Pavement Test Research Articles

Rutting prediction of asphalt pavement with semi-rigid base: Numerical modeling on laboratory to accelerated pavement testing

Rutting development and its prediction are critically important for the long-term preservation of semi-rigid pavements. A multi-scale test was developed to analyze the rutting development and its prediction model, involving triaxial, scaled loading, and full-scale loading tests. Laboratory-based triaxial tests predicted the rheological properties of asphalt mixtures at high temperatures. The flow number FN was only related to the cohesion and internal friction angle, confining pressure, and circulating pressure of the mixture. Rutting prediction models were established using a modified Burgers finite-element model and verified through scaled and full-scale loading tests. The relationship between FN of the dynamic creep test and loading times of scaled and full-scale loading test were determined through comparative analysis. Finally, the cross-scale relationship between the multi-scale tests was established by introducing correction coefficients K1 and K2. The proposed prediction model of rutting development is suggested to be computationally reasonable, which could provide a scientific reference for the treatment and maintenance of rutting on asphalt pavements.

Evaluation of Airport Pavement Base Layer Stiffness Characteristics via Embedded Field Sensors

Real-time stiffness monitoring of unbound aggregate layers using embedded field sensors is crucial for advanced airport pavement design and management. This paper describes research findings on monitoring base layer stiffness characteristics for airport pavements using an embedded bender element (BE) field sensor, inductive coil sensors, and pressure cells. A full-scale pavement test section was constructed at the Federal Aviation Administration (FAA)’s National Airport Pavement Test Facility (NAPTF). A BE field sensor, inductive coil sensor pairs, and pressure cells were installed in the unbound aggregate base to evaluate the layer stiffness, applied load stress, and deformation characteristics. A triple dual tandem gear loading module applied 58,000 lb per wheel to the tested section with a wander pattern consisting of 66 passes arranged in nine lateral wander positions. Dynamic responses of coil sensors and pressure cells were collected during the traffic test, whereas BE signals were collected after completing each wander pattern. Laboratory repeated load triaxial tests with BE instrumentation were also conducted to establish a correlation for converting BE field sensor readings to layer modulus properties. The in situ modulus characteristics of the aggregate base layer were evaluated during full-scale testing using two methods: shear wave velocities from a BE field sensor, and strain and stress measurements from coil sensors and pressure cells. The moduli estimated using both approaches fell into typical ranges of crushed aggregate bases and were highly comparable. The moduli from the dynamic sensor measurements clearly showed the effect of vehicle wander on aggregate base layer measured responses.

Theoretical Derivation of and Experimental Investigations into the Dielectric Properties Modeling of Concrete

Investigations into the dielectric properties of concrete form the basis for concrete quality testing and condition monitoring of internal structures when using ground penetrating radar (GPR). The formula used to describe the quantitative relationship between the dielectric properties of composite materials and the dielectric constant and volumetric ratio of each component is known as a dielectric model. In this study, a new dielectric model was deduced from circuit theory on the basis of structural abstraction to calculate the dielectric permittivities of concrete. To validate the applicability and accuracy of the proposed model, because concrete consists of matrix (cement mortar), aggregates (limestone), and air, the dielectric traits of concrete and its components were acquired using an open-ended coaxial probe. Compared with the Brown model, complex refractive index method (CRIM) model, Looyenga model, and Rayleigh model (i.e., the classical models), the results showed that among overall predictions of concrete dielectric constants, the proposed model has the greatest accuracy, because the theoretical results matched the test results more closely. This indicates that the proposed model can better interpret the dielectric properties of concrete, which lays the groundwork for future research into the application of GPR to the nondestructive testing and evaluation of concrete pavement.

