Prediction of Layer Moduli from Falling Weight Deflectometer and Surface Wave Measurements Using Artificial Neural Network

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.

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'.

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.

Decisions regarding the selection and implementation of appropriate pavement rehabilitation methods is usually based on pavement functional and structural condition data. Visual distress surveys and Falling Weight Deflectometer (FWD) testing are often carried out by agencies as parts of their pavement preservation programmes. Although backcalculation of individual layer moduli from FWD data is a common approach to assess a pavement’s structural condition, the accuracy of this approach is largely dependent on exact estimates of individual layer thicknesses. Coring operations to determine pavement layer thicknesses require significant time and resource commitments, and hence cannot always be accommodated within an agencies’ operational constraints. Accordingly, alternative analysis methods to assess the pavement’s structural condition from FWD data are often desired. An ongoing research study at Boise State University is focusing on combined usage of pavement structural and functional evaluation data for making pavement rehabilitation decisions. Considering the lack of pavement layer thickness information for all locations, this study is using Deflection Basin Parameters (DBPs) calculated from FWD test data to make inferences regarding the structural condition of individual pavement layers. This manuscript presents findings from this study, and establishes DBPs as reasonable alternatives to be used in network-level pavement condition evaluation practices. The adequacy of DBPs to assess the structural condition of individual pavement layers was first assessed through Finite-Element Modelling. A series of analyses were performed by assigning typical modulus values to individual pavement layers, and the corresponding DBPs were calculated. The calculated DBP values mostly fell within typical ranges specified in the literature for different layer conditions. Once the DBPs were established as adequate alternatives for making network-level pavement assessment decisions, four selected pavement sections in the state of Idaho were analyzed based on this method, and the results were compared against those obtained from visual distress assessment routines.

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

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

The primary objective of this research was to develop a model to accurately predict the modulus of flexible pavement layers from surface deflections measured using the falling weight deflectometer (FWD) device. To do so, a synthetic dataset consisting of 10,000 flexible pavements was created using the layered elastic theory. The developed dataset contained the moduli values for different pavement sections, and deflections at known distances from the load center. Next, the moduli of different asphalt pavement layers consisting of a surface course, base course, and subgrade were calculated using the Artificial Neural Network (ANN) methodology through backcalculation. The inputs for the neural network are thicknesses and deflection values at seven distances from the load center. The outputs are the moduli for different layers. The optimum neural network consists of two hidden layers and has a general architecture of 9-36-18-3. The transfer function is sigmoid for hidden layers and linear for the output layer. Results indicate that the ANN model can predict the modulus of different layers accurately with a coefficient of determination (R2) of more than 0.999 in all cases. For validation, the results from the developed model were compared with typical backcalculation software of ISSEM4, MODCOMP, MODULUS, WESDEF, and BAKFAA as well as 386 LTPP sections. Moreover, an analysis was conducted to assess the contribution of inputs in moduli prediction for each of the pavement layers using the Garson algorithm and the Connection Weight methods. It was concluded that the results from the latter method are in better agreement with the theories. Finally, the significance of different input variables was assessed using an indirect method by excluding each from the analysis and checking of the model predictability power. It was concluded that in order of significance, the base layer thickness, 1st geophone deflection, and the AC layer thickness are the variables that their exclusion causes large errors.

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.

The Falling Weight Deflectometer (FWD) is one of the most important non-destructive tests (NDT) used for pavement structural evaluation based on deflection measurements. Inverse analysis of FWD measurements—back-calculation—is a common process for obtaining the pavement layer moduli, including the subgrade soil, which are useful for estimating remaining pavement life for overlay design purposes. Based on a proficiency test (PT) involving testing devices from different manufacturers, the objective of this study is to analyze the influence of FWD uncertainty on the back-calculated flexible pavement and foundation layers moduli. A parametric study was performed considering the back-analysis of different asphalt pavements with regard to layer thickness and resilient modulus (unbound granular and asphalt concrete layers, including subgrade). It was concluded, in general, that FWD uncertainty could have an important influence on the layers modulus obtained from back-calculations. This influence was less significant in the case of asphalt concrete layers (±25%). The variation in resilient modulus was greater in the layers of the subgrade soil (−33 to +83%) and the unbound granular materials (−42 to +80%).

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

This paper describes the use of artificial neural networks (ANNs) as pavement structural analysis tools for the rapid and accurate prediction of critical responses and deflection profiles of full-depth flexible pavements subjected to typical highway loadings. The ILLI-PAVE finite element program, extensively tested and validated for over three decades, was used as an advanced structural model for solving the critical responses of full-depth flexible pavements. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, nonlinear stress-dependent subgrade soil response models were used in the ILLI-PAVE program to account for the softening nature of fine-grained subgrade soils under increasing stress states. ANN models then trained with the results from the ILLI-PAVE solutions have been found to be viable alternatives. The trained forward calculation ANN models were capable of predicting asphalt tensile strains and subgrade compressive stresses/strains with low average absolute errors of those obtained directly from the ILLI-PAVE analyses. ANN backcalculation models developed in this study were also capable of successfully predicting the pavement layer moduli from the falling weight deflectometer (FWD) deflection basins and they may be used in the field for rapidly assessing the condition of pavement sections during the FWD testing.

In recent years, artificial neural networks have successfully been trained to backcalculate pavement layer moduli from the results of falling weight deflectometer (FWD) tests. These neural networks provide the same solutions as existing programs, only thousands of times faster. Unfortunately, their use is constrained to the test conditions assumed during network training. These limitations arise from practical aspects of neural network training and cannot be circumvented easily. The goal of this research was to develop a backcalculation program combining the speed of neural networks and the flexibility of conventional programs to produce the same solutions as existing programs. This was accomplished by forgoing neural network backcalculation in favor of neural network forward-calculation, that is, using neural networks in place of complex numerical models for computing the forward-problem solutions used by the conventional backcalculation programs. A suite of neural networks, covering a range of flexible pavement structures, was trained using data generated by WESLEA, the forward-problem solver used in the WESDEF backcalculation program. When tested on 110 experimental FWD results, a version of WESDEF augmented by the neural networks provided statistically identical answers 42 times faster, on average, than the original. Provisions have been made for periodic upgrades as additional networks are trained for other pavement types and test conditions. Meanwhile, the original WESLEA can still be used when an appropriate network is unavailable. This preserves the flexibility of the original program while taking maximum advantage of the speed gains afforded by the neural networks.

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.

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.

This paper presents a backcalculation method for pavement layer elastic modulus and thickness. The effect of deflection measurement errors on the backcalculated results is also considered. The falling weight deflectometer (FWD) data are generated by applying a load to the pavement while calculating deflection at various fixed distances from the load centre. The measurement errors in FWD data are simulated by perturbing the theoretical deflections. Using these data, a backcalculation technique based on the improved genetic algorithm is proposed. In order to deal with the measurement errors, besides the common root mean square, a new objective function called area value with correction factor is introduced to the backcalculation algorithm. Numerical examples for two- and four-layer pavement structures are presented, which show the capability of the proposed method in backcalculation of pavement layer modulus and thickness.