Subgrade Resilient Modulus Research Articles

Study on Correction Method of Subgrade Modulus Based on FWD

In order to calibrate subgrade back calculation modulus, the correction method of subgrade back calculation modulus was put forward based on theoretical analysis and indoor/outdoor experiments, and the detailed implementation process was analyzed combined the latest results of subgrade dynamic modulus and subgrade moisture forecast. The results show that the equivalent resilient modulus of subgrade simultaneously consider subgrade material moisture condition and stress dependency and the forecast equation has higher correlation. The ratio of the loading plate test results to the predicted results ranges from 0.25 to 0.4 and the mean is 0.32, which conforms to the requirements of AASHTO normal value. Utilizing the scale factor 0.65 to calibrate subgrade back calculation modulus is viable and validated. The results provides a new perspective and approach for deformation analysis of subgrade structure. As well, the established method makes up the defect that can't reflect the effect of moisture and stress on equivalent resilient modulus of subgrade.

Study on Moisture Modification for Resilient Modulus of Subgrade

Subgrade resilient modulus of 3 typical soils, namely sand, silt and clay, have been measured under different conditions of 2 target dry density and 3 target water content by triaxial repeated-loading test. Analysis has been focused on the effect law of subgrade moisture and compaction for resilient modulus based on test results. Subgrade moisture has some impact on resilient modulus of test soils, especially on that of fine-grained soils. The influence degree of subgrde compaction depends on soil group. Coefficient of moisture modification has been introduced to characterize relationship between subgrade moisture and resilient modulus. Two-parameter Logistic model with inflexion control has been proposed according to the following basic rule. Coefficient of moisture modification usually increases or decreases along with the decrement or increment of subgrade moisture. And then variety rate of coefficient becomes small in state of high or low water content.

Investigation on Adjustment Factor of AASHTO Subgrade Resilient Modulus Prediction

AASHTO (1993) predicts the backcalculated subgrade resilient modulus on an existing flexible pavement as a function of load applied, surface deflection at a ratio of radial distance to 0.7 of radius of stress bulb at the subgrade pavement interface least one. This paper reports the investigation on the accuracy of the predicted subgrade resilient modulus. From the investigation, the ratio of radial distance at least 1.5 is a better limit. The prediction is improved after an adjustment factor. For thick pavement structures, adjustment factors depends on the pavement structure, it is greater than 1.2. For other cases, with the ratio of radial distance greater than 4.0 the adjustment factor is more stable in a range of 1.06 to 1.10. For the ratio of radial distance in a range of 1.5 to 4 which is more practical for daily routine evaluation, the adjustment factor is in a range of 1.06 to 1.20, and the proposed adjustment factor to be used for pavement evaluation is 1.15.

Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus

This paper investigates the use of artificial neural networks (ANNs) and genetic algorithms to improve the accuracy of the prediction of subgrade resilient modulus (M r) based on soil index properties. Furthermore, it also examines the effect of the accuracy of the M r estimation on the mechanistic empirical pavement design guide (MEPDG) performance prediction. The results of this paper showed that the ANN models had much better prediction of the M r coefficients of subgrade soils than that of the regression models. In addition, the use of the genetic algorithms in the selection of the input variables of the ANN models enhanced the accuracy of the prediction of those models. The results of the MEPDG analyses indicated that the prediction model used to estimate the subgrade M r input value can have a significant effect on the predicted performance of pavements. Furthermore, those results showed that the use of ANN models yielded much more accurate pavement performance prediction than using regression models; in particular when genetic algorithms were used in developing those models.

