Journal of Transportation Engineering, Part B: Pavements

Compaction quality control/assurance of unbound geomaterials is one of the crucial components in pavement and embankment construction to ensure their performance, stability, and sustainability. Conventional density-based methods such as nuclear density gauge to determine the compaction quality have been widely used due to the straightforward relationship between the readings and targeted material property. Recent modifications in construction standards and the introduction of the Mechanistic-Empirical Pavement Design Guide have inspired a growing interest in developing and implementing strength/stiffness-based compaction control quality assurance (QA) specifications. Numerous studies have been dedicated to investigating the efficiency and effectiveness of the stiffness-based compaction QA tools. This paper presents a comprehensive review of the recent compaction QA relevant literature and surveys. Findings of different approaches for studying QA devices, and the main results of the existing models, experiments, and engineering practices were summarized. Several in situ spot QA technologies, including the latest compaction QA technologies [e.g., the lightweight deflectometer (LWD)], were highlighted, and their efficiency and effectiveness were compared. The review also summarized the intercorrelations between different devices, the correlations between in situ QA device readings and mechanical properties of unbound material, findings of the numerical simulations, and case studies and current practices using different QA tools. The recommendations for future research needs and practical implementations were identified and discussed.

Delivering durable asphalt concrete within a reasonable cost is one of the great ambitions of pavement material engineers. This state-of-the-art review article documents the efforts spent in the past two decades to ensure the durability and performance of asphalt mixtures in mix design and production. A perspective with the attempt to integrate laboratory mix design, plant production quality assurance, and field place acceptance is applied in the review. The development of the performance specification and performance mix design is summarized. The paper categorizes performance specification into index-based performance specification and predictive performance specification that include performance-related specification and performance-based specification. The approaches to developing index-based performance mix design/balanced mix design and predictive performance mix design are also compared in the review. The challenges and solutions in incorporating performance tests in asphalt productions are documented and discussed. The challenges include selecting performance testing methods, determining index threshold limits, estimating and incorporating testing variability and uncertainty, determining sampling position and testing frequency, and so forth. The paper also provides suggested areas of future research and implementation activities.

Unmanned driving is the direction of future development in the field of transportation. The development of unmanned driving will cause sensor monitoring and machine control to play a greater role. According to road roughness data collected by unmanned ground vehicles and vehicle dynamics, it is of great significance to establish a vehicle–pavement coupling model to analyze and guide driving comfort, cargo safety, and road friendliness. Pavement roughness is the main external excitation in the process of driving a vehicle. Therefore, a three-dimensional (3D) model of pavement roughness was constructed based on fractal theory and fractal interpolation. According to tire–pavement contact characteristics, a newly rerevised 3D flexible roller contact tire model was proposed. On this basis, vehicle vibration dynamics models for a car and a truck were established. The traditional point contact model was used to verify the model. Based on the comfort threshold of a passenger, the cargo vibration threshold, and the dynamic load coefficient (DLC) threshold of the truck, the conditions for ride comfort of an unmanned ground vehicle were expounded. The results showed that, compared with an ideal fully flat road surface, pavement roughness had a great impact on vehicle vibration. The 3D model of pavement roughness generated had high accuracy. The vehicle–pavement coupling model based on tire–pavement 3D contact under 3D pavement roughness excitation was correct. The proposed model can calculate vibration characteristics of passengers and cargo and the DLC of wheels in real time. For different types of vehicles and different roughness levels of pavement, different speed thresholds were given to ensure driving comfort, cargo safety, and road friendliness. Driving speed and each evaluation index met the Fourier function relationship. Partial correlation analysis showed that in order to ensure the comfort of passengers, more attention should be paid to pavement roughness, and for cargo safety and road friendliness, pavement roughness and vehicle speed should be considered at the same time. The results provide theoretical suggestions for the comfortable driving of future unmanned ground vehicles.

