Pavement Structural Evaluation Research Articles

Comparison between ACN–PCN and ACR–PCR for rigid airport pavement with case study

This technical note analyses the new Aircraft Classification Rating–Pavement Classification Rating (ACR–PCR) system with a case study. The literatures and case studies showed that anomalies exist between results of pavement structural evaluation of rigid airport pavements using the pavement thickness design (PTD) method and the Aircraft Classification Number–Pavement Classification Number (ACN–PCN) method. In some situations, the ACN–PCN system provides a conservative assessment of pavement damage which will likely overestimate the load impact of aircraft with complex gear configuration. The International Civil Aviation Organisation (ICAO) introduced a new system, the ACR-PCR, which applies layered elastic analysis to estimate pavement bearing capacity. Using a case study from an airport in Southeast Asia, the anomalies between the designed pavement bearing strength of the PTD method and the ACN–PCN method were discussed. The ACR–PCR system can provide pavement structural evaluation results that are consistent with the PTD results.

A comparative life cycle assessment (LCA), life cycle cost analysis (LCCA), mechanical and long-term leaching evaluation of road pavement structures containing multiple secondary materials

Oil-Based Drill Cuttings (OBDC) are one of the hazardous wastes of the oil and gas industry. In this study, Reclaimed Asphalt Pavement (RAP), OBDC and Fly Ash (FA) geopolymer were used in Cold Central Plant Recycling (CCPR) mixtures. The dynamic modulus test was used to appraise the viscoelastic properties of CCPR mixtures. The Toxicity Characteristic Leaching Procedure (TCLP) test was also utilized to investigate the long-term contaminants leaching. In addition, a cradle-to-grave Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) were conducted to evaluate the potential environmental burdens and economic costs of pavement structures containing CCPR mixtures. Moreover, Multi-Criteria Decision-Making (MCDM) analysis using four different methods was performed to determine the optimal sample. The results revealed that the recycling of OBDC and RAP in stabilized CCPR mixtures with FA geopolymer can improve the viscoelastic properties and prevent the leaching of contaminants from these waste materials. Furthermore, recycling of the mentioned waste materials in CCPR mixtures has caused a significant reduction in global warming, acidification, eutrophication, smog, respiratory effects, Human toxicity, ecotoxicity, and cumulative energy demand up to 41%, 36.6%, 73%, 43%, 47%, 34%, 73%, and 13%, respectively. Additionally, using CCPR mixtures containing the OBCD, RAP, and FA geopolymer in the pavement structure has led to economic benefits of up to 31% compared to the conventional pavement system.

Investigation on shakedown response-behavior of thawed subgrade soils under long-term traffic loading

The shakedown state of the subgrade is crucial for the sustainable design and long-term stability evaluation of pavement structures. In order to characterize the plastic deformation and shakedown behavior of subgrade soil in seasonal frozen regions, cyclic triaxial tests were conducted on the thawed subgrade soil after seven cycles of freeze-thaw. The influences of the numbers of cycle loading, the amplitude of cyclic deviator stress, and the confining stress were considered variables. The evolution features of accumulative plastic strain, accumulative plastic strain rate, and critical dynamic stress were experimentally analyzed. Based on the shakedown theory, the ensuing discoveries were that the accumulative plastic strain response-behavior of thawed subgrade soil was typically divided into plastic shakedown, plastic creep, and incremental collapse under the long-term cyclic loading. Furthermore, the shakedown standard for thawed subgrade soil was also proposed based on the evolution of the accumulative plastic strain rate. The critical dynamic stresses can be obtained by the proposal formula to determine the different plastic deformation ranges.

