Subgrade Resilient Modulus Research Articles

Improved Prediction Model for Dynamic Resilient Modulus of Subgrade Silty Clay in Eastern Hunan and Its Relevant Finite Element Method Implementation

With the enhancement of transportation speed and axle load, dynamic response of subgrade increases significantly. In order to improve the calculation accuracy of subgrade response under complex stress state, it is necessary to use dynamic indicators instead of static indicators in calculative process. For the sake of investigating the influence factor of dynamic resilient modulus of subgrade silty clay in Eastern Hunan, resilient modulus tests were carried out by conducting repeated load tri-axial tests. Based on available model, an improved resilient modulus prediction model considering four parameters was proposed by introducing k4. Corresponding accurate consistent tangent stiffness matrix was derived. Afterward, the improved model was implemented into finite element method software and verification work was put forward both on single element and pavement-subgrade structure. Finally, calculated results were compared with in-site measured results. Study achievements demonstrate that the improved model exhibits a higher precision and efficiency on single element because k4 can better adjust the affecting proportion of octahedral shear stress. When applied to analysis on pavement-subgrade structure, the improved model can reflect subgrade resilient modulus distribution and evolution more factually. In addition, numerical calculated result nearly coincides with measured results, which shows the application value of the improved model.

Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing

The subgrade resilient modulus is an important parameter in pavement analysis and design. However, available non-destructive testing devices such as the falling weight deflectometer (FWD) have limi...

Resilient modulus of subgrade materials for mechanistic-empirical pavement design guide

This paper describes the test program, proposed test protocol for subgrade resilient modulus (Mr), determination of Mr values, and development of design tables and constitutive models for Mr estimations. Subgrade samples were collected from 12 locations throughout the state of Wyoming, USA for standard laboratory and Mr tests. The proposed Mr test protocol was validated using test data collected from a Round Robin Test Program. The study shows that Mr values of soils having R > 50 increase with increasing deviator stresses. In contrast, Mr values of soils having R ≤ 50 decrease with increasing deviator stresses. Design tables of Mr values were developed. Also, three constitutive models were locally calibrated for Mr estimations. The constitutive Model Type B yields a relatively better estimation of the resilient modulus than Models A and C. The proposed methods could be adopted to increase the efficiency of pavement design in the USA and other countries.

Methodology to evaluate performance of pavement structure using soil moisture profile

Variation of moisture content in the subgrade layer of a flexible pavement may play a significant role in its structural performance by influencing the stiffness of the pavement system. The objective of this study is to develop and test methodologies to incorporate the soil moisture profile into flexible pavement evaluation and to determine how the changes of water table level will affect the pavement deformation. Falling weight deflectometer (FWD) data at two locations in Minnesota and Oklahoma were used to estimate the in situ measured pavement deformation with seasonal changes of water content. Then, a layer elastic analysis approach was adjusted to include the moisture dependency of the subgrade resilient modulus in calculating the deflection basin. The controlling parameters in these set of analyses were asphalt layer temperature, the degree of water saturation in unbound materials, depth to the groundwater table, and depth to bedrock. The environmental data for this study were obtained from the Long Term Pavement Performance-Seasonal Monitoring Program (LTPP-SMP) for the two pavement sections for different climatic conditions. The comparisons showed that dividing the subgrade layer into sublayers with variable, moisture-dependent modulus according to the location of moisture measurements will result in more accurate estimation of the pavement deflection. In addition, the predicted deflections indicated that considering moisture data up to the depth associated with 10% of surface vertical stress may suffice for this type of analysis. However, the overall quality of prediction is sensitive to the material type in the subgrade layer.

Back-Calculation of Resilient Modulus and Prediction of Permanent Deformation for Fine-Grained Subgrade under Cyclic Loading

AbstractSubgrade of roadways is subjected to repeated traffic loading at different loading intensities. The resilient modulus of subgrade is one of the important parameters for the design of paveme...

