Temperature effect on flexural bowl determined by falling weight deflectometer testing

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

A pavement temperature profile is indispensable for analyzing and interpreting falling weight deflectometer (FWD) testing data. This paper presents a simple analytical approach to rapidly predicting the transient temperature profile within the asphalt layer of a flexible pavement on the basis of measured surface temperatures during FWD testing. The proposed analytical approach consists of the classical solution of a one-dimensional heat equation in a homogeneous half-space subjected to specified surface temperatures and a Gaussian-quadrature numerical scheme to approximate the classical solution. Model validation was conducted using a large temperature database. Comparisons between predicted and measured pavement subsurface temperatures suggest that the proposed simplified analytical method generated a reasonable asphalt temperature profile during the short period of FWD testing. The main advantage of the proposed method is that it can be easily applied to assist field engineers in rapidly estimating the temperature profile within the asphalt layer of a multilayered flexible pavement based on measured pavement surface temperatures during FWD testing.

Using Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) to monitor the effects of seasonal moisture variation on the structural capacity of pavements

This paper presents a simple method to determine dynamic modulus master curve of asphalt layers by con­ducting Falling Weight Deflectometer (FWD) for use in mechanistic-empirical rehabilitation. Ten new and rehabilitated in-service asphalt pavements with different physical characteristics were selected in Khuzestan and Kerman provinces in south of Iran. FWD testing was conducted on these pavements and core samples were taken. Witczak prediction model was used to predict dynamic modulus master curves from mix volumetric properties as well as the bitumen viscosity characteristics. Adjustments were made using FWD results and the in-situ dynamic modulus master curves were ob­tained. In order to evaluate the efficiency of the proposed method, the results were compared with those obtained by us­ing the developed procedure of the state-of-the-practice, Mechanistic-Empirical Pavement Design Guide (MEPDG). Re­sults showed the proposed method has several advantages over MEPDG including: (1) simplicity in directly constructing in-situ dynamic modulus master curve; (2) developing in-situ master curve in the same trend with the main predicted one; (3) covering the large differences between in-situ and predicted master curve in high frequencies; and (4) the value obtained for the in-situ dynamic modulus is the same as the value measured by the FWD for a corresponding frequency.

A condition assessment procedure for pavement layers that uses multiload-level falling weight deflectometer (FWD) deflections is presented. A dynamic finite element program that incorporates a stress-dependent soil model was used to generate the synthetic deflection database. On the basis of the data in this database, the relationships between surface deflections and critical pavement responses, such as the stresses and strains in each layer, have been established. The FWD deflection data, distress survey results, temperature, and laboratory testing results used to develop this procedure were collected from the Long-Term Pavement Performance project database. Research efforts also focused on the effect of the FWD load level on the condition assessment procedure. The results indicate that the proposed procedure can estimate the asphalt concrete (AC), base, and subgrade layer conditions. The AC layer modulus and the tensile strain at the bottom of the AC layer were found to be better indicators of the condition of the AC layer than the deflection basin parameter. It was also found that the structurally adjusted base damage index and base curvature index were good indicators for prediction of the stiffness characteristics of the aggregate base and subgrade, respectively. An FWD test with a load of 71.2 kN or less does not improve the accuracy of this procedure. The results from the study of the nonlinear behavior of a pavement structure indicate that the deflection ratio obtained from multiload-level deflections can predict the type and quality of the base and subgrade materials.

Influence of bedrock on viscoelastic responses and parametric back-calculation results for asphalt pavements and prediction of bedrock depth under FWD tests