Testing and Evaluating the High-Temperature Rutting Development of Steel Deck Asphalt Pavement Using Full-Scale Accelerated Pavement Testing

Abstract Steel deck asphalt pavement (SDAP) significantly differs from traditional pavement in terms of substructure and service environment, so its rutting development is unique. Indoor tests and the finite element method cannot competently simulate service environment and accurately show the rutting development of SDAP. In this study, the rutting development of SDAP was evaluated using full-scale accelerated pavement testing (APT). First, a steel bridge with two different pavement structures was constructed and a heating system was installed to keep the high temperature. Second, three indoor tests—dynamic stability (DS), hardness number (HN), and indentation—were conducted to quantify the rutting resistance of pavement materials, and the Mobile Load Simulator 66 executed the APT. The indoor results showed that DS and HN correlated well with the APT rut depth. With the increase of APT loading cycles, the rutting depth, the ratio of sag area to uplift area, and the ratio of sag area to full areas were increased. Meanwhile, the rutting depth changing speed and the ratio of uplift area to full areas declined with the loading. A rutting depth prediction model (RPM) was proposed based on the number of loading cycles and the initial rutting depth at 5,000 loading cycles. The RPM applied well to the previous study’s test data, indicating that rut development can be evaluated by its early status.

Validation of a Novel Sensing Approach for Continuous Pavement Monitoring Using Full-Scale APT Testing

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.

Modulus attenuation of semi-rigid asphalt pavement layer in accelerated pavement testing with MLS66

This research aimed to investigate the attenuation mode of the layer modulus of asphalt pavement in accelerated pavement testing (APT). A full-scale experimental section was constructed and tested using the APT facility. Two non-destructive testing (NDT) methods, named falling weight deflectometer (FWD) technique and portable seismic property analyzer (PSPA) test, were used to obtain the layer moduli of asphalt pavement during the APT test. The variation patterns of layer moduli obtained by FWD and PSPA tests were calculated and compared after the temperature was corrected to 20 ℃. It was found that the variation pattern of surface layer modulus based on field FWD measurements was consistent with the one measured from PSPA tests. That is the modulus of the surface layer increases with the APT load repetitions firstly and then decreases with the rise of the repetitions. The modulus values of the surface layer measured from PSPA tests are obviously larger than those backcalculated based on deflection basins. The ratio of the measured surface layer modulus based on the PSPA test to the backcalculated one based on the FWD test ranges between 2.06 and 2.71. The backcalculated base layer modulus always declines with the increasing loading repetitions. The attenuation patterns of the surface layer modulus and the base layer modulus in the damage stage are described as Ea=421100*N-0.6119 and Eb=128000*N-0.1096, respectively.

Evaluation of the Effect of Interlayer Bonding Condition on the Deterioration of Asphalt Pavement

In this study, to investigate the effect of interlayer bonding condition on the deterioration of asphalt pavement, we conducted numerical analysis based on multi-layer elastic theory, verification of numerical analysis results by experiments on a full-scale test track, and comparison of the progress of deterioration based on accelerated loading tests conducted on the test pavement. Firstly, the analysis indicated that stronger tensile strain could occur on the upper side of the interlayer when the interlayer bonding weakens. Furthermore, the test pavement simulating interlayer debonding showed the same strain tendency as the analysis. These results suggest that if the interlayer debonding continues, the pavement may suffer early fatigue failure. On the other hand, there are some results that are not consistent with those of the analysis, such as the magnitude of deflection in the test pavement; thus, further investigation is required. Furthermore, because of the accelerated loading on the test pavement, the sections where the layers were debonded immediately after construction became bonded. Since the deflection of the test pavement did not change significantly from that immediately after construction, and no conspicuous damage such as cracks was observed on the road surface, it is considered necessary to continue follow-up surveys.

Pixel-Level patch detection from full-scale asphalt pavement images based on deep learning

ABSTRACT The patch recognition and localisation from high-resolution pavement images play a key role in evaluating asphalt pavement condition. To achieve the purpose, two line-scan cameras were employed to acquire lots of full-scale asphalt pavement images with 1 mm2 per pixel resolution in the field. The raw images were pre-processed to construct the dataset, including 984 patches of various shapes in 827 pavement images. Subsequently, the YOLO series models innovatively applied in automatically detecting the patches from pavement images were well trained with the pavement training and validation sets. The testing results reveal that the F1-score and mAP@0.5 values of the YOLOv4 model are 0.911 and 92.92% respectively in the test set. The comprehensive recognition accuracy of the YOLOv4 model outperforms the other YOLO models. And the detection speed of the YOLOv4 model for pavement data video is 38.1 frames per second. Additionally, all the features of patches in the partial testing pavement images, no matter whether they are strip, block, intersection, blurry within the shadow, or background noise, can be correctly predictedby the rectangular box. The average localisation errors of the width and height of the predicted patches are 21 and 16 mm respectively, and the average IoU is 0.83.