Sensibilidade de métodos empírico mecanísticos para análise de pavimentos flexíveis

The mechanistic-empirical method (E-M) of design the initial structure adopted for the pavement should be checked for performance criteria and changed if they are not met. In this context, the sensitivity analysis may help to understand that changes can be made. Thus, this study aims to compare the sensitivity of E-M AASHTO, applying the Mechanistic Empirical Pavement Design Guide (MEPDG) software, and the software ELSYM5 for variation in thickness and resilient modulus of flexible pavement layers. Therefore, simulations were performed on software to estimate the pavement performance. The results were adjusted by regression models and regression coefficients were used to determine the significant parameters in predicting performance and to compare applied methods as sensitivity. Distress estimated by the MEPDG software were sensitive to at least one of the parameters under consideration, while the performance predicted by the computer program ELSYM5 was sensitive to variations in the thickness of the asphalt layer and the sub-base and the subgrade resilient modulus. It was concluded that there are differences in sensitivity observed in both softwares, however, the ELSYM5 is a viable alternative to the use as an E-M method of design. Keywords: Sensitivity analysis, Mechanistic-empiral method, MEPDG software, Software ELSYM5.

Quality control of subgrade soil using intelligent compaction

AbstractIntelligent Compaction (IC) of subgrade soil has been proposed to continuously monitor the stiffness of subgrade during its compaction. Modern IC rollers are vibratory compactors equipped with (1) an onboard measuring system capable of estimating the stiffness of the pavement material being compacted, (2) Global Positioning System (GPS) sensor to precisely locate the roller, and (3) an integrated mapping and reporting system. Using IC, the roller operator is able to evaluate the entire subgrade and address deficiencies encountered during compaction. Continuous monitoring of quality during construction can help build better quality and long-lasting pavements. However, most of the commercially available IC rollers report stiffness in terms of Original Equipment Manufacturer (OEM) specified indicator, known as Intelligent Compaction Measurement Value (ICMV). Although useful, additional tests are required to establish the correlation between these ICMV values and the resilient modulus of subgrade (Mr). Since the mechanistic design of the pavement is performed usingMr, it is important to know if the designMris achieved on the entire subgrade during compaction. This paper presents a systematic procedure for monitoring the level of compaction of subgrade in real time using intelligent compaction (IC). Specifically, the Intelligent Compaction Analyzer (ICA) developed at the University of Oklahoma was used for estimating the modulus of the subgrade. Results from two demonstration studies show that the ICA is able to estimate subgrade modulus with an accuracy that is acceptable for quality control activities during the construction of pavements.

Performance of geosynthetic reinforced/stabilized paved roads built over soft soil under cyclic plate loads

The benefits of using geosynthetics to enhance the performance of pavement constructed over soft subgrade was evaluated using cyclic plate load testing. A total of six test sections, with varying types and layers of geosynthetics and base thicknesses, were constructed inside a 2 m × 2 m × 1.7 m test box. A cyclic load at a frequency of 0.77 Hz was applied through a 305 mm-diameter steel plate. The test sections were instrumented by a variety of sensors to measure the load-associated pavement response and performance. The test results clearly showed the benefits of geosynthetics in significantly reducing the pavement rutting. The test section with double geosynthetics layers performs much better than all other sections studied in this paper. Geosynthetics placed at the base–subgrade interface function more as weak subgrade stabilization than as base layer reinforcement in this study. Finally, the benefits of geosynthetic reinforcement was quantified, within the context of the AASHTOWare Pavement ME Design guide, in terms of increasing the resilient modulus of base course layer and/or reducing the thickness of base aggregate layer in pavement structure. The results of analysis show that for geosynthetics functioning as base reinforcement alone, the value of resilient modulus of the base course layer can be increased by about one quarter and that the thickness of base layer can be reduced by about one third for the pavement sections, with 457 mm thick base and single layer of geosynthetic placed at the base–subgrade interface, tested in this study. For geosynthetics functioning as subgrade stabilization alone, the test results showed that the resilient modulus of subgrade can be almost doubled.

Systematic back-calculation protocol and prediction of resilient modulus for MEPDG