Asphalt track bed, in which an asphalt mixture is inserted underneath the ballast in a conventional granular track bed, has been experimentally proven to have better safety, stability, and economic efficiency. However, most existing theoretical studies were based on continuous mechanics theory, mainly focusing on the impact of the asphalt layer on the macroscale response of the track bed or subgrade, and rarely on the particle-scale mechanical behavior of ballast due to the limitations of the method. Hence, aiming at the improved performance of asphalt track bed and its mesoscale mechanisms, the discrete element method (DEM) model of asphalt track bed is established in this study and then validated with the measurements obtained from on-site tests. Subsequently, the validated model is used to investigate the effect of asphalt layer on the dynamic response and cyclic settlement of ballasted track bed under high-speed train moving loading. Results reveal that the asphalt layer has an ability to reduce vibration of the track bed, which contributes to less active ballast and more stable interparticle structure, thereby decreasing the accumulative settlement of the ballast layer. Besides, the asphalt layer can improve the uniformity of the force chain network inside the ballast layer. The well distributed and less intense force chain network in the ballast results in a slower degradation rate. A series of parameter analyses is performed in the end, concluding that the required thickness of the ballast layer in the asphalt track bed could be reduced by approximately one-third without changing the bearing capacity of the subgrade, and the asphalt track bed structure is a more economical and effective track form for high-speed or heavy-haul railway lines. This study discusses the improvement of ballasted railway track by insertion of asphalt layer and its mesoscale working mechanisms, and the results provide guidance for the design of asphalt track bed in high-speed railways or heavy-haul railways.

The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximize the use of available resources for pavement management agencies through better coordinated preservation and maintenance activities. This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process. In this paper, pavement condition data and maintenance and rehabilitation history collected by the Texas Department of Transportation over the past 18 years were used. Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models. Promising preliminary results were obtained. Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.

The objective of this paper is to present a novel approach for the continuous monitoring of pavement condition through the use of combined piezoelectric sensing and novel condition-based interpretation methods. The performance of the developed approach is validated for the detection of bottom-up fatigue cracking through full-scale accelerated pavement testing (APT). The innovative piezoelectric sensors are installed at the bottom of a thin 102 mm (4 in.) asphalt layer. The structure is then loaded until failure (up to 1 million loading cycles in this study). The condition-based approach, used in this work, does not rely on stain measurements and allows users to bypass the need for any structural or finite-element models. Instead, the data compression approach relies on variations in strain energy harvested by smart sensors to track changes in material and structural conditions. Falling weight deflectometer (FWD) measurements and visual inspections were used to validate the observations from the sensing system. The results in this paper present a first large-scale validation in pavement structures for a piezopowered sensing system combined with a new response-only based approach for data reduction and interpretation. The proposed data analysis method has demonstrated a very early detection capability compared to classical inspection methods, which unveils a huge potential for improved pavement monitoring.

The durability of asphalt mixtures and flexible pavements are closely related to the properties of the asphalt fine aggregate matrix (FAM) with aging. However, there are still no uniform aging procedures specified for laboratory-prepared FAM. In this study, the intermediate-temperature behavior of FAM under various accelerated aging procedures was investigated at the binder scale to reveal the influence of the FAM aging. Three different long-term aging procedures were considered, including loose mixes aging (95°C and 135°C) and pressure aging vessel (PAV) protocols. Frequency sweep test and linear amplitude sweep (LAS) test were carried out on binders extracted from aged FAMs. Rheological tests were also performed on corresponding binders with standard PAV procedure to show the impact of FAM during the aging process. Moreover, an aging kinetics model was put forward to qualify the linear rheological property evolutions for fast and constant aging reaction stages under various aging conditions. The results show that 135°C loose FAM mixes aging significantly reduces the fatigue performance of styrene-butadiene-styrene (SBS) modified binder, and employing 95°C loose mixes aging and PAV aging procedures can avoid this adverse effect. Moreover, to match the aging degree of asphalt and FAM, an aging duration more than 40 h is recommended.

Pay for Performance (PFP) is a statistical-based quality assurance specification used to evaluate asphalt concrete construction for projects having mix quantities greater than 7,260 t (8,000 US tons). The contractor pay is adjusted based on mix field density, air voids, and voids in mineral aggregate. In this study, data were analyzed from the 2015 to 2017 highway construction seasons in Illinois to determine variability trends. Density was the major factor driving contractor pay disincentives in PFP, followed by air voids. To identify the impact of construction field density consistency on the final pay factor, a sensitivity analysis was performed to determine the changes in the contractor pay with respect to variability, which is measured by standard deviation. In addition, an econometric analysis based on sensitivity analysis using linear regression was conducted. The results showed that a 1% reduction in density standard deviation led to a 0.066 increase in density pay factor. Based on the 79 projects analyzed, if the density standard deviation had been reduced from 1.67 to 1.0, the average increase in pay per project would be $38,000.