Evaluation of Selected Empirical Models for Asphalt Pavement Temperature Prediction in a Tropical Climate: The Case of Ghana

Asphalt pavement temperature has several applications, including pavement structural design and evaluation, asphalt mixture design, asphalt binder grade determination and material aging characterization. However, available asphalt pavement temperature prediction models were mostly developed for temperate climatic conditions. Before such models are adopted for use in a tropical climate to facilitate advanced pavement engineering, their applicability must be verified. This study evaluated five empirical asphalt pavement temperature prediction models: the Lukanen (BELLS 3), Park, Diefenderfer, and Taamneh models, all developed in the United States, and the Asefzadeh model, formulated in Canada, to ascertain their prediction accuracy in a tropical climate, using the West African country Ghana as a case study. The results of such a model evaluation study will justify the adoption of existing models for local application or the development of new ones suitable for tropical climates. In this study, in situ asphalt pavement temperature data were measured at two sites for eight months: Kumasi and Tamale in the Forest and Savannah climatic zones, respectively. The measured pavement temperature data were compared with predicted pavement temperatures using the two independent-samples t-test, the coefficient of determination, the line of equality, and three error statistics (mean bias error, mean percentage error and root mean square error). It was found that the Park model provided a more accurate pavement temperature prediction in both climatic zones. The other models either over-predicted or under-predicted asphalt pavement temperature with significant error margins. However, there is a need to improve the prediction accuracy of the Park model as considerable over-prediction occurred at a temperature of at least 47 °C or a local model developed.

Sensitivity analysis of asphalt pavement performance under freeze-thaw cycles by applying reliability method

Asphalt pavement performance remains pivotal in ensuring the longevity and safety of our transport networks, especially in regions that face unique environmental challenges such as the Qinghai-Tibet plateau. This study presents a groundbreaking reliability model for the evaluation of asphalt pavement performance, based on the esteemed AASHTO design equation. Four cardinal uncertainty factors, encompassing the environment, traffic loads, pavement structures and materials, and anthropogenic activities, were meticulously integrated into the performance analysis. Delving deep into the intricate dynamics of the pavement structure strength, five random variables - SN, ΔPSI, Mr, ESAL0and r - were identified and analyzed, reflecting the inherent uncertainties during its lifecycle. A notable observation was the linear decline of SNF-T with increasing freeze-thaw actions. Additionally, a comprehensive sensitivity analysis highlighted the profound influence of ESAL0 on the sensitivity of the load equation and emphasized the dominant role of SNF-T in pavement structural performance evaluation. This investigation not only unravels the complex interactions governing asphalt pavement behavior but also provides an invaluable framework for future research, design optimization, and infrastructure maintenance strategies in challenging terrains.

A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall

Embankments and retaining walls are integral parts of the bridge system and provide a smooth transition from lower elevations (i.e., roadways) to higher elevations (i.e., bridge decks). Performances of pavement structures supported on embankments or retaining walls are directly related to their conditions. This paper presents a comprehensive case study of the evaluation of pavement structures supported on an in-service mechanically stabilized earth (MSE) wall that showed significant distresses, such as lane separation, faulting, lane settlement, and tilting of the MSE wall. The conditions of the pavement structures were evaluated via visual observations, falling weight deflectometer (FWD) tests, and coring through pavement structures. The conditions of the MSE wall were evaluated through dynamic cone penetrometer (DCP) tests, cone penetration tests (CPTs), LiDAR surveys, and soil borings. Detailed analysis of the data obtained in this study provides valuable insights into potential distress mechanisms.