Incorporation of Subgrade Modulus Spatial Variability in Performance Prediction of Flexible Pavements

Recent efforts under the auspices of the National Cooperative Highway Research Program (NCHRP) have resulted in the development of a Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures. The recently developed Mechanistic-Empirical Pavement Design Guide (MEPDG) employs an iterative procedure for accumulating damage over the entire design period. It follows by mechanistically computing the pavement response (i.e., stresses and strains) and empirically translating these responses into individual distresses and pavement roughness. In the current procedure, the subgrade is divided into several sublayers, and an average modulus is used to define the mechanical behavior of each sublayer, without any consideration of the spatial variability in the subgrade modulus. This paper aims to evaluate the effect of such variability on the overall performance of flexible pavements. To achieve this objective, actual field data were collected and analyzed to determine the frequency distribution of the subgrade modulus, which was incorporated in three-dimensional (3D) finite element models of typical flexible pavement structures. The response obtained from the 3D finite element analysis was used to predict the pavement performance using the MEPDG transfer functions. The results of this study showed that the higher the variability is in the subgrade resilient modulus, the higher the variability is in the pavement response. Furthermore, the effect of the spatial variability in subgrade modulus was more pronounced on the prediction of rutting than that of the fatigue cracking.

Use of Structural Condition Index for Pavement Structural Evaluation Tool

Abstract In this study, an effort was made to evaluate pavement condition using the pavement structural condition index (SCI), which isbased on falling weigh deflectometer (FWD) deflection basin and structural numbers that incorporated various levels of traffic andsubgrade resilient modulus. Extensive field validations were conducted to assess the effectiveness of SCI along with comparisonof pavement distress and condition score restored in the Pavement Management Information System (PMIS). The analysis resultsrevealed that the SCI appears to realistically capture the existing pavement condition compared to the pavement scores and fieldsurvey results. Based on this finding, probabilistic and deterministic models were developed to estimate SCI when FWD data isnot available. These models are deemed most useful for network-level analysis purposes to aid establish pavement maintenanceand rehabilitation plans.Key words: Falling weigh deflectometer deflection basin, Pavement structural condition index, Pavement management informationsystem, Probabilistic and deterministic models, Subgrade resilient modulus

Global Sensitivity Analysis of Mechanistic–Empirical Performance Predictions for Flexible Pavements

The new AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various feasible design scenarios. The MEPDG performance predictions for the anticipated climatic and traffic conditions depends on the values of the input parameters that characterize the pavement materials layers, design features, and condition. This paper focuses on comprehensive global sensitivity analyses of flexible pavement performance predictions to MEPDG design inputs under five climatic conditions and three traffic levels. Design inputs evaluated in the analyses include traffic volume, layer thicknesses, material properties, and groundwater depth. Correlations between design inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates and three traffic levels). Two response surface modeling approaches—multivariate linear regressions and artificial neural networks—were developed for modeling the analysis results for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks were particularly effective in providing robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs. The design limit normalized sensitivity index adopted in the study provided a practical interpretation of sensitivity by relating a given percentage change in an MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The design inputs most consistently in the highest sensitivity categories across all distresses were the hot-mix asphalt dynamic modulus master curve, hot-mix asphalt thickness, surface shortwave absorptivity, and Poisson's ratio of hot-mix asphalt. Longitudinal and alligator fatigue cracking were also very sensitive to granular base thickness and resilient modulus and subgrade resilient modulus.

Fatigue and rutting lives in flexible pavement

Flexible pavement is designed based on axle load limits and climatic conditions. The Egyptian code has specified certain load limits that should not be exceeded. The overweight trucks cause severe deterioration to the pavement and thus reduce its life. The study aims at studying the effect of axle load increase, and the variation in pavement modulus, on the overall pavement life. The research uses the BISAR software and the Egyptian environmental and pavement materials conditions to estimate the tensile strains occurring under the asphalt concrete (AC) layer and the compressive strains above the subgrade surface. The results revealed that tensile and compressive strain increased with increasing axle loads and decreased with increasing asphalt layer modulus thus the violating trucks should be unloaded when their weights exceed certain limits. Base thickness and subgrade resilient modulus were the key elements which control the equilibrium between fatigue and rutting lives.