A passenger vehicle’s fuel-powered engine compensates for its dissipated energy, which is caused by road roughness. This paper presents an integrated vehicle–pavement approach and investigates the excess fuel consumption of a seven-degree-of-freedom (DOF) full-car model on rough pavements. In the approach, a nonstationary Laplace process is used to artificially generate road roughness profiles with consideration of local roughness variance and parallel roughness correlation. The proposed mechanistic approach quantifies the impact of road roughness and vehicle dynamic characteristics on excess fuel consumption. The study concluded that overlooking local roughness variance, an indicator of road roughness nonstationarity, may underestimate excess fuel consumption by 30%. Compared with a two-dimensional (2D) half-car model, a three-dimensional (3D) full-car model reduces computation error of excess fuel consumption by approximately 11%. For practical implementation, the extended roughness-speed-impact (ERSI) model ($R^2=98\%$) is developed to estimate roughness-induced energy dissipation of five two-axle vehicles.

Unpaved roads are an important part of the road transportation system of many countries and they contribute to the accessibility of remote communities and businesses. Despite the importance of unpaved road networks on social and economic development of remote regions, research on semiautomated and automated assessment of these roads is limited. This paper proposes low-cost computer vision–based solutions for assessment of unpaved roads using two approaches: unmanned aerial vehicle (UAV) and participatory-based imaging methods. Both methods use deep neural network to process captured images and locate major road distresses, including potholes, rutting, and corrugations. In addition, a method is proposed to estimate the size of detected potholes in the UAV-captured video frames. Each of the proposed methods was evaluated using a set of experiments, which demonstrated promising performance in assessment of these infrastructure assets that are vital for reliable access of rural and remote communities.

In winter, the ice and snow on the asphalt pavement reduce the friction coefficient of the pavement, which may lead to serious traffic accidents and large-scale congestion. Taking preventive measures to ensure traffic safety by accurately predicting road surface temperature is an economical and environmentally friendly solution. However, road surface temperature (RST) prediction is a challenging task due to the complicated uncertainty and periodicity. To improve the accuracy of RST prediction, this paper aims to propose an advanced ensemble deep learning model using a gated recurrent unit (GRU) network and long short-term memory (LSTM) network. The ensemble model predicts RST by extracting the periodicity of RST and incorporating the lag and accumulation effects of meteorological factors. To verify the applicability of the ensemble model, RST data and climatic data were collected from a road weather station in Jiangsu, China. Extensive experiments are conducted including predictions for 1, 3, and 6 h ahead. The results demonstrated that the performance of the proposed ensemble deep learning model is validated for 1-, 3-, and 6-h nowcasts of RST, with mean absolute error (MAE) of 0.345, 0.833, and 1.743, respectively, and the prediction accuracy was higher than that of the baseline models [convolutional neural networks (CNN)-LSTM networks, support vector regression (SVR), and backpropagation neural network (BP) networks].

Fatigue cracking due to repeated truck traffic loads is the leading cause of failure of asphalt concrete pavement in many locations. Rest periods, referring to the time intervals between successive trucks, may allow for partial or full recovery from fatigue damage and in turn extend pavement fatigue life. This paper delves into the characteristics of rest periods between highway truck traffic loads using traffic data from 40 weigh-in-motion (WIM) stations installed on California state highways and evaluates their effects on pavement performance using mechanistic-empirical incremental-recursive damage and cracking simulation (CalME software). Toward this purpose, truck traffic data were extracted from these WIM stations at selected periods throughout the year 2015 to calculate rest periods. The probability distribution of rest periods and quantiles of cumulative rest periods were calculated, respectively. Regression and statistical analysis for 0.5 quantiles (median) of cumulative rest periods were also performed for different spectra groups and seasons. It was found that rest periods are strongly correlated with the truck traffic volume regardless of the WIM station location and seasons of the year. The actual rest periods based on the nonuniform truck traffic measured from the WIM data were found to be slightly shorter than the corresponding theoretical average rest periods for uniform traffic (ARP-UT) currently assumed in CalME, likely due to truck-following. This theoretical value assumes an equal time interval between trucks all the time. After comparing pavement performance with and without rest periods, it was found that rest periods have a significant influence (30% in difference) on pavement cracking performance in the modeling. Based on this preliminary analysis, the difference in pavement performance caused by the difference between the actual rest periods and ARP-UT is minimal. It is therefore recommended to continue to use ARP-UT to account for the effect of rest periods in pavement design.