Traffic Speed Deflectometer for Network-Level Pavement Management in Tennessee

The traffic speed deflectometer (TSD) overcomes the limitations of the falling weight deflectometer (FWD) with respect to traffic interruption and test inefficiency and is considered the preferred method for network-level pavement structural evaluation. However, some challenges exist when applying the TSD to routine survey work. This study evaluated the long-term repeatability of TSDs and compared TSD measurements with FWD measurements to examine device-related errors in TSD measurements. In addition, a framework for incorporating TSD measurements into the pavement management system (PMS) was developed by introducing deflection bowl parameters (DBPs). All data analyses were based on TSD data collected in Tennessee. From the current study, the repeatability of the TSD has not been fully validated because of device-related errors. Since TSD measurements and FWD measurements are broadly similar, periodic validation of TSDs by FWDs is recommended for quality control. TSDs provide valuable information about pavement structural conditions that are not reliably or adequately reflected in the current PMS (which focuses mainly on pavement functional conditions). DBPs are a simple and fast method for making initial estimates of pavement structural conditions without the need for layer thickness. In the proposed DBP system, the surface curvature index (SCI_12) and D60 (TSD deflection at 60 in. before the load center) represent the structural strength of the asphalt layer and subgrade, respectively. The combination of pavement functional conditions and TSD-measured structural conditions (PMS+DBPs) may help to make more accurate predictions of pavement performance and to make more appropriate decisions on maintenance and rehabilitation strategies.

Estimating Load Transfer Efficiency for Jointed Pavements from TSD Deflection Velocity Measurements

Transverse joints are the weakest element of jointed pavements, and when these joints lack structural capacity, the onset of load-related distress is imminent. The most widespread measurement of the joints’ structural performance is the Load Transfer Efficiency Index (LTE), a ratio of the deflection of the two adjoining slabs. LTE can easily be assessed with a falling weight deflectometer, but this test procedure is not advisable for evaluation at the network level because of user safety concerns and because it can be excessively time-consuming. Traffic speed deflection devices like the traffic speed deflectometer (TSD) are suitable devices for network-level pavement structural evaluation. Yet, as of today, no interpretation technique to get structural health metrics for jointed pavements from TSD data has been published. In this paper, a backcalculation scheme based on slab theory is proposed to estimate the joints’ LTE from TSD deflection velocity measurements. The backcalculation problem formulation and its numerical solution using fast procedures are described in detail. The approach is tested with TSD data collected on the MnROAD test track. Overall, it was found that the backcalculation converges to reasonable estimates of the pavement structural properties and can furnish LTE estimates for most transverse joints from 5 cm-resolution TSD data, all at a reasonable computational cost. This allows for corridor-wide LTE assessment of a pavement’s joints using TSD measurements.

Materials Optimization and Service Performance Evaluation of a Novel Steel Bridge Deck Pavement Structure: A Case Study

Although the double-layer pavement structure with a top layer of stone mastic asphalt concrete (SMAC) and a bottom layer of epoxy asphalt concrete (EAC) has been confirmed to have excellent overall performance in the laboratory, there is a lack of comparison and verification in practical projects. Hence, the utilization of the SMAC + EAC structure in this steel bridge deck pavement (SBDP) practical project and the clarification of its service performance are of significant importance for facilitating the promotion and application of this novel structure. This study relied on an SBDP reconstruction project in Ningbo, China. Indoor performance tests were used to determine the appropriate material compositions for SMAC and EAC. Subsequently, both ERS and SMAC + EAC pavement structures were paved in the project, and the service conditions of the different pavements after one year of operation were tested and compared. The results indicated that the epoxy SBS asphalt (ESA) binder prepared by substituting SBS-modified asphalt binder for the base binder, exhibited improved mechanical strength and toughness. The variation of modifier content significantly affected the high-temperature stability, low-temperature crack resistance, and moisture damage resistance of epoxy SBS asphalt concrete (ESAC) and high-viscosity SBS asphalt concrete (HSAC), while the gradation mainly influenced the skid resistance. The optimal contents of modifiers in ESA and HAS binders were finalized at 45 wt% and 11 wt%. After one year of operation on the trial road, the pavement performance of the SMAC + EAC structure had significant advantages over the ERS system, with all lanes having an SBDP quality index (SDPQI) above 90 and an excellent service condition. The successful application of the SMAC + EAC structure validated its applicability and feasibility in SBDP, which provided strong evidence for the further promotion of this structure.