A research focusing on the characterisation of representative local material properties was conducted to facilitate the full implementation of the Mechanistic-Empirical Pavement Design Guide for roadway designs in Wyoming. As part of the test program, falling weight deflectometer deflection data were collected from 25 test sites in Wyoming for back-calculation of subgrade resilient modulus. Also, subgrade materials from these test sites were sampled for laboratory resilient modulus measurement in accordance with the AASHTO T 307. The back-calculation is a user-dependent procedure and produces a non-unique resilient modulus estimation. To alleviate this limitation, this paper focuses on the recent development of a systematic back-calculation protocol for subgrade resilient modulus using MODCOMP6 software. The protocol is intended for use on a flexible pavement with a crushed base. The proposed procedure discusses pre-analysis checks, seed modulus adjustment, pavement structure adjustment and program termination criteria. A correlation study was conducted to correct back-calculated resilient modulus to laboratory-equivalent values. The results conclude that a non-zero intercept linear regression model provides a better correlation than the widely used zero intercept linear regression model. Furthermore, better correlations are achieved when the back-calculated resilient modulus of a lower subgrade layer and resilient modulus measured at higher laboratory test sequences Nos. 11 to 15 are considered. The non-zero model based on Mr test sequence No. 14 and lower subgrade layer yields the best correlation. For the zero model, a C-factor of 0.645 based on Mr test sequence No. 15 and lower subgrade layer yields the best correlation.

An Optimization Model for Design of Asphalt Pavements Based on IHAP Code Number 234

Pavement construction is one of the most costly parts of transportation infrastructures. Incommensurate design and construction of pavements, in addition to the loss of the initial investment, would impose indirect costs to the road users and reduce road safety. This paper aims to propose an optimization model to determine the optimal configuration as well as the optimum thickness of different pavement layers based on the Iran Highway Asphalt Paving Code Number 234 (IHAP Code 234). After developing the optimization model, the optimum thickness of pavement layers for secondary rural roads, major rural roads, and freeways was determined based on the recommended prices in “Basic Price List for Road, Runway and Railway” of Iran in 2015 and several charts were developed to determine the optimum thickness of pavement layers including asphalt concrete, granular base, and granular subbase with respect to road classification, design traffic, and resilient modulus of subgrade. Design charts confirm that in the current situation (material prices in 2015), application of asphalt treated layer in pavement structure is not cost effective. Also it was shown that, with increasing the strength of subgrade soil, the subbase layer may be removed from the optimum structure of pavement.

Effects of Seasonal Variation on the Load-Bearing Capacity of Pavements Composed of Insulation Layers

Seasonal variation in the subgrade resilient modulus is likely caused by external factors, such as precipitation and freeze–thaw cycles. One of the strategies for minimizing the impact of this variation on the subgrade modulus is to use insulation layers to prevent frost penetration. This study investigated the effects of the use of insulation layers on pavement performance in the fully instrumented Integrated Road Research Facility in Edmonton, Alberta, Canada. Three insulated sections of the test road were comprised of bottom ash (BA) (100 cm) and polystyrene boards of two thicknesses (5 and 10 cm), and the adjacent conventional section was considered the control section (CS). The resilient modulus and the effective modulus of pavement were backcalculated with the data obtained from falling weight deflectometer testing conducted at the test road during a 1-year monitoring period, from July 2014 to July 2015. Temperature and moisture probes, installed across the depth of the sections, were used to determine the frozen, thawed, or recovering condition of the pavement. The study results revealed that polystyrene boards protected subgrade soil from freezing and thawing effects. The minimum ratio of the backcalculated subgrade modulus of each test to the resilient modulus of the test performed in September was 0.94 in the BA section, and the ratio of the CS could decrease to 0.88 in the recovering period. Comparison of the load-bearing capacity of insulated sections and the CS indicated that, unlike BA, polystyrene boards significantly decreased the load-bearing capacity of the pavement.

Quality Improvement of Subgrade Through Intelligent Compaction

Proper quality control and quality improvement strategies during compaction can improve the quality of the subgrade and minimize premature failures of asphalt pavements. This paper discusses the use of the Intelligent Compaction Analyzer (ICA), which was developed at the University of Oklahoma, in quality control and quality improvement during compaction of stabilized subgrade. Six case studies were conducted to demonstrate the ability of the ICA in monitoring subgrade density and subgrade modulus (known as ICA modulus) in real time. The ICA modulus correlated reasonably with the subgrade modulus backcalculated from the falling weight deflectometer data ( R2 = .63), fairly with the dynamic cone penetrometer test results ( R2 = .50), and excellently with the in situ subgrade resilient modulus ( R2 = .72 to .97). The subgrade modulus in undercompacted areas could be increased by more than 50% by applying additional roller passes.

