Finite Element Analysis of Flexible Pavement with Geogrids

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.

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.

In many countries of the world, rigid and flexible pavements are widely used. Some of the external factors such as stresses and displacements play major role in the design of pavement layers such as coating, base and sub-base. Although empirical formulas and methods were used in the calculation and design of the pavement layers, complexity of today's transportation engineering demands effectiveness of the empirical formulas were diminished. Nowadays complex problems can be easily simulated and solved thanks to the higher analysis capabilities of the computer-aided softwares. In this study, the stress distributions and displacements were examined under traffic loads in rigid and flexible pavements with different coating layer thicknesses (30 mm, 50mm, 70mm, 100 mm, and 150 mm) by using finite element method. As a result, the vertical displacement in the flexible pavements were obtained as 5% higher than the vertical displacement in the rigid pavements. Based on the stress distribution results, the stress values of flexible pavements were 60% lower than the stress values of the rigid pavements. Moreover, It was determined that the stresses in the rigid pavements remain in the coating layer, while the stresses in the flexible pavements reach the base and sub-base. In addition, regression models have been developed to predict stress and displacements by using layer thicknesses. High correlation and determination coefficient values (> 0.90) were achieved based on the regression analysis both in flexible and rigid pavements.

Geogrids and geotextiles enhance the performance and design of flexible pavements by providing geosynthetic reinforcement to the pavement structure. The structural performance of a layered pavement system is improved through the use of geogrids and high-strength geotextiles as base and sub-base reinforcement. Based on the finite element methodology, a non-linear mechanistic model was developed and utilized to investigate the structural benefit provided by geosynthetic reinforcement. The non-linear mechanistic model was employed to compare responses predicted from isotropic and cross-anisotropic characterizations of the granular base layer in unreinforced and geosynthetic-reinforced conventional flexible pavements. Results indicated that the use of a high-stiffness geogrid reinforcement typically reduced critical pavement responses, computed due to traffic loading and used primarily in mechanistic-based pavement thickness design. Higher percentage reductions in critical pavement responses, and thus increased geogrid reinforcement benefits, were often realized from the cross-anisotropic base characterization. Another indicator of this benefit was the computed stiffening of the granular base and subgrade layers, which could be demonstrated by the small modulus increases in the vicinity of the geogrid reinforcement.

Flexible pavement utilises asphalt as a binding agent. The pavement layer transmits and distributes the weight from the road or flexible pavement uniformly onto the compacted subgrade. Damage to flexible pavement results from a confluence of multiple connected factors. The purpose of the study is to ascertain the thickness of flexible pavement layers and the various forms of flexible pavement. This research employs the 2017 Bina Marga approach, utilising predicted LHR data over the design life or LHRT, to ascertain the kind and thickness of flexible pavement layers, considering the CESA4 and CESA5 values. The 2044 LHRT value for Passenger Cars and Other Light Vehicles is 33,358.7; Class 5A is 6.2; Class 5B is 4.1; Class 6A is 17.1; Class 6B is 39.1; and Class 8 or non-motorized vehicles is 280.1. The CESA4 value derived from the LHRT value was 4021566.47, whereas the CESA5 value was 469755.421. The most preferred type and thickness of flexible pavement is asphalt with a granular foundation layer, namely AC – WC pavement type at 4 cm thickness, AC – BC at 6 cm thickness, and LFA Class A at 40 cm thickness.

Sound pavement design is important for improving pavement performance, but construction is equally critical. Because of variations in pavement construction, the as-constructed pavement layer thickness varies spatially within a pavement section, and the mean constructed pavement layer thickness often deviates from the designed values. The as-constructed pavement layer thicknesses are compared to their design values by using data from the newly constructed flexible pavement sections in the Long-Term Pavement Performance program. First, the distribution type of the mean layer thickness deviation was investigated, and a combined statistical test for skewness and kurtosis showed that the mean thickness deviations may be assumed to be normally distributed for a given layer type and target thickness. Typical thickness deviation summary statistics values were derived to estimate the extent of deviations from the design thicknesses. Second, the analysis showed that elevation thickness data differ from the core examination data. Furthermore, as-constructed layer thickness values were compared to their design values by looking into the percentage of the individual measurements falling into specified ranges from the design values and by using two-sided and one-sided t-tests to compare the mean constructed thicknesses and the design values. The analyses showed that the mean constructed layer thicknesses tend to be above the design value for the thinner layers and below the design value for the thicker layers for the same layer and material type. Results from the study will be useful as inputs for reliability-based design procedures.