Toward a Comprehensive Pavement Reliability Analysis Approach

Reliability has been incorporated in pavement design tools to account for input variability influence on predicted performance. As they are not based on a probabilistic method of uncertainty propagation, the reliability analysis methodologies that are currently implemented in pavement performance tools lack rigor and robustness. This paper investigates the potential of three reliability analysis methodologies for pavement application: the Pavement ME reliability analysis methodology, Monte Carlo simulation (MCS), and the first-order reliability method (FORM). The MCS and FORM involve a response surface method for the generation of a second-order surrogate model. The investigation was performed using inputs and performance data from accelerated pavement testing structures. Inputs that were identified as significant were characterized as random variables and their associated variability was established using measured structural and material properties. Pavement performance with respect to rutting was predicted using the ERAPave performance prediction tool, while MCS was used to generate the actual variability of the distress. The reliability analysis results have shown that a comprehensive reliability analysis methodology is required that effectively captures input variabilities and the error associated with surrogate models.

A prediction model of the friction coefficient of asphalt pavement considering traffic volume and road surface characteristics

ABSTRACT In order to build an appropriate prediction model of pavement friction coefficient attenuation, the effects of aggregate texture, on a basis of texture scanning and pavement testing of the dynamic friction coefficient, traffic volume, and rock characteristics on pavement slide decay resistance were studied, and the accuracy of the developed prediction model of friction coefficient was thereafter compared and analysed. First, based on the tire-pavement dynamic friction analyzer independently developed by the research group, accelerated loading tests under different traffic conditions were conducted by the orthogonal test method, and the dynamic friction coefficient of pavement during the abrasion process was recorded. At the same time, the non-contact laser profile measurement system was used to collect three-dimensional micro-texture data of coarse aggregate surface at different abrasion stages, and characteristic parameters such as height, wavelength, and shape were calculated. Second, the factors such as traffic volume and rock characteristics were incorporated, and the algorithms such as multiple linear stepwise regression, feedforward neural network, and random forest regression were adopted to develop estimation models of pavement friction. The results showed that the model constructed based on only texture feature parameters poorly represented pavement skid resistance. After introducing the two indicators of total traffic volume and rock type, the correlation coefficients of the model significantly improved, reaching 0.63, 0.58, and 0.82 respectively, indicating that the latter two factors have significant effects on the anti-skid attenuation of pavement. Moreover, the above three models can achieve satisfactory prediction results. In addition, when a large sample size can be obtained, a random forest algorithm is recommended to acquire higher prediction accuracy of pavement friction coefficient attenuation.

Using Bender Elements to Investigate the Effect of a Multi-axial Geogrid on Aggregate Layer Stiffness

Geogrid inclusion in low-volume road applications has been shown to improve rutting performance. Although rutting performance and traditional instrumentation response data provide meaningful insights into pavement behavior, they do not provide a direct measurement of material behavior near the geogrid. Recently, the University of Illinois at Urbana-Champaign has developed field-deployable shear-wave transducers, that is, bender elements to measure the localized stiffness enhancement attributed to geogrid inclusion in unbound aggregate layers, which have been verified in laboratory and limited field applications. Bender element sensors were installed in a full-scale accelerated pavement test experiment at the U.S. Army Engineer Research and Development Center that included a recently developed multi-axial hexagonal geogrid. The sensors were installed directly on the geogrid and 10 cm above the geogrid in the unbound aggregate layer to measure the geogrid zone of influence. The test item was trafficked with a dual-wheel truck gear, and bender element measurements were made at select intervals throughout traffic application. To estimate the unbound aggregate moduli, the data were analyzed using two techniques: first arrival time and peak-to-peak arrival time. The bender element sensors were found to be sufficiently robust to survive installation, construction, and trafficking. Calculated modulus values 10 cm above the geogrid were found to be higher than those directly on the geogrid, which was an unexpected finding. However, a forensic investigation revealed moisture and fines migration into the bottom of the unbound aggregate layer, which could have dampened the shear-wave signal, resulting in lower calculated modulus values.