In this paper, the feasibility of using siliceous sandstone aggregate in porous asphalt mixture porous asphalt concrete (PAC)-16 was studied. The gradations of siliceous sandstone and limestone PAC-16 mixtures were calculated by the backward regression method and asphalt contents were obtained by the Schellenberg leakage test and Cantabro scattering test. The void rate attenuation, gradation refinement, mechanical properties, and road performances of sandstone and limestone PAC-16 mixtures were tested and compared. Result shows that under the same load, the attenuation of total and connected void rates of sandstone mixture are less, and the mass percentage variation of aggregate in each grade in a sandstone mixture under repeated load is smaller. A sandstone mixture has similar mechanical properties as a limestone mixture with a lower void rate, and it has better mechanical properties compared with limestone mixture with a similar void rate. Compared with a limestone mixture, a sandstone mixture has better permeability and high-temperature performance, close low-temperature performance, and slightly worse moisture stability. Therefore, a sandstone mixture could not be applied in winter frost areas to reduce freeze-thaw cycling, and it could be applied in single-layer drainage pavement with low traffic volume or in the lower layer of double-layer drainage pavement to avoid direct contact with traffic loads. The addition of an antistripping agent is strongly recommended.

Abstract

Moving Ahead for Progress in the Twenty-First Century (MAP-21) requires US state highway agencies (SHA) to utilize performance-based approaches in their pavement management decision-making processes, and use of a remaining service life (RSL) model would be one of such performance-based approaches for facilitating the pavement management decision-making process for SHAs. In this study, statistical and artificial neural network (ANN)-based pavement performance and RSL models were developed for Iowa jointed plain concrete pavement systems (JPCP) using actual pavement structural, traffic, construction history, and pavement performance records obtained from the Iowa Department of Transportation pavement-management information system database. While both models were found to be potentially useful for project and network level performance and RSL predictions, statistical and ANN-based models were respectively found to be more suitable for project and network level analysis. Using these models, efficient Microsoft Excel-based automation tools were created to predict future performance of a JPCP section and estimate RSL values based on predicted future performance and threshold limits for the performance indicators. Consequence analysis was also conducted to investigate the impact of traffic and preservation treatment (diamond grinding) on the RSL of a JPCP. The tool, also capable of estimating realistic pavement pretreatment and posttreatment performance and RSL, could be successfully used as part of performance-based pavement management strategies and helping decision-makers make better-informed pavement management decisions to properly allocate agency resource expenditures. Moreover, this study provides a better understanding of RSL and the factors that influence both the project and network level RSL.

Practical Applications

In this study, the authors developed models for potential use in predicting future conditions of Iowa jointed plain concrete pavements using advanced modeling techniques. Based on such predicted data in combination with a threshold value, the criterion defining when a JPCP section could no longer be structurally and/or functionally good enough for use, remaining service life of a JPCP section could be estimated. Using the models, a Microsoft Excel–based automation tool was created to automatically predict future pavement-condition, calculate RSL, and graphically show the results from user-entered inputs. The tool is also capable of re-estimating RSL either if (1) traffic levels unpredictably increase or decrease in the future, or if (2) pavement preservation (diamond grinding) is applied. Overall, the automation tool created in this study is capable of providing realistic pavement condition and RSL estimations and could be successfully used in developing pavement management strategies by incorporating it into pavement management systems and helping decision-makers make better-informed pavement-management decisions to properly allocate agency resource expenditures.

Bicyclists depend on the visibility of the surrounding environment to maintain a safe travel path. Throughout the country, new types of bicycle infrastructure and pavement markings are being installed. This study used a new procedure to evaluate different pavement markings used in bike lanes. Three different paints were evaluated including green waterborne, green methyl methacrylate (MMA), and thermoplastic paint. The deterioration of these materials was tested under different conditions that simulated wear from motorized vehicles and street equipment. Surface polishing (i.e., repeated passing over the material) was examined under pneumatic tires, steel wheels, and steel scraper blades. Different characteristics were measured including durability, retroreflectivity, color changes, and surface friction of the test materials. The results demonstrated that the procedure used in this study was able to evaluate different materials in a short time compared to field evaluation which often can take years to complete. As expected, the retroreflectivity decreased with the number of polishing cycles. Overall, there was a significant decrease in percent retroreflectivity after 1,000 polishing cycles for all testing conditions before retroreflectivity eventually reached a terminal value. Based on the ${\mathrm{R}}^2$-values, a logarithmic model was suited to best describe the change in the retroreflectivity of markings with respect to loading cycles. The MMA paint experienced the lowest color loss even after 100,000 polishing cycles irrespective of the exposure and the testing conditions. It is believed that the small reduction in color is due to the presence of chemicals coupled with thicker paint of the MMA materials compared to waterborne materials. The durability results demonstrate that the waterborne markings peeled off the surface with increased polishing cycles. These findings will benefit transportation agencies, to predict the performance deterioration of various pavement markings for the bike lanes, and the proposal of new pavement making, while assisting in the schedule for marking maintenance projects.