Discussion of “Neural Network Modeling of ResilientModulus Using Routine Subgrade Soil Properties” by MusharrafZaman, Pranshoo Solanki, Ali Ebrahimi, and Luther White

The authors presented a good comparison of different artificial neural network (ANN) models to predict the resilient modulus of subgrade (MR) using routine subgrade soil properties, which include grain size distribution, plasticity index, compaction characteristics, and unconfined compressive strength of soil. The database is limited to soils from the state of Oklahoma. The novelty of the paper lies in presentation of the input and output data in terms of its frequency distribution both for training and testing data. Unlike previous studies, the data were not normalized and, hence, it would have been better to see the distribution of the normalized data, followed by the necessary model development.

Influence of Variation of Groundwater Level on Deformation of Subgrade Based on Hydromechanical Coupling

Deformation of subgrade caused by variation of groundwater level is a problem of hydromechanical coupling. The coupling mechanism of seepage and deformation of subgrade was analyzed, and a model of hydromechanical coupling was then established. The resilient modulus of the model is related to the water content and compactness of subgrade soil. The relationship between them was obtained based on laboratory tests, and was employed to research the influence of rise of groundwater level on resilient modulus and deformation of subgrade. The results showed that the rise of water level had great influence on resilient modulus of subgrade, and the deformation of subgrade can’t be neglected.

Influence of Variation of Groundwater Level on Deformation of Subgrade Based on Hydromechanical Coupling

Deformation of subgrade caused by variation of groundwater level is a problem of hydromechanical coupling. The coupling mechanism of seepage and deformation of subgrade was analyzed, and a model of hydromechanical coupling was then established. The resilient modulus of the model is related to the water content and compactness of subgrade soil. The relationship between them was obtained based on laboratory tests, and was employed to research the influence of rise of groundwater level on resilient modulus and deformation of subgrade. The results showed that the rise of water level had great influence on resilient modulus of subgrade, and the deformation of subgrade can’t be neglected.

Regression Model for Resilient Modulus of Subgrade Soils in Shanghai

Subgrade resilient modulus (MR) is very important for effective design of pavements. Several methods to estimate the resilient modulus were suggested in the past years. The main objective of this paper was to validate the correlation of MR with other physical properties of the subgrade soils. Cohesive soils representing major soil types in Shanghai were selected. The resilient modulus tests were conducted with UTM. Additional analysis was then performed to develop correlations between the model parameters and other soil properties. To verify the prediction models independently, laboratory MR tests were conducted on new subgrade soils. It was observed that the predicted MR values compared well with the laboratory measured values for the soil samples.

Local Calibration and Implementation of the Mechanistic-Empirical Pavement Design Guide for Flexible Pavement Design

This paper presents the results of the calibration and implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG) for the design of flexible pavements in Italy. Local input data for traffic, climate, materials, and subgrade (foundation soil) are evaluated and subsequently arranged, as required by the MEPDG to be used in the associated software. The coefficients of the distress models are derived by means of a comparison between the performances predicted by the MEDPG and those predicted by theoretical equations, which are believed to represent the local distress process of a pavement and/or assumptions prevalently made in Italy. A wide parametric analysis is performed so that the influence in design process attributed to load spectra, temperature, dynamic modulus of asphalt concrete, and resilient modulus of subgrade is shown. The results obtained from MEPDG are compared, according to the hypothesis of zero-maintenance during the service life of a pavement, to those derived from using a local mechanistic-empirical method. As a consequence, the difference between these two design methods is quantified. For a verification of the results obtained from the MEPDG, the 1993 AASHTO guide is used. Subsequently, by using the MEPDG for rehabilitation, a maintenance strategy based on two resurfacings of wearing course of pavement is also investigated, and its effect on the extension of service life of a pavement in contrast with zero-maintenance is shown. Therefore, this work provides the knowledge for locally implementing the MEPDG with significant success both for the assumption of zero-maintenance and resurfacing treatments during the service life of a pavement.