A new algorithm for predicting layer moduli using measurements from both falling weight deflectometer (FWD) and surface wave tests is presented. This algorithm employs numerical solutions of a multilayered half-space based on Hankel transforms as a forward model and an artificial neural network (ANN) for the inversion process. Phase velocities for frequencies ranging from 10 Hz to 10,000 Hz are calculated using the forward model for varying pavement structures with a range of layer moduli and thicknesses. These phase velocities, along with the layer moduli and thicknesses, are used to train an ANN to backcalculate layer moduli from dispersion curves (i.e., phase velocity versus frequency curves) constructed from the FWD and stress wave test data. To account for the effect of bedrock on the moduli prediction, another network is trained with layer thicknesses and phase velocities for predicting the depth to bedrock. Combining this network with the network for the moduli prediction results in a sequential dispersion analysis technique in which the depth to bedrock predicted from the first network becomes an input to the second network for predicting layer moduli. FWD and stress wave test measurements from an intact pavement and an asphalt overlay over cracked asphalt layer are processed using the sequential dispersion analysis technique and MODULUS 5.0 backcalculation program. Comparison of the results indicates that the dispersion analysis technique yields less variable subgrade moduli and is more sensitive to changes in the asphalt surface layer, because the high-frequency data from the stress wave test is incorporated.

Falling weight deflectometer (FWD) data are generally processed through backcalculation software for computing layer modulus of airport pavements. Though a number of backcalculation software are currently available, it is not known that software produces the most consistent and accurate results. Inconsistent and inaccurate backcalculation software can take a significant toll on the overlay and rehabilitation design of airport pavements as modulus being the inputs for such design calculations. Most backcalculation software requires an initial value of modulus, known as seed modulus, to initiate simulations. Unfortunately, in many of the software, the outputs vary with the initial choice of seed modulus (consistency). In addition, depending on the software algorithm, the final output differs from the laboratory measured value of modulus (accuracy). In this study, three backcalculation software programs are evaluated for consistency and accuracy. They are BAKFAA, MODULUS and EVERCALC. The major goal is to determine the most consistent and accurate software for determining layer modulus of airport pavements in New Mexico. Consistency of software is determined by comparing outputs from three sets of input data from FWD tests at identical locations with three magnitudes of loads: 40·03, 53·38 and 71·17 kN. Laboratory tests are conducted for modulus and tensile strength of asphalt and soil samples collected from the same locations of FWD tests. Software accuracy is determined based on the distribution and magnitude of the variance of laboratory and FWD modulus. Laboratory tensile stress is also compared with the backcalculated tensile stress using KENLAYER, a multilayer elastic analysis software. Study results show that EVERCALC software produces more consistent and accurate modulus values than the BAKFAA and MODULUS software. Therefore, EVERCALC is recommended for analyzing layer modulus of airport pavements.

Seasonal and diurnal variations can significantly impact Falling Weight Deflectometer (FWD) measurements and, thus, any deflection-based parameter such as slab elastic modulus (Epcc ), modulus of subgrade reaction (k), and load transfer efficiency (LTE). Also, general guidelines for the FWD measurements are needed to help agencies better interpret the derived parameters. Long-Term Pavement Performance (LTPP) Seasonal Monitoring Program (SMP) study data for Jointed Plain Concrete Pavements (JPCP) were analyzed. The results showed that seasonal and diurnal changes impact the derived parameters for the rigid pavements in different climatic regions. PCC slab’s upward curling and full contact with the underlying layer at lower temperatures resulted in higher Epcc and k-values and vice versa. Trends seen for k-values closely followed those for moduli values. Therefore, any guidelines concerning FWD testing should consider PCC layer temperature, which affects its shape at testing time. Also, LTE was found higher at elevated temperatures due to locked joints. Relating testing temperatures with observed trends of parameters helped determine general guidelines for FWD testing. The preferred temperature ranges for FWD testing are 55 to 70°F and 60 to 75ᵒF in freeze and non-freeze regions respectively; testing before noon is recommended in spring and fall seasons in all climates.

Seasonal and diurnal variations can significantly impact falling weight deflectometer (FWD) measurements and, thus, deflection-based parameters such as back-calculated layer moduli. Therefore, it is necessary to evaluate and quantify these effects. The back-calculated layer moduli data from the Long-Term Pavement Performance Seasonal Monitoring Program study were analyzed within each climatic region. The effects of seasonal and diurnal changes were quantified and related to ambient temperatures were related to the development of general measurement guidelines. The HMA layer moduli showed minimal variation during the spring and fall seasons in every climatic region and were consistently higher based on the deflections measured before noon. However, temperature correction, generally applied to the HMA layer moduli, can eliminate the temperature impacts of the FWD measurements. Because the unbound layer moduli are back-calculated from the single measured deflection basins on the surface, temperature and moisture conditions at the time of measurements can affect the material properties. The base and subgrade moduli showed minimal variation within the temperature ranges of the spring and fall seasons. Therefore, such temperature within each climatic region may result in layer moduli values closest to their representative in-field conditions. In addition, the unbound layer showed no effect of time within a day, suggesting that FWD testing can be conducted at any time during the day. Based on the results, the preferred temperature ranges for FWD measurements are 55–70 °F and 65–75 °F in the freeze and non-freeze regions, respectively. FWD measurements can be conducted at any time during the day. The preferred seasons for testing are spring and fall in all climatic regions.