Structural Performance of Foamed Asphalt Base in a Full Depth Reclaimed and Sustainable Pavement

Environmental awareness and budgetary constraints in road construction and rehabilitation require innovative and at the same time environmentally friendly solutions in favor of resource conservation, in any type of pavement intervention. In the meantime, surface interventions in severely deteriorated pavements are insufficient, increasing as such the recycling potential of existing materials in Full Depth Reclamation (FDR). This study addresses the efficiency of an FDR process in which the recycled base course of an existing semi-rigid pavement is stabilized with Foamed Asphalt (FA). The adverse effects of FA curing, which have a significant impact on pavement evaluation, particularly in the early stages of a pavement’s life cycle, prompted the conduct of a comprehensive testing campaign at two different periods, shortly after pavement rehabilitation and five years thereafter. The campaign included nondestructive testing at the aforementioned periods using Ground Penetrating Radar (GPR) and the Falling Weight Deflectometer (FWD), which facilitate the structural evaluation of the FA base. Using the collected nondestructive testing data, the principles of Multi-Layer Elastic Theory (MLET) and Genetic Algorithms (GA) are used to investigate the performance of the FA base. Both analysis tools captured the FA strength increase in the second period, but an increased variance of the FA modulus was observed mainly in the first test period, probably due to the curing effect. In addition, GA showed an advantage over other, more conventional tools for back-analysis of pavement stiffness, resulting in an interesting correlation potential of FA modulus to a deflection-based parameter reflecting the condition of the FA layer. Overall, the study contributes to the development of a practical methodology suitable for the evaluation of non-conventional and sustainable pavement structures.

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment. In some cases, such as launching missiles or rockets, layered pavements are required to bear large impulse load. However, traditional methods cannot non-destructively and quickly detect the internal structural of pavements. Thus, accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity. In recent years, machine learning has shown great superiority in solving nonlinear problems. In this work, we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing. The regression coefficient R 2 of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses, which indicates that the prediction results have great consistency with finite element simulation results. This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements, and has application potential in non-destructive evaluation of pavement structure.

Evaluating structural characteristics of asphalt pavements by using deflection slopes from traffic speed deflectometer

The Traffic Speed Deflectometer (TSD) overcomes the limitations of the Falling Weight Deflectometer (FWD) in terms of traffic interruption and testing inefficiency, making it an efficient tool for network-level pavement structural evaluation. The current research on TSD is inherited from FWD and mainly focuses on deflection itself. However, the raw measurement of TSD is the deflection slope, i.e., the first-order derivative of deflection. TSD deflection is calculated by curve fitting and integration, and the extensive use of deflection itself may accumulate the error caused by curve fitting. This study explores the potential use of TSD deflection slope in assessing the strength of pavement structures, estimating the location of the inflection point, and calculating the lag distance. The results show that both the TSD deflection and slope are effective in detecting weak asphalt layers and subgrades, but they are not sensitive to damage to the base layer. It is recommended that TSD slopes at 5″ and 60″ (in front of the load center) be used to indicate the strength of the asphalt layer and subgrade, respectively. The location of the inflection point is related to the asphalt thickness and subgrade modulus, and it can be approximated by the location of the maximum slope measurement. In addition, the lag distance can be calculated by determining the maximum deflection point or by determining the zero-slope point. Since the maximum deflection method accumulates errors in curve-fitting and is more sensitive to interpolation methods, the zero-slope method is recommended to calculate the lag distance.

ContactGAN development – prediction of tire-pavement contact stresses using a generative and transfer learning model