Validation of Long-Term Pavement Performance Prediction Models for Resilient Modulus of Unbound Granular Materials

Reliable characterization of unbound granular material and subgrade resilient modulus requires repeated load triaxial testing, advanced dynamic loading equipment, and technical experience not typically available in many geotechnical laboratories. For Level 2 design inputs, the Mechanistic–Empirical Pavement Design Guide recommends correlation models to estimate the resilient modulus of unbound granular material and subgrade from basic material properties (e.g., gradation, unconfined compressive strength, and California bearing ratio). These correlation models are developed from data on a wide range of soil and material types; this wide range increases the associated error with resilient modulus prediction. This paper compares laboratory measured resilient modulus for two types of locally available unbound granular materials to the predicted values; models developed under the Long-Term Pavement Performance (LTPP) program were used. Resilient modulus tests of six gradations of two types of material—100% crushed limestone and gravel—were conducted at two levels of moisture content. Results showed that the LTPP models significantly underestimated resilient modulus for the limestone material and showed high residuals associated with evaluating resilient modulus of both gravel and limestone materials. The coefficient of variation of the root mean square error was 50.3% for gravel and 55.6% for limestone. The high residuals associated with evaluating resilient modulus with the LTPP models justify the development of local prediction models to improve the reliability of Level 2 design inputs for unbound granular materials.

Methodology to Improve the AASHTO Subgrade Resilient Modulus Equation for Network-Level Use

AbstractFalling weight deflectometer (FWD) testing is commonly used to estimate subgrade resilient modulus (MR) by using back calculation and/or the AASHTO MR equation. Considering the availability of limited information in the pavement management information system (PMIS) database, the AASHTO MR approach is suitable for subgrade assessment at a network level because of its simplicity. An adjustment factor is commonly applied to the AASHTO MR equation because of the difference between laboratory-measured MR and estimated MR. However, large variations in the adjustment factor have been observed with different geographical locations and climate conditions. Thus, using a fixed value of the adjustment factor has led to inaccurate assessment of subgrade soil MR. This paper presents a methodology to improve the accuracy of the existing AASHTO MR equation by constructing an MR adjustment factor based on the correlation of back-calculated ER and AASHTO MR. This study also investigated the effects of subgrade soil...

Estimation of Subgrade Resilient Modulus for Fine-Grained Soil from Unconfined Compression Test

The resilient modulus of subgrade soil is used to design a pavement structure that is adequate for the expected traffic and environmental loading. Both mechanistic and empirical design procedures use this property. The resilient modulus test is a complex test that requires significant resources, including a high level of technical ability on the part of the operator. For smaller or less critical projects with a low volume of traffic where costly and complex resilient modulus testing is not justified, a correlation with other, simpler tests could be used. In this study, a correlation with the results of an unconfined compression (UC) test is explored for estimation of the resilient modulus of fine-grained soils. This paper presents several models with which to estimate the resilient modulus of fine-grained soil from the results of UC tests considering two sample preparation techniques: static and impact compaction. The simplest model considers only unconfined compressive strength to predict the resilient modulus with a fair correlation. The more detailed models also consider the plasticity index, percentage of material passing the Number 200 sieve, and modulus of the stress–strain curve from the UC test. The sample preparation technique had a significant influence on the UC test results.

Field and Laboratory Determination of Subgrade Resilient Modulus and its Application in Pavement Design

This paper presents a comparison study of the experimental results from the falling weight deflectometer (FWD) test and laboratory resilient modulus test on granular subgrade materials and its application in flexible pavement design. Field and laboratory testing programs were conducted to develop a practical methodology for estimating resilient modulus (Mr) values of subgrade soils for use in the design of pavement structures. Soil characterization database was established for lab testing. A multiple regression model can be used to predict Mr value using several factors including soil properties, soil type and state of stresses for three popular American Association of State Highway and Transportation Officials (AASHTO) soil types (A-4, A-6, and A-7-6) in Indiana, and these prediction models developed were verified compared with laboratory Mr tests with high R2 value. In situ Mr seasonal variation based on abundant FWD test data in five field testing sites spread in Indiana was conducted in order to find the correlation between resilient modulus, temperature, and precipitation for the period from 2006 to 2012. The proposed method can accurately predict subgrade Mr of lab testing. However results from lab testing are significantly lower than recommended range by mechanistic-empirical pavement design guide (MEPDG) and backcalculation one using an adjust factor of 3. The design examples showed that the seasonal variation of temperature and precipitation as well as traffic can affect the design thickness by as much as 15 to 20 % in general. The findings of this study are expected to be helpful in the implementation of the pavement design in Indiana and elsewhere.