Three-Dimensional Modeling and Analysis of Virtual Test Pavements for Automotive Test Sites

The three-dimensional modeling and analysis of virtual pavement are conducted for the complex and diverse test pavements in the vehicle reliability driving test. Firstly, a complete pavement unevenness acquisition system is constructed, and secondly, the classification and processing methods of different pavements are determined according to the actual working conditions and load spectrum data of the test pavement in the test site to provide a theoretical basis for the virtual pavement modeling. At the same time, considering that the test pavement also includes various complex pavement conditions such as curves, slopes and lateral inclination, the structural composition and differences of plane curves, longitudinal ramps and cross-sections are analyzed to provide a guarantee for the further establishment of an accurate 3D pavement model. Finally, the 3D virtual pavement model of typical test pavements is established according to the theory and methods of the above research, and the accuracy of random pavement reconstruction is analyzed, which provides accurate and effective technical support for the simulation of automobile reliability driving tests.

Rutting Predictions in Flexible Airfield Pavements Using a Newly Modified Drucker–Prager Combined Hardening Model with Progressively Evolving Yield Surface

Granular materials subjected to compressive cyclic loading of constant amplitude when modeled using an isotropic hardening, elastoplastic Drucker-Prager (DP) model do not accumulate significant plastic strain beyond the first loading cycle. This can lead to predictions of permanent deformations in airfield asphalt pavements that do not compare well with experimental observations. Accordingly, this paper proposes a modification to the existing DP model as a means of accounting for this shortcoming. A new parameter, γ, is introduced, which causes the yield surface to progressively evolve and shrink in size during successive load cycles to account for microphysical restructuring of the granular material during constant amplitude cyclic loading. The constitutive model is implemented within the finite element code ABAQUS through the user material subroutine UMAT. The model is subsequently tested via 2D finite element analysis on pavement sections by applying it to the granular base and modeling all other layers as linear elastic. Validation is undertaken by comparing rutting with field experimental values from an accelerated pavement testing program. Increased equivalent plastic strain and vertical plastic strains are observed with this modification, thereby resulting in rutting values that are significantly closer to the experimental data than those obtained with the classical DP model.

Finite Element modeling of force amplification at the spindle due to a tire's cavity mode: experimental verification

Reduction of tire-road noise is an important issue when developing luxury cars and electric vehicles. In this context, the air-cavity mode is an important source of spindle forces transmitted to the suspension that then increase interior noise levels. When a tire rotates, the cavity mode near 200 Hz splits into two adjacent modes due to a Doppler effect and tire deformation. That split can lead to increased levels of both longitudinal and vertical spindle forces at the spindle since the two acoustic modes each contribute to both forces when the tire rotates. Thus, it is important to develop tools to identify the contributions of the split air-cavity modes to the spindle force. A FE simulation of the spindle force for a steady-state rolling has been verified by a comparison with laboratory test results obtained by using a wheel-force transducer mounted on Purdue's Tire Pavement Test Apparatus. It was observed that the frequency split expands as the rotation speed increases and that the vertical spindle force increases when aligned with an odd-numbered circumferential structural mode. By using the correlated simulation model, parametric studies have been carried out focused on minimizing the vertical spindle force due to air-cavity mode near 200 Hz.

Self-powered weigh-in-motion system combining vibration energy harvesting and self-sensing composite pavements

Overloaded vehicles are the primary cause of accelerated degradation of road infrastructures. In this context, although weigh-in-motion (WIM) systems are most efficient to enforce weight regulations, current technologies require costly investments limiting their extensive implementation. Recent advances in multifunctional composites enabled cost-efficient alternatives in the form of smart pavements. Nevertheless, the need for a stable power supply still represents a major practical limitation. This work presents a novel proof-of-concept self-sustainable WIM technology combining smart pavements and vibration-based energy harvesting (EH). The feasibility of piezoelectric bimorph cantilevered beams to harvest traffic-induced vibrations is firstly investigated, followed by the demonstration of the proposed technology under laboratory conditions. The main original contributions of this work comprise (i) the development of a new self-powered data acquisition system, (ii) a novel approach for the fabrication and electromechanical testing of the piezoresistive composite pavement, and (iii) laboratory feasibility analysis of the developed EH unit to conduct traffic load identification through electrical resistivity measurements of the smart pavement. While the presented results conclude the need for dense EH networks or combinations of different EH technologies to attain complete self-sustainability, this work represents an initial feasibility evidence paving the way towards the development of self-powered low-cost WIM systems.