Assessment of geogrids in gravel roads under cyclic loading

The main objective of this paper was to provide some useful statistical-based models for flexible pavement behavior. The results of a theoretical analysis, using the finite element (FE) program SAP was utilized in this statistical analysis study. The maximum surface deflection, the maximum horizontal tensile strain (t) at the bottom of AC layer and the maximum compressive strain (c) at the top of subgrade are the most commonly used criteria for flexible pavement design. They were used in this study as the basis for measuring the flexible pavement response. The Statistical Package for Social Sciences (SPSS) version 11.0 was used to relate these parameters to the basic flexible pavement layer properties.Clearly, the properties of the top layer, asphalt concrete (AC) layer, are the key to control the behavior of flexible pavements. Based on that, the models developed in this paper concerned with the thickness and modulus of AC layer. Three different AC thicknesses and eight different AC moduli were used to investigate the response of flexible pavement. Based on the theoretical and statistical work presented in this study, simple statistical-based models for the response of flexible pavements were developed. These models could be easily used by pavement engineers to predict the flexible pavement indicators; maximum surface deflection, maximum horizontal tensile strain at the bottom of asphalt concrete layer and maximum vertical compressive strain at the top of subgrade layer. The presented models emphasized that the thickness of asphalt concrete layer was the most important independent variable for the response of flexible pavement followed by the modulus of AC layer.

Undertaken in the field of design methods for flexible pavement, the present study focuses on the introduction of traffic loads, applied mainly to mechanistic empirical methods. In such research, strains due to traffic loads — calculated by pavement modeling based on linear elastic theory — are used for the evaluation of the life duration of the pavement. Real traffic loads on pavement vary significantly according to their intensity as well as their application modes. Our work aims to evaluate the relevance of the simplifying assumptions used in modeling to determine which parameters used in the description of traffic loads have more influence on the strains in flexible pavements. The study is chiefly based on experimental data. Strain measurements were made on several road structures, varying the main parameters of traffic loads, i.e. load intensity, inflation pressure of the tires and type of wheels. These measurements were carried out at various temperatures. Two structures featuring contrasted rigidities were modeled according to the French design method for flexible pavement. The horizontal strains exerted on these structures were studied at two depths : the bottom of the base course and the bottom of the wearing course. Then, experimental data were systematically compared with the modeling results. Strains at the bottom of the base course were both analyzed related to their maximal value — which corresponds to one of the design criteria used in mechanistic empirical methods — and as signals corresponding to the strain recordings in one given point during the passage of a traffic load. Analysis of maximal values allowed an evaluation of the influence of the studied load parameters and lead on to solutions to include them in calculations for equivalent traffic. The study of strain signals resulted in the definition of values allowing to express strain variations in function of time. The said values apply principally to notions like strain rates and the definition of frequencies that will best model strain variation in time with a sinusoidal curve. Such frequencies are obtained by decompositions in Fourier series. A relation between load speed and frequency has been defined. At the bottom of the wearing course, the study focused on strain signals only, with strange variations — which have been called — appearing in the horizontal strains at low depth. The scrutiny of these irregularities showed that bituminous materials located near to the road surface of flexible pavements are paradoxically more stressed horizontally than vertically. A situation that tends to link the behavior of bituminous layers in flexible pavements to the performance of a slam structure. Analysis of strain signals was supplemented by linear viscoelastic modeling based on Huet's model. If introducing the viscoelastic behavior of the bituminous layers showed no notable improvement in the quality of modeling, it nevertheless highlighted the necessity to model the pavement structure as a whole. Stress analysis underlined the effects of interactions between layers following different patterns of behavior and the importance of the interface conditions between these layers. Finally, the influence of the real vertical load pressure distribution on stress and strain in bituminous layers has been evaluated with elastic modeling. Variation of the loading conditions showed little effects on stress and strain distribution in the bituminous layers.

A nonlinear optimization program is developed to accept the AASHTO DNPS86 pavement design computer program as a subroutine. This enables the obtaining of the least cost design for flexible and rigid pavements without manual interation. Examples illustrate using the 3 design variables for rigid pavements (reliability, subbase thickness, and performance period of intital pavement), and 4 variables for flexible pavements (the preceding 3 plus an additional layer thickness). Minor program changes permit the star-search routine to include additional design variables, at the cost of added computation time. Parametric curves for minimum-cost designs of rigid and flexible pavements are provided that allow identifying the least cost pavement type before the using the program to obtain final layer thicknesses.

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).