Cracking is one of the most common pavement distresses that affect the design life and durability of hot-mix asphalt pavements. The evaluation of cracking resistance is essential in pavement design. Several laboratory tests and performance indicators are used to evaluate cracking resistance. This study examines the sensitivity of various cracking tests and performance indicators to the change in mix composition and evaluates the variability and correlation between various cracking performance indicators. The cracking performance indicators used in this study are IDEAL-${\mathrm{CT}}_{\mathrm{Index}}$, cracking resistance index (CRI), ${\mathrm{N}}_{\mathrm{flex}}$, ${\mathrm{Weibull}}_{\mathrm{CRI}}$, fracture energy (${\mathrm{G}}_{\mathrm{f}}$), ${\mathrm{IDT}}_{\mathrm{Strength}}$, ${\mathrm{IDT}}_{\mathrm{Modulus}}$, and flexibility index (FI). The results showed that ${\mathrm{IDT}}_{\mathrm{Modulus}}$ and ${\mathrm{IDT}}_{\mathrm{Strength}}$ captured the change in binder content, binder grade, and RAP content. Other indices, including IDEAL-${\mathrm{CT}}_{\mathrm{Index}}$, ${\mathrm{Weibull}}_{\mathrm{CRI}}$, CRI, and ${\mathrm{N}}_{\mathrm{flex}}$, were sensitive to binder content and to only one RAP source. RAP source was found to have a significant effect on cracking performance in this study. The results also showed that high RAP content (up to 50%) provided performance comparable to that of the control mix depending on the RAP source. In addition, cracking performance could be improved by increasing the binder content in mixes with stiffer RAP materials. This indicates the importance of balanced mix design when incorporating RAP materials in asphalt mixes.

Although high-friction surface treatments (HFSTs) are a proven efficient countermeasure for the reduction of roadway departure crashes, their long-term pavement performance has yet not been fully understood with large-scale field monitoring. This paper presented and analyzed 3 years of monitoring results on 36 HFST sites located in 12 states in the US ranging from 0 to 11 years in treatment age. Taking the traffic directions and number of lanes at each site into consideration, a total of 65 HFST segments were measured. Onsite data were collected by a Grip Tester for pavement friction, the PaveVision3D laser imaging system for condition evaluation and high-resolution pavement images, and the AMES high-speed profiler for surface texture and roughness. The evolutions of HFST surface characteristics and field performance were evaluated. It was found that most HFST sites continued providing superior friction values compared with the neighboring nontreated surfaces. Cracking and surface delamination were observed as the major failure types on a few sites. The delaminated area exhibited higher roughness values and lower friction numbers. Subsequently, a multivariant friction prediction model was developed to assist in understanding its influencing factors and deterioration trends. Furthermore, a safety performance function was built for crash frequency estimation on HFST sites, through which the benefit of HFST in reducing roadway departure crashes could be further quantified.

Construction materials and related management, handling, and storage provisions account for a large portion of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. This study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimeter and an error level of approximately 3% when measuring soil stockpile volumes. In conjunction with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors ensure road quality.

The poor convergence of surface mechanical responses of asphalt pavement has always been an important factor affecting the application of elastic system mechanics. This paper briefly reviewed the analytical solutions of the elastic multilayered system under an elliptical load. Then, the calculation efficiency of mechanical response at the surface was analyzed. Afterward, the integral kernel oscillation of mechanical response was analyzed. And it shows that the reason for the poor calculating efficiency of surface mechanical response is that Bessel function is difficult to converge. Finally, a fast calculation method was proposed through the piecewise calculation of integral and the simple formula of integral with Bessel function. The accuracy and efficiency of the proposed method was evaluated with comparing the initial calculation program. The result shows that the proposed method could greatly improve the efficiency of calculation and control the error within 1%.