ABSTRACT An accurate characterisation of tire-pavement contact stresses is important for pavement structural analysis and performance evaluation. The demand for rapid pavement design and analysis requires a simple, accurate, and robust approach that pavement engineers can easily use. In this study, a generative adversarial network model – ContactGAN – was developed to replace the finite element analysis (FEA), for predicting the three-dimensional (3D) non-uniform contact stress. The ContactGAN model consists of a Generator that generates synthetic stress fields, and a Discriminator that distinguishes synthetic and FEA-generated stress fields. Three finite-element (FE) generated tire contact stress datasets were used. Compared with the previously developed ContactNet model, the ContactGAN model produces a more accurate contact stress distribution in both the overall quality and the most critical values. As part of the deep learning model, a transfer learning method was developed to address the challenges of applying the model to tasks with minimal datasets. After the transfer learning approach is applied, the mean square errors (MAEs) of the 3D contact stress predictions of the cornering tire dataset and the hyper-viscoelastic tire dataset are reduced by 83.8% and 92.9%, respectively. This research delivers a data-driven approach for the rapid and accurate prediction of tire-pavement contact stresses.

A parameter identification technique for traffic speed deflectometer tests of pavements

The structural evaluation of existing pavements forms the basis for formulating cost-effective maintenance and rehabilitation strategies. A promising tool for pavement structural evaluation at network level is the Traffic Speed Deflectometer (TSD) test. However, the application of the TSD test is hindered by the lack of a robust and efficient parameter identification technique. To solve this problem, a theoretical model for the TSD test is first formulated. Then, a minimisation algorithm which works best with the theoretical TSD model for parameter identification is selected. Finally, the performance of this combination in processing field TSD measurements is studied. The results show that the modified Levenberg-Marquardt algorithm using all the 9 detection points is most suitable to be combined with the theoretical TSD model for parameter identification, which gives a promising parameter identification technique for TSD tests of pavements. The presented work contributes to the development of technologies for pavement structural evaluation.

A data-driven approach to the evaluation of asphalt pavement structures using falling weight deflectometer

<p style='text-indent:20px;'>The evaluation of asphalt pavement structures has been a critical challenge in the field due to the practical limitations in methodology. In this paper, we propose a data-driven framework to evaluate structural performance of nineteen widely used asphalt structures in the Research Institute of Highway Ministry of Transport track (RIOHTrack). Specifically, we utilize the unsupervised machine learning method to delineate the similar and disparate performance among tested structures based on four years of falling weight deflectometer (FWD) experiments. Next, the structural performance is investigated on the temporal scale and the dynamic performance variations are captured over the course of the testing. Finally, experimental results are discussed and we provide essential evidence to aid future asphalt pavement design and construction.</p>

Comparative study on structural evaluation of flexible pavement using BBD and FWD

Flexible pavements undergo deterioration with increase in heavy traffic. Thus, there is a need to evaluate the existing pavement condition and undertake appropriate maintenance measure for safe and efficient flow of traffic. Benkelman Beam Deflectometer (BBD) and Falling Weight Deflectometer (FWD) are the most widely used non-destructive techniques for evaluation and strengthening of flexible pavements in India. There is a need for rational approach of pavement evaluation because of high cost associated with new road construction and limited financial resources for maintenance of roads. Benkelman beam deflectometer involves static loading whereas falling weight deflectometer involves dynamic loading which simulates moving traffic. The main objective of this study is to develop a correlation between FWD and BBD. This paper presents a typical case study on a 5 km length of National Highway from chainage 34+006 to 39+003 in Bangalore, Karnataka. Pavement structural evaluation is conducted using FWD and BBD. The tests were conducted on the same road stretch at the same point of loading. The results are analyzed as per Indian Road Congress (IRC) guidelines and overlay thickness is designed using KGP-BACK and IIT-PAVE. Linear correlation between BBD and FWD is developed using central deflection values and the co-efficient of correlation (R2) is found to be 0.8994. The developed correlation is then validated for present study data and available literature data. The root mean square error is found to be 6.77 and 7.67 respectively. Based on the observations, it is concluded that FWD serves as one of the more reliable method for the structural evaluation of pavements and this method proves to be cost effective by reducing the desired overlay thickness.

