Artificial neural network back-calculation of flexible pavements with sensitivity analysis using Garson’s and connection weights algorithms

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%).

Six pavement sections were tested using a falling weight deflectometer (FWD). The six pavement sections were all in the Phoenix, AZ area. All six pavements have the same pavement structural cross-section: 3 to 4 in. (76.200–101.60 mm) of hot mix asphalt concrete surface supported by 10 to 13 in. (254.00–330.20 mm) of aggregate base course and underlain by a granular subgrade. Three of the six pavements possess aggregate base courses which have been stabilized with 1 % hydrated lime. The other three pavements incorporate the same aggregate base course but without lime stabilization. Resilient moduli of all pavement layers were back-calculated based on the FWD data. The moduli of all pavement layers are analyzed statistically. The addition of the same percentage of lime is shown to make a substantial difference in the stiffness of the layers and the structural contribution of the base layer to the pavement system. The effect of the stabilization with small percentages of lime on pavement performance and pavement life is discussed.

The light-weight deflectometer presents significant potential for compaction quality control and for determining in situ resilient modulus of pavement layers during construction. Data collected from a laboratory large-scale model experiment and two light-weight deflectometer devices (Keros and Zorn) were used to develop a means of estimating the resilient modulus of various base course materials. The procedure can be used for validating the input parameters in design. Measured surface elastic deflections of a layered pavement system from the LWD devices must be corrected for the effect of subgrade deflection and the differences in the state of stresses. A power function represents the relationship between the corrected elastic deflection from the light-weight deflectometers and the resilient modulus of the base course layer from the large-scale model experiment. This study provides a basis for use of the light-weight deflectometer in estimating as-constructed resilient modulus of a base course layer involving natural and recycled unbound materials as well as fly ash stabilised materials.

Laboratory evaluation of an indigenous developed light weight deflectometer as a quality control tool for pavement construction

This report presents the results of an investigation on the effectiveness of glass fiber grids as a reinforcement of the asphalt base layer in a flexible pavement. The study involved both laboratory experimental work and computer analysis of pavement sections. As part of the experimental work, twenty flexible pavement sections (with and without glass fiber grids) were constructed and tested in the laboratory. The laboratory-scale pavement sections were instrumented with pressure cells, displacement gages, and strain gages. Test sections were subjected to 1,000,000 load applications at a frequency of 1.2 Hz. Static loading tests were conducted at intervals of 100,000 load applications. In thirteen experiments, the glass fiber grid was used in the asphalt base layer. In five experiments, with and without reinforcement, a crack was simulated in the hot mix asphalt (thickness of 3-inch (76 mm)) base layer. A series of computer analyses was performed to analyze flexible pavement sections using the KENLAYER (Huang (1993)) computer program and the Finite Element Method (FEM). The laboratory data were compared with results from the computer analysis. Observations on doubly reinforced cases indicate that the vertical stresses in the subgrade is lower in pavement sections with stronger glass grids. In terms of cumulative displacement, results indicate that a 6-inch (152 mm) thick non-reinforced hot-mix asphalt base layer does not improve the performance of the pavement system in comparison to a 3-inch (76 mm) thick reinforced asphalt section. Observations on thinner pavement sections indicate that the increase in vertical subgrade stress caused by a simulated crack is offset by the decrease in vertical subgrade stress due to the reinforcement of the pavement section. The results show that the glass fiber grid used in this study improved the pavement performance. It was also observed that the inclusion of glass fiber grid in the HMA layer provided resistance to crack propagation. Overall, the flexible pavement sections reinforced with glass fiber geogrids (in the asphalt base layer) showed better performance under laboratory test conditions.

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.

Finite Element Analysis of Flexible Pavement with Geogrids

The use of construction and demolition waste (CDW) aggregates in unbound road pavement layers is increasing. However, the lack of data on performance in the field has spurred this investigation into their in-situ properties. A lightweight deflectometer (LWD) is a fast and simulative testing device for estimating the elastic modulus of unbound pavement layers. A field test pit was built to run LWD measurements on an unbound subbase layer containing CDW aggregates compacted at different energy levels. To assess their stress-strain non-linear behaviour, several LWD drops were performed on the same location by varying (i) the loading mass, (ii) the drop height, and (iii) the plate diameter. A stress-hardening behaviour of in-situ CDW aggregates was observed, consistent with the stress dependent evolution of resilient modulus of granular material commonly recorded in laboratory tests. The LWD modulus was found to be dependent on the level of compaction energy but also sensitive to the mechanical response of the layer below. The outcomes at this test pit suggest it would be wise to consider the rational evaluation of the in-field stress dependent behaviour of unbound CDW granular materials at both the design stage and when devising quality acceptance procedures. Nonetheless, to have a comprehensive interpretation of LWD results a greater uniformity of material properties and a stronger control of construction procedures will be desired, especially when heterogenous materials such as CDW aggregates are investigated.