The performance of flexible pavement is influenced by pavement material properties and the strength or the stiffness of the pavement layers. Pavement temperature significantly impacts the material properties of flexible pavements. However, to date there has not been much research that investigates the prediction of the pavement temperature in unbound material (base and subgrade layers). The goal of this research is to apply a new approach, machine learning, to predict pavement temperature in unbound material. Pavement temperature recordings collected at the Integrated Road Research Facility (IRRF) test road in Alberta from January 2013 to February 2020 were used to train and validate machine learning models. Finally, high-performance machine learning models with two parameters (air temperature and day of the year) were developed to predict the average daily pavement temperature at $0.5–2.7\mathrm{m}$ below the road surface. The accuracy of the temperature in the base and subgrade layers predicted using the machine learning models was found to be higher than for an existing model.

Nanosilica (NS) has been involved in many investigations over bituminous materials. Although nanosilica is variable in size and type, most literature has failed to consider or acknowledge its type and/or size impacts. Hence, this research focused on this knowledge gap by considering 20-nm, 20-nm with couplant, and 60-nm NS types for some index, rheological shear, and creep tests. Tests were conducted at virgin, short-term, and long-terms aging conditions as well. Estimations of rutting and fatigue resistances were found to be promising at different extents. Generally, 60-nm NS was better for rutting, whereas 20-nm NS was more efficient for fatigue. Initially, dry blending was considered during sample preparations. However, wet blending was also used later for extra samples and limited shear oscillation tests. Some discrepancies in dry-prepared results were found to be attributed to mixing conditions. Outcomes of experiments after wet blending showed more consistent and reliable direct relationships between sample properties and NS contents. Hence, coarse NS at higher ranges, typically at 8% usage, was more efficient than other batches.

This paper examined the applicability of the pruned exact linear time (PELT) algorithm for optimal change-point analysis to delineate pavement into homogeneous sections based on road condition. The primary objective of the study was to address some of the limitations associated with existing delineation approaches, such as the AASHTO cumulative difference approach (CDA) and the Bayesian binary segmentation method. The paper presents a mathematical framework for the optimal change-point analysis of pavement deflection data collected on a runway of an international airport. Through an analytical comparative study, the paper demonstrated how the proposed approach is better than existing delineation methods in terms of accuracy in identifying the change points, flexibility in the choice of the number of homogeneous sections, and computational time. The approach provides an opportunity for practitioners and road agencies to integrate their experience and knowledge of the road network into decision-making about the location of change points and their count without compromising computational accuracy. The proposed method was found to be robust because the solutions are independent of the parameters chosen for the analysis.

The demand for high-performance asphalt pavements has been increasing in recent years due to increased traffic volume, automobile size and weight, and rising temperatures due to climate change. It is necessary to modify the bitumen to achieve long-life high-performance pavements in many cases. Consequently, polymers obtained from plastic wastes emerged as a modifier to enhance the bitumen properties. Polymers offer a number of advantages. Polymers such as polyethylene (PE) or polypropylene (PP) offer a number of advantages when used as binder modifier. Using PE or PP in infrastructure projects can be an innovative method to reduce the environmental damage of these nonbiodegradable wastes and the high cost of recycling these wastes. Therefore, this paper extensively investigates the experimental studies carried out by many researchers to evaluate polymer-modified binders’ properties as an alternative for pure bitumen in asphalt mixtures under different percentages and production processes. The main goal is to establish a better understanding of the engineering properties of polyethylene-modified asphalt mixtures and ultimately argue that waste polyethylene has a viable use as a modifier for asphalt binders.

Flexible pavements are prone to premature deterioration, and researchers are unresolved regarding the importance of the underlying causes resulting in inappropriately selected modeling parameters and increased uncertainty in predicting subsequent behavior and performance. A windshield survey, literature survey, and fuzzy Delphi study were undertaken as complementary approaches to costly conventional investigations to identify reasons for flexible pavement deterioration in the design, construction, and life span phases. Overall, the results revealed that the life span phase consists of the most contributors to pavement deterioration, which is approximately twice as much as the design and construction phases. However, the findings suggest that most causes of deterioration in the life span phase can be attributed to deficiencies in the preceding phases. Experts believe that structural and traffic are the most significant contributors to pavement deterioration, more so than construction, environment, and maintenance factors. Additionally, the surface and subgrade layers were deemed to be the most problematic. Applying the fuzzy Delphi method minimizes the ambiguities associated with the causes of pavement deterioration identified in the literature and is advantageous for limited data. This study proposes measures for improving the design and construction of more sustainable tropical pavements. Improved knowledge of the causes of deterioration is vital for selecting the appropriate design, construction, and maintenance strategies.