Soil Resilient Modulus Regressed from Physical Properties and Influence of Seasonal Variation on Asphalt Pavement Performance

Subgrade soil, as the critical underlying support for other pavement layers and traffic loads, should be stiff enough to maintain the integrity of pavement structures and the smoothness of pavement surface. The resilient modulus, as an indicator of subgrade stiffness, is an essential input in the American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG). At input level 1 of MEPDG, the MEPDG generalized model is required to describe resilient modulus of subgrade soil, and the coefficients of this model are used for pavement design. The change of the resilient modulus model has raised the interest of many state highway agencies and made it necessary to convert old resilient modulus test data into new ones required for the MEPDG model. In this study, the coefficients of the generalized and the universal models for soil resilient modulus were obtained through regression of the results of 13 soils in Tennessee. The coefficients of the two models were also compared. There is a potential risk that the coefficients from the universal model may be mistakenly used in the MEPDG instead of those coefficients from the generalized model. The consequence of this improper use was demonstrated in the comparison between the miscalculated and the real resilient moduli. The coefficients of the generalized model were correlated to soil physical properties, which provided an alternate time-saving and economical method to obtain soil resilient modulus as level 2 inputs. The coefficients were obtained at different post-compaction water contents, to allow the estimation of pavement response under seasonal moisture variation of subgrade. Rutting and roughness of two typical pavement sections were analyzed to investigate the influence of the seasonal variation of soil resilient modulus on pavement performance. The results showed that moisture variation had a significant effect on subgrade resilient modulus and, subsequently, on pavement performance. It is recommended that seasonal change in soil resilient modulus be considered in the analysis on pavement performance.

Prediction of subgrade resilient modulus using artificial neural network

The development of an Artificial Neural Network (ANN) model to estimate subgrade resilient modulus is described in this paper. Nine (9) different sources of subgrade materials locally available in Georgia were subjected to the resilient modulus test with two replicates. The stress state and physical properties on resilient behavior of subgrade soils were successfully correlated with an ANN model developed in this paper. The results demonstrated that the stress state and physical properties of subgrade soil significantly influenced the subgrade resilient modulus, which in turn has a substantial effect on the pavement response predictions that impact pavement design.

Optimization of resilient modulus prediction from FWD results using artificial neural network

One of the most important steps in the design of new pavements and overlays is the selection of an accurate input value for the subgrade resilient modulus (Mr). This paper evaluates the use of regression analysis and artificial neural networks (ANN) to develop models that can be used to accurately predict the subgrade Mr design input value using Falling Weight Deflectometer (FWD) test results. The results of the regression analyses conducted in this paper indicated that the use of linear elastic analysis for backcalculation of the FWD modulus yielded better prediction of laboratory measured resilient modulus compared to using the AASHTO or Florida Equations. In addition, the accuracy of Mr prediction was significantly enhanced when ANN based models were used. For models that were based on FWD modulus backcalculated using different Softwares, the ANN improvement was only noticed when the model included soil physical properties. Finally, the results of this paper indicated that when using the FWD modulus backcalculated using the AASHTO or Florida equation to predict Mr design input value, it’s recommended to use the ANN model with variables selected using stepwise selection analysis.

Characterising the elastic behaviour of fine-grained subgrade soils under traffic loading

Resilient modulus is a measure of the elastic behaviour of subgrade soil under traffic loading. The design thickness of a pavement structure and its predicted performance are highly dependent on subgrade modulus. Characterisation of subgrade resilient modulus involves conducting advanced repeated loading triaxial testing that requires special equipment and technical experience that are not available in many soil laboratories. The objectives of this paper were to characterise the resilient modulus of typical fine-grained subgrade soils and develop design inputs for the Mechanistic-Empirical Pavement Design Guide. The resilient modulus of subgrade soil samples was tested at different levels of moisture content. Results of the laboratory testing were used to develop prediction models for resilient modulus as function of physical properties of the subgrade soil and stress state. The proposed models were compared to those developed under the Long-Term Pavement Performance programme. The results showed that the proposed models provided more reliable predictions with lower root mean square error.