The performance of rigid pavement is greatly affected by the properties of the base/subbase and subgrade layer. However, the performance predicted by the AASHTOWare Pavement Mechanistic-Empirical (ME) Design shows low sensitivity to the properties of base and subgrade layers. In this study, a new set of modified models, that is, resilient modulus ( MR) and modulus of subgrade reaction ( k-value) are adopted to better reflect these layers’ influence on the overall performance. An artificial neural network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation data sets in the ANN model consist of 27,000 simulation cases with different combinations of pavement layer thickness, layer modulus, and slab-base interface bond ratio. Eight pavement section data are collected from the long-term pavement performance (LTPP) database and modeled using the FE software ISLAB2000 to evaluate the sensitivity of the modified MR model and k-values to pavement performance. The computational results indicate that the modified MR values have a higher sensitivity to water content in the base layer on fatigue cracking (bottom-up and top-down cracking) and the faulting performance of rigid pavements compared with the results using the Pavement ME design model. It is also observed that the k-values using the ANN model have a greater sensitivity to cracking and faulting performance as a result of changes in any bonded conditions (fully bonded, partially bonded, zero bonded), whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonded and zero bonded).

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 paper presents a modeling study based on finite element (FE) analysis to mechanistically evaluate flexible pavements constructed with quarry by-products (QB). Twelve pavement test sections, a control section, and 11 others incorporating QB as unbound subgrade replacement and chemically stabilized base/subbase applications were evaluated for field performance using accelerated pavement testing (APT). First, falling weight deflectometer (FWD) deflection basin parameters were calculated and compared with critical pavement responses to evaluate the structural adequacies of the QB pavement sections. The moduli of the constructed pavement layers were then backcalculated from the FWD deflections using the GT-PAVE FE analysis program with nonlinear and cross-anisotropic layer characterizations. For stabilized QB applications, the layer properties calculated from this analysis for the base/subbase layers were used to calculate critical pavement responses. The sections were compared using a response benefit parameter, defined as the ratio of maximum resilient surface deflection in a conventional pavement, as the control section, to that obtained for each section having a certain QB application in consideration. According to the results obtained from the APT sections and the mechanistic FE analyses, the measured and calculated FWD deflection basins were successfully matched with individual sensor errors not exceeding 5% for all 12 test sections. The calculated response benefits indicated significant advantages for using cement-stabilized QB applications over fly ash-stabilized QB applications and conventional flexible pavement sections. Considering the pavement structural response benefits and good performance trends observed, major cost benefits can be realized by routine use of these sustainable QB applications.

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.

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.

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of different percentages of RAP on resilient modulus of hot-mix asphalt (HMA). In this research, 216 resilient modulus tests were conducted for establishing the experimental dataset. Input variables for predicting resilient modulus were temperature, penetration grade of asphalt binder, loading frequency, change of asphalt binder content compared to optimum asphalt binder content and percentage of RAP. Results of modeling using feed-forward neural network (FFNN) and generalized regression neural network (GRNN) model were compared with the measured resilient modulus using two statistical indicators. Results showed that for FFNN model, the coefficient of determination between observed and predicted values of resilient modulus for training and testing sets were 0.993 and 0.981, respectively. These two values were 0.999 and 0.967 in case of GRNN. So, according to comparison of R2 for testing set, the accuracy of FFNN method was superior to GRNN method. Tests results and artificial neural network analysis showed that the temperature was the most effective parameter on the resilient modulus of HMA containing RAP materials. In addition by increasing RAP content, the resilient modulus of HMA increased.

Pavement strength evaluation is necessary to ensure the sustainability of airfield pavements. This study focuses on different methods of airfield pavement strength evaluation. These methods include the determination of tensile stress-strain at the bottom of Hot Mix Asphalt (HMA) layer, pavement cross-section adequacy and accumulated damage determination over the service life. To implement these steps, Falling Weight Deflectometer (FWD) test has been conducted on the airfields of selected airports all over New Mexico. Layer moduli have been backcalculated from the FWD test data by ELMOD. Laboratory indirect tensile test has been conducted on the samples collected from the field coring to measure the indirect tensile stress. In addition, a Finite Element Model (FEM) has been developed in ABAQUS to determine the tensile stress at the bottom of HMA layer. Pavement layer thickness adequacy has been investigated by FAARFIELD. KENLAYER has determined the accumulated damage over the service life. Remaining life has been determined from this accumulated damage. Pavement thickness adequacy investigation shows that most of the pavement sections do not have adequate thickness to withstand the load from aircraft gear. However, comparison between laboratory indirect tensile strength test and determination of tensile stress at the bottom of HMA layer by FEM as well as remaining life calculation show the pavement sections are in good condition.