Subgrade Resilient Modulus Research Articles

Experimental and numerical based model formulation for estimation of subgrade resilient modulus using the repeated load CBR test considering in situ state of stress

Characterizing subgrade in terms of resilient modulus is a crucial aspect of flexible pavement design. This paper proposes a methodology and predictive model to estimate the resilient modulus with better consideration of subgrade soils’ in situ stress state using a simple Repeated Load CBR (RLCBR) test. RLCBR tests were conducted on eight subgrade soils at three moisture contents. Numerical studies were conducted by simulating the CBR test in the commercial package LS-DYNA® to understand the stress state under plunger loading concerning field conditions. A new model was proposed for the characterization of subgrade soils based on laboratory RLCBR tests and the FEM, considering the stress state experienced by subgrade soils in the field. The proposed model was validated using data from four other soils and showed good agreement. The study model showed a better predictive capacity for the low plastic subgrade soils than previously developed models. Practicing engineers can use the developed model for estimating the subgrade resilient modulus at the recommended stress state for mechanistic pavement design while understanding the soil’s load-deformation behavior.

Enhancing pavement performance prediction with traffic speed deflection data

ABSTRACT Pavement performance modelling is an essential part of pavement management. Past research has shown that a structural indicator can be included in pavement performance models to capture differences in performance between weak and strong pavements. Weak pavements are expected to deteriorate more rapidly than strong pavements, as consistently shown in past research. The work presented in this paper uses the Traffic Speed Deflectometer (TSD) to calculate multiple structural indexes and compares their ability to differentiate between poor and well performing pavements. Historical condition data from the US National Parks pavement network is used in this assessment. Two approaches for calculating the subgrade resilient modulus and two approaches for calculating the effective structural number from the TSD results are compared. The temperature corrected centre deflection, surface curvature index and ratio of effective to required structural number are all calculated from the TSD data. Performance models are developed for two condition indexes and the International Roughness Index (IRI). In all cases, the addition of the structural indexes improved the quality of predictions, and the IRI prediction showed the most improvement from including the indexes. Ultimately, it was shown that the TSD testing results can be used to inform the pavement management process.

Analysis of Flexible Pavement Structural Behavior Considering Decreased Subgrade Resilient Modulus

This research focuses on the subgrade resilient modulus and its significance in investigating flexible pavements. The study compares results obtained from two distinct flexible pavement structures, utilizing numerical analysis using FLAC3D softawere. Four decreasing levels of subgrade resilient modulus (PF50, PF25, PF10, and PF