Sampling-Based Sensitivity Analysis of the Mechanistic–Empirical Pavement Design Guide Applied to Material Inputs

The Mechanistic–Empirical Pavement Design Guide (MEPDG) is the result of NCHRP Project 1-37A. This pavement design procedure uses mechanistic and empirical models calibrated with national data. Calibration of MEPDG is required to improve the accuracy of the models for local conditions. The data needed for calibration require numerous tests to characterize materials. The fieldwork for collecting the data to verify the models is laborious. This research studied the sensitivity of MEPDG to material input parameters. The main goal of this research was to determine the influence of material parameters on pavement performance as predicted by MEPDG. Results from sensitivity analyses can be used for planning data collection for calibration, implementation, and general understanding of MEPDG. However, MEPDG is so complex that the sensitivity analysis methodology was carefully designed to identify the relative importance of input variables. This research employed space-filling computer experiments with Latin hypercube sampling, standardized regression coefficients, and Gaussian stochastic processes to categorize the relative importance of material inputs in MEPDG for two flexible pavement structures that used Level 3 analysis. The methodology worked well for analyzing the sensitivity of material inputs in MEPDG, with the advantages of varying all parameters at once and requiring a relatively small number of runs for a completed analysis of the entire input space. Effective binder content, as-built air voids, Poisson's ratio, surface shortwave absorption of asphalt layers, and resilient modulus of subgrade had a significant effect on pavement performance.

Developing Correlation Relationship between Modulus of Subgrade Reaction and Resilient Modulus for Florida Subgrade Soils

The resilient modulus of pavement subgrade is an essential parameter for mechanistically based pavement design procedures; the modulus of subgrade reaction (k) is used as a primary parameter for rigid pavement design. The AASHTO pavement design guide (1986, 1993) suggested a theoretical relationship between modulus of subgrade reaction and resilient modulus of subgrade based on the assumption that the subgrade material is linear elastic. This paper presents an experimental study to evaluate the load–deformation and resilient modulus characteristics of the granular subgrade soils with the use of field and laboratory tests. An extensive field static plate bearing load testing program was conducted to evaluate the in situ bearing characteristics of typical Florida pavement subgrade soils. A full-scale laboratory evaluation of the subgrade performance was conducted in a test-pit facility that simulated the actual field conditions. A laboratory triaxial test was performed to evaluate the resilient modulus characteristics of the subgrade materials. Comparative studies were conducted to evaluate the resilient modulus from laboratory triaxial tests and field experimental studies. Correlation relationships were developed between the measured resilient modulus in the laboratory and the resilient modulus measured with the use of a test-pit facility and between the subgrade soil resilient modulus and the modulus of subgrade reaction. The calibrated relationship based on the experimental studies was close to the AASHTO theoretical relationship. The calibrated relationship could be used in the Florida pavement design guide to obtain realistic subgrade resilient modulus values from laboratory resilient modulus measurements.

Prediction of Subgrade Resilient Modulus Using Genetic Algorithm and Curve-Shifting Methodology

This paper demonstrates the applicability of the genetic algorithm and curve-shifting methodology to the estimation of the resilient modulus at various stress states for subgrade soils by using the results of triaxial resilient modulus tests. This innovative methodology is proposed as an alternative to conventional nonlinear constitutive relationships. With the genetic algorithm, laboratory curves for different deviator stress levels at different confining pressures are horizontally shifted to form a final gamma distribution curve that can represent the stress–strain behavior of subgrade soils with the corresponding predicted shift factors. Resilient modulus values for a given stress state can be estimated on the basis of this curve and another gamma function that represents the variation of the shift values for different confining stresses. To compare the effectiveness of these two approaches, coefficients for the Uzan constitutive model were also determined for each laboratory test and compared with those determined by the approach described in this paper. Predicted resilient modulus values from each approach are separately compared with artificial neural network (ANN) model predictions to evaluate their efficiency and reliability for resilient response prediction. The results of the analysis indicated that the curve-shifting methodology gave superior estimates and a coefficient of determination 14% higher than the Uzan model predictions when the results were evaluated with the ANN model outputs. Thus, although it is not a constitutive model, use of the genetic algorithm and curve-shifting methodology is proposed as a promising technique for the evaluation of the stress–strain dependency of subgrade soils.