This study aims to investigate the extreme temperature variations of asphalt pavement structures. First, this study established a thermal analysis model using finite element methods to investigate the temperature profile of asphalt pavements. Thereafter, the significant factors affecting the pavement temperature were determined using the grey correlation method. Finally, linear prediction models for maximum, minimum, and average temperatures of asphalt layers at any depth were developed. The results indicated that the pavement and air temperature change trends were similar, and that the temperature decreased with increasing depth. The maximum temperature occurred on the road surface during the daytime and moved from the asphalt layer to the base layer during the night. The maximum temperature difference on the road surface was 26.8 °C. The most significant factors affecting the asphalt pavement temperature were air temperature and solar radiation. The maximum, minimum, and average temperatures of the asphalt layers had good linear relationships with air temperature, solar radiation, and depth of concern, and the R 2 of these predicted models were all greater than 0.94. In addition, a comparison between the predicted and actual measured pavement temperature of Beijing’s RIOHTRACK road in 2019 found that the R 2 between the measured and predicted values was greater than 0.90 and the root-mean-squared error was less than 5 °C. Using this model, the maximum, minimum, and average temperatures of the asphalt layer at any depth can be determined if the air temperature is known. These results will be beneficial for analyzing the stresses and strains resulting from temperature differences and calibrating the structural design methods for asphalt pavements.

A systematic review of structural design methods and nondestructive tests for airport pavements

Seasonal variations and in situ assessment of concrete pavement foundation mechanistic properties

Falling weight deflectometer (FWD) tests are performed worldwide for assessing the health of pavement structures. Interpretation of FWD-measured surface deflections turns out to be challenging because the behavior of pavement structures is temperature-dependent. In order to investigate the influence of temperature on the overall pavement performance and on the stiffness of individual layers, temperature sensors, asphalt strain gauges, and accelerometers were installed into one rigid (concrete) and two flexible (asphalt) pavement structures, mostly at layer interfaces. Three different methods for installation of the strain gauges are compared. From correspondingly gained experience, it is recommended to install a steel dummy as a place-holder into the surface of hot asphalt layers, immediately after their construction and right before their compaction, and to replace the dummy with the actual sensor right before the installation of the next layer. Concerning the first data obtained from dynamic testing at the field-testing sites, FWD tests performed at different temperatures deliver, as expected, different surface deflections. As for the rigid pavement, sledgehammer strokes onto a metal plate, transmitted to the pavement via a rubber pad, yield accelerometer readings that allow for detection of curling (=temperature-gradient-induced partial loss of contact of the concrete slab from lower layers). In the absence of curling, the here-proposed sledgehammer tests yield accelerometer readings that allow for quantification of the runtime of longitudinal waves through asphalt, cement-stabilized, and unbound layers, such that their stiffness can be quantified using the theory of elastic wave propagation through isotropic media.

Frequent flooding and wet weather events are the challenges for managing road infrastructure. In this study, falling weight deflectometer (FWD) equipment is employed to investigate the effect of moisture consequence of January 2011 flood at Queensland, Australia on a flooded road. The impacts of the flood on the structural strength were obtained for the whole section as well as different layers individually. These two sets of data provide a clear picture of the road condition as sound, severe or warning before and after the maintenance of the road. Furthermore, the moisture content of the spotted road at field condition providing the structural stiffness was obtained with a correlation of FWD data. These analysis and investigations play an essential role in optimizing the maintenance method for the roads suffering from the flood.