In this paper, three-dimensional elasto-plastic finite element analysis was performed on flexible pavements under vertical repeated traffic loads to evaluate their shakedown behavior. Six different pavements with different structural number (SN) were modeled and subjected to a wide range of cyclic vehicle loads. Shakedown limits were obtained considering both failure and serviceability restraints. Results indicate that shakedown coefficient and shakedown bearing capacity increase with a rise in SN for all load types. Lower limit of shakedown bearing capacity versus SN can be regarded as a criterion for pavement design. Besides, shakedown failure-displacement factor (SFDF) was introduced as an index which is able to include both failure and serviceability criteria to compare pavements in terms of their shakedown behavior. Results suggest increase in SFDF with increasing SN, particularly for light loads. Furthermore, results indicate that increase in asphalt layer thickness always improves shakedown bearing capacity, while increase in base and subbase layer thickness is not only ineffective beyond an effective thickness but may also be damaging. In addition, the results of the present study were compared to lower bound and upper bound shakedown analysis for verification and showed reasonable agreement.

Road infrastructure development has an important role as a supporter of social and economic activities in the community. However, there are still areas in West Sumatra, one of which is on Road Kurai Mudiak Liki, 50 Regency. Road Kurai is a connecting road between villages in the 50 Regency District which is still in the form of land and has a narrow width. In an effort to improve the condition of the Kurai road, it is necessary to plan for the highway by taking into account the vehicle load, weather, pavement material and soil carrying capacity. The bearing capacity of the subgrade is determined by the value of the California Bearing Ratio which greatly determines the thickness of the pavement layer because it will become the flexible pavement foundation that will be planned. Meanwhile, vehicular traffic, vehicle dimensions and payload will cause a compressive force on the vehicle axle which is supported on the road pavement which is channeled to the subgrade which is also an important reference in determining the thickness of the pavement. This study aims to determine the planning of flexible pavement thickness using the SKBI component analysis method 2.3.26.1987 and the 2017 Road Pavement Design Method No. 02/M/BM/2017 on Road Kurai Mudiak Liki Suliki. To find out which method is stronger for traffic load. The higher the traffic load, the thickness of each layer is important. Based on the calculation results of the two methods, the results obtained in the Component Analysis Method, namely Laston = 11.25 cm, Upper Foundation Layer = 15cm, Lower Foundation Layer = 20cm, and Road Pavement Design Method 2017 obtained calculation results AC-WC = 4 cm, AC-BC4 =6cm, AC-BC or AC Base =7.5cm, CTB3=15cm, Class A Aggregate Foundation=15cm.Keywords: Component Analysis Method, Road Pavement Design Method 2017, Road Kurai Mudiak Liki Suliki

Full-scale accelerated testing was used to provide new insight into quantifying the effectiveness of geogrids on low-volume flexible pavement performance. Although several previous studies report that geogrids improve pavement performance by enhancing structural capacity and reducing distress potential, the new study addresses how to maximize the benefits and cost-effectiveness of geogrid. To perform full-scale testing, three cells of flexible pavements, each having three pavement sections, were constructed. The granular base and hot-mix asphalt (HMA) layer thicknesses varied, and each cell had at least one control and one geogrid-reinforced pavement section. Instruments were embedded during construction to measure stress, strain, deflection, moisture, pore-water pressure, and temperature and were used to monitor pavement response to moving load. A moving dual-tire at 8 km/h and 44 kN was used to apply accelerated traffic loading. The performance of the various pavement sections when exposed to accelerated loading is presented. On the basis of pavement measured response as well as visual observation of the pavement cross section after excavation, the study showed that geogrid is very effective in reducing the horizontal shear deformation of the aggregate layer, especially in the traffic direction. Hence, the effectiveness of geogrid is clear for aggregate base layers with thicknesses ranging from 203 to 457 mm, and geogrid is expected to show similar effectiveness for greater base thickness given that thin HMA layer is used. The study also found that the optimal geogrid location in a thin aggregate layer is at the unbound aggregate-subgrade interface. For a thicker base layer, it is optimal to install a single geogrid at the upper third of the layer; the addition of another geogrid at the subgrade-base layer interface may be needed for stability.

Ground-penetrating radar (GPR) was used to identify the variability in layer thickness for 13 general pavement study sites in North Carolina. Two data sets were collected for each of 11 sites and one data set was available for 2 additional sites. Asphalt thicknesses varied over a wide range, but the standard deviation for asphalt layers less than 6 in. (15.24 cm) thick was 1 or less. GPR results were consistent with North Carolina typical base course thicknesses, whether that base be asphalt, dense-graded aggregate, or cement-treated aggregate. Variability in base course thickness was greater than variability in the surface layer. The effects of changing layer thicknesses from the mean thicknesses to the mean 1 standard deviation were examined by backcalculation and by calculating overlay thickness requirements with the North Carolina Department of Transportation overlay design procedure. Overlay thickness requirements typically changed by 3.8 mm (0.15 in.) over the range of layer thicknesses. Backcalculated moduli for asphalt and aggregate base were found to be most affected when the asphalt layer is thinner and the base course standard deviation is large. It is concluded that it is important to have an estimate of layer thicknesses for overlay design but that extensive coring is not required to obtain an adequate estimate.