Subgrade resilient modulus prediction models for coarse and fine-grained soils based on long-term pavement performance data

Resilient modulus (M R) of subgrade soils is the elastic modulus based on the recoverable strain under repeated loads and depends on several factors such as soil properties, soil type and stress states. This paper presents the prediction equations to estimate M R from a set of soil physical properties for the unified soil classification system soil types namely coarse-grained and fine-grained subgrade soils. Data extracted from long-term pavement performance information management system database for 259 test specimens of reconstituted soil samples from 19 states in New England and nearby regions in the USA and two provinces in Canada were used in this study. Generalised constitutive model consisting bulk stress and octahedral shear stress was used to predict the M R of subgrade soils by developing equations for the regression coefficients (k-coefficients) in the constitutive model that relates them to various soil properties. Prediction models were developed by conducting multiple linear regression analysis using computer software SAS®. To verify the prediction models, a set of fresh laboratory M R tests were conducted on representative New England subgrade soils using AASHTO standards. The laboratory test results show that the developed models predict M R values fairly well for the soils with their properties values within the range used in developing the prediction models.

Characterization of NAPTF subgrade soils for mechanistic-based analysis and design of airport flexible pavements

The National Airport Pavement Test Facility (NAPTF) was constructed to generate full-scale testing data to support the development of advanced airport pavement design procedures. The NAPTF material properties were characterized using test pit data obtained prior to full-scale heavy gear trafficking as well as using test data obtained from trenches opened after the completion of traffic testing. In this paper, the post-traffic subgrade characterization test results obtained from NAPTF subgrade trench sections were evaluated and compared with the pre-traffic test pit data. The combined data were used to evaluate the most commonly used correlations amongst subgrade soil properties. Subgrade moduli backcalculated from Heavy Weight Deflectometer (HWD) test data were compared with the laboratory test results and were also correlated to field California Bearing Ratio (CBR) values. Regression analyses indicated that subgrade resilient modulus is significantly related to the unconfined compressive strength. These results may be used in the mechanistic-based analysis and design of airport flexible pavements subjected to New Generation Aircraft (NGA).

Stress Path Testing of Realistically Cured Lime and Lime/Cement Stabilized Clay

This paper aims to show that the addition of lime and lime/cement can significantly improve both the stiffness and the resistance to permanent deformation of clay soils even under adverse, though realistic, curing conditions. The AASHTO Guide for Design of Pavement Structures incorporates a method of calculating subgrade resilient modulus (MR), using repeated load triaxial testing, with respect to seasonal variations of the soil’s moisture content and temperature, while the equivalent British specifications adopt a “soaked California bearing ratio” approach. In practice 8°C is a realistic worst case curing temperature in situ and a (necessarily) wetter underlying clay will allow water to be drawn into the chemically treated layer during curing as a result of suctions, while the material will experience a small degree of confinement after compaction. These conditions have been replicated in a series of repeated load triaxial tests on lime and lime/cement stabilized, predominantly kaolinitic clay and the results compared with those from sealed samples, which were also cured at 8°C. All samples were subjected to 100 cycles of stresses representing 20, 40, 60, and 80% of the relevant undrained shear strength. This paper concludes that allowing the samples access to water is necessary if field performance is to be replicated.

Part 2: Evaluation of Subgrade Resilient Modulus: Resilient Modulus Estimation System for Fine-Grained Soils

A software tool was developed to estimate the resilient modulus of fine-grained subgrade soils. The subgrade resilient modulus is a key factor for use in mechanistic-empirical pavement design methods. However, for routine design use, the test method is too expensive and difficult to perform. Resilient modulus estimation software was developed with expert system approaches. Information entered by the user is first examined for reasonableness and accuracy. Then, data searching processes are initiated, and more than 30 estimation models can be invoked, depending on the availability of input data. All results were evaluated on the basis of certainty rules, such as how well data meet limitations existing during the model's original development environment. The user is given four alternate methods on which to base the choice of a resilient modulus that is most appropriate for the site: one based on certainty rules and three based on statistical methods.