was leveraged to analyze how flooding could interact with the pavement distress, thus affecting the overall performance of existing pavements. The study outcomes showed that the most flood-affected distress types include roughness and random cracking. Based on the findings from the analysis, this study developed a machine learning-based prediction method that can calculate future pavement performance after a flood event. After applying different algorithms for creating the prediction model, the eXtreme Gradient Boosting (XGB) classifier was selected because it represented the highest accuracy among other examined classifiers. Various datasets and scenarios were investigated with the developed prediction model to identify the most effective features and dataset combinations. The prediction model is expected to identify vulnerable pavement sections and facilitate network-level preventive maintenance of pavement to mitigate future flooding impacts by prioritizing resource allocations for maintenance and rehabilitation after a flood event.

Using Ground Penetrating Radar to Monitor Seasonal Moisture Fluctuations in Base Layers of Existing Roads

A high moisture content in the unbound aggregate layers of pavements can lead to severe functional and structural damage, especially in pavement constructed in freeze-thaw environments, such as Minnesota. In these climates, the freezing temperatures can contribute to the formation of severe crack-heaving issues, and the early spring-thaw conditions can significantly reduce a pavement’s bearing capacity. A good understanding of subsurface seasonal moisture fluctuation is, therefore, essential for implementing effective maintenance and road load management techniques. Unfortunately, traditional pavement moisture testing approaches are expensive, time-consuming, invasive, limited in spatial coverage, and negatively affect the traffic flow. The present study builds on previous efforts to characterize moisture and frost conditions in pavement base layers using ground penetrating radar (GPR) and moisture sensing instruments. The paper offers a simple methodology for monitoring moisture variations in the base layer and assessing the moisture susceptibility of aggregate base materials from single-offset GPR measurements. The methodology was validated by comparing the GPR data with moisture content readings collected from four different pavement test sections over a period of approximately two years. Overall, the moisture contents obtained from the GPR were comparable with those obtained from sensors installed in the pavement section. However, the GPR approach offered the added advantage of covering long stretches of pavement in short times and with minimal impact on the pavement and traffic.

Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks

This article presents a novel system reliability-based framework for multi-objective optimisation of preventive maintenance (PM) management of in-service asphalt pavement. To accurately predict the international roughness index (IRI) sequence of pavement sections, a long short-term memory (LSTM) neural network that considers the spatiotemporal correlations between IRI sequences is trained with data retrieved from the long-term pavement performance (LTPP) program. Based on time-dependent limit-state functions (LSFs) incorporating the uncertainty associated with LSTM neural network prediction and the observational error involved in IRI measurement, Monte Carlo simulation (MCS) with importance sampling (IS) is adopted to calculate the reliability of pavement sections. Pavement sections located in New Mexico and Montana are selected as illustrative examples. Tri-objective optimisation processes are investigated by maximising user benefits (i.e. improved system reliability) and agency benefits (i.e. extended service life) while minimising the associated life-cycle cost (LCC) (i.e. user and agency costs) with multi-objective genetic algorithms (GAs). The obtained Pareto solution sets may assist decision-makers in the selection of well-balanced solutions to identify the optimal timing for applying PM treatments to in-service asphalt pavement.

Effect of Alkali-Resistant Glass Fibers and Cement on the Geotechnical Properties of Highly Expansive Soil

The main objective of this study was to evaluate the use of glass fibers and cement as stabilizing agents for highly expansive soil. Fibers with two aspect ratios (12 and 30 mm in length) were added as percentages of the soil’s dry weight (0.5%, 0.75%, 1%, and 1.5%), the cement was added in two percentages by dry weight soil (2% and 6%). Sixteen groups of treated soil specimens were prepared and subjected to laboratory tests—unconfined compressive strength (UCS) after curing (0, 7, 14, and 28 days), swelling percentage, and California bearing ratio (CBR). Interactions at the interface between the fiber surface and the soil matrix were analyzed by scanning electron microscopy (SEM). Also evaluated was effect of fibers on the design and performance of cement-stabilized subgrade layers according to the Mechanistic-Empirical Pavement Design Guide (MEPDG) to compare the performance of flexible pavement sections modeled using default values and laboratory-established soil properties for inputs. Results indicated increases in UCS and soaked CBR and decreases in swelling tendency. The 6% cement/1.5% short fiber mixture resulted in a significant improvement in expansive soil properties over other mixtures.

The Effect of Temperature, Rest Periods and Ageing on the Response of Bituminous Materials in Fatigue Tests: Considerations and Proposals on Analytical Dimensioning Models.

One of the basic assumptions of analytical dimensioning models of asphalt pavements is failure due to fatigue cracking of the bituminous layers. Furthermore, it is considered that the damaging effects of different traffic loads are linear and cumulative, per Miner’s law. However, the analysis carried out on the effect of temperature, rest periods, and ageing of the bituminous materials questions considering fatigue failure as the only and main assumption for the calculation of the pavement life. Ageing of the pavement asphalt layers results in stiffening and transverse cracking. Consequently, these asphalt layers are no longer of infinite extent in the horizontal direction and their response resembles that of a slab. The application of this last calculation assumption provides pavement sections more in line with those used in Spain in the Catalogue of Structural Sections, which is based on experience gained from the real behavior of those sections. The calculation based on the fatigue laws results in undersized structures. This paper shows the results of a strain sweep test implemented at the UPC Roads Laboratory, which was used to analyse all the aforementioned effects and to propose a calculation procedure for heavy traffic pavements considering transverse cracking of the asphalt layers.

Pavement maintenance and rehabilitation budget allocation considering multiple objectives and multiple stakeholders

ABSTRACT Highways play an important role in economic and social development at both national and regional levels. As a major component of highway infrastructure, pavements require significant funding to be maintained at an acceptable level of service. Allocating the budget for pavement maintenance and rehabilitation (M&R) is a complex decision-making process for a highway agency. This allocation of funding across M&R activities and across districts becomes a challenging process with the presence of multiple stakeholders, constrained budgets, customer satisfaction, agencies' strategic goals, and deteriorating pavement conditions. This paper proposes an extended goal-programming framework for allocating the available pavement M&R budget across multiple management districts of a highway agency over its planning horizon. A case study of applying the proposed model is conducted using data from the Texas Department of Transportation (TxDOT), where five objectives are considered: maximise the proportion of pavement sections with best conditions, minimise the proportion of pavement sections with worst conditions, minimise crash severities through pavement M&R, minimise energy consumption due to M&R activities, and maximise the total lane mile of pavement sections receiving M&R treatments. A sensitivity analysis is also conducted to examine the effect of different parameter values on the results of budget allocation.