Investigation of factors affecting bond strength of thin epoxy overlay pavements

ABSTRACT This study investigated the factors influencing the bond strength between existing pavement and thin epoxy overlay via laboratory and field tests. Laboratory tests revealed that the highest bond strength occurred at a general temperature and relative humidity of 25 °C and 60%, respectively. The concrete surface profile (CSP) of the specimen was found to have no impact on the bond strength between the latex-modified concrete (LMC) and epoxy binder under favorable conditions. Field testing of well-maintained concrete pavements indicated that neither the existing pavement's pre-treatment process nor its resulting CSP affected the bond strength. However, the bond strength decreased with environmental and vehicle loadings six months after the overlay. A second field test conducted on a deteriorated bridge deck with an LMC pavement revealed that most of the damage occurred at a shallow depth in the existing pavement, demonstrating low bond strength just two days after construction owing to inadequate removal of the deteriorated section. The initially low bond strength increased over time as the epoxy binder permeated the deteriorated area and hardened at greater depths, thus making it necessary to thoroughly remove all deteriorated sections of the existing pavement before constructing a thin epoxy overlay.

Shear failure investigation of unbound pavement layers under accelerated heavy aircraft loading: case study

ABSTRACT Airport pavements are designed to sustain aircraft-induced stresses while maintaining adequate serviceability. To enhance pavement design procedures, the Federal Aviation Administration (FAA) utilises the National Airport Pavement Test Facility (NAPTF) to perform full-scale tests of airfield pavements under representative tire loads. Due to higher tire loads and pressures, unbound layers may experience shear failure and excessive permanent deformation. In this study, a new methodology is proposed to estimate the shear strength parameters of unbound materials by extrapolating resilient modulus test data until failure by adopting the hyperbolic stress–strain constitutive soil model. Using those shear strength parameters of unbound pavement layers of the NAPTF Construction Cycle 3 (CC3), a comprehensive simulation was performed using a modified version of SuperPACK (Super Heavy Load Pavement Analysis PACKage), developed based on the Federal Highway Administration (FHWA) Analysis Methodology for superheavy load (SHL). Pavement behaviour and performance were investigated for four FAA flexible pavement configurations tested mainly to study the role of different subbase thicknesses. It was found that modified SuperPACK was able to reasonably predict the shear failure mechanism in the different unbound layers existing in CC3.

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

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

Editorial: “Functional pavement and advanced material testing technology”

Editorial: “Functional pavement and advanced material testing technology”

Investigating the Deterioration of Pavement Skid Resistance Using an Accelerated Pavement Test

Stone matrix asphalt (SMA) mixture has been widely used in pavement engineering for its preferable in-service performance. However, deterioration of SMA pavement in skid resistance is apparent under traffic loading. There remains lacking attention on skid resistance attenuation of SMA pavement, which in turn is important for skid durability design in practice. Hence, this study aims to perform a thorough investigation to reveal the skid resistance attenuation law of SMA pavement. Multiple types of SMA-13 mixtures prepared by different material designs were selected to conduct a kneading test that simulates real surface states of in-service SMA pavement. Pressure-sensitive film and a 3D laser scanner were utilized for evaluating anti-skid performance and skid durability. The finite element (FE) method is introduced to simulate vehicle braking distance for skid-resistance evaluation. The results show that skid resistance attenuation of SMA pavement consists of two stages: In the first stage, the skid resistance of SMA pavement experiences a short enhancement, followed by a long-term weakening stage. Abundant surface texture of SMA helps to mitigate the impact of traffic load on skid resistance. The FE analysis and pressure-sensitive film results demonstrate the potential of skid durability design of SMA pavement based on the skid resistance attenuation law.