Numerical investigation of pavement responses under TSD and FWD loading

The Traffic Speed Deflectometer (TSD) overcomes the limitations of the Falling Weight Deflectometer (FWD) in terms of traffic interruption and testing inefficiency, thus has been used for network-level pavement structural evaluation. However, compared to FWD, which has accumulated plenty of data and comprehensive evaluation methods over decades, the applicability of TSD is yet to verify. It is imperative to compare pavement responses under TSD and FWD to validate the applicability and substitutability of TSD. Based on the difference between TSD and FWD in loading characteristics and asphalt concrete (AC) properties, six 3D-Move models were established successively to explore the influence of different testing factors on pavement responses. The simulation results show that under the same load magnitude, peak deflections and peak strains caused by TSD were always greater than that of FWD due to the lower equivalent load frequency of TSD. Compared to the peak deflection, the deflection basin shape of TSD was more sensitive to the dynamic effects and the viscoelasticity of the AC layer. Besides, AC viscoelasticity and damping caused the lag in TSD deflections and strains. In the layer modulus back-calculation from TSD deflections, the dual circular load and dynamic effect of TSD were crucial, but the AC viscoelasticity could be neglected. The use of FWD-based programs to back-calculate the layer modulus from the TSD deflection would result in a stiffer AC layer and a stiffer subgrade.

Traffic Study and Analysis of Highway (NH5) from Balongi to Kharar

Evaluation of in service pavements is very vital for keeping them in good serviceable condition because pavements deteriorate with age and traffic loading. To get a complete idea of the existing condition of any pavement both structural and functional evaluation are necessary. This study aims to investigate the accidental spots, traffic volume, and pavement condition. For this survey, the location of Kharar was chosen i.e. Balongi to Kharar Bus-Stand of 7 km stretch. The road initiates with intersection near Kharar bus stand and passes through many in between intersections near Sunny Enclave, VR Punjab Mall which are prime locations in that area. This road also connects with T junction and connects to NH5 / NH7 via Airport road. The data collected was processed, categorized and analyzed to generate reports for vehicle classification, hourly traffic variation, accidental black spots, pavement condition and origin & destination of trips.

3D-finite element pavement structural model for using with traffic speed deflectometers

ABSTRACT The traditional practice for pavement structural capacity evaluation is mostly dependent on the falling weight deflectometer (FWD) measurements. However, this practice has several challenges due to its stationary nature. Recently, a Rolling Weight Deflectometer (RWD), capable of measuring the pavement deflections at regular traffic speeds, has been introduced into the market. This research aims to develop a finite element (FE) model simulating pavement structural response under the moving RWD loads. This model could be further utilised for accurately estimating pavement layers mechanical characteristics. The model was successfully validated utilising the RAPTOR (a commercially available RWD) data collected on the National Center for Asphalt Technology (NCAT) test track. A good correlation between the simulated and the measured values was found – yielding an average root mean square percentage error (RMSPE) of approximately 2.40% between the RAPTOR and the FE deflections.

A nonlinear spectral element model for the simulation of traffic speed deflectometer tests of asphalt pavements

ABSTRACT A non-destructive testing method suitable for network-level pavement structural evaluation is the traffic speed deflectometer (TSD) test. However, the analysis of TSD measurements still needs a proper parameter back-calculation procedure, which requires an accurate and efficient forward calculation model. As a first step to solving this issue, a nonlinear spectral element model which can simulate TSD tests of asphalt pavements is developed. The model is used to investigate the characteristics and parameter sensitivity of the response of asphalt pavements caused by the TSD loading. The results indicate that the vertical deflection curve along the direction of movement observed on the asphalt pavement surface is slightly asymmetric, and the maximum deflection appears behind the centre of the loading area. In addition, the slope curve of vertical deflection is highly sensitive to the magnitude of the applied force, the moduli of the base layer and subgrade, and the thicknesses of the asphalt layer and base layer. Furthermore, the slope curve is relatively sensitive to the glassy modulus of the asphalt layer. Because of its good predictive capability and high computational efficiency, the proposed model has the desired characteristics to be used as the computational kernel for parameter back-calculation procedures of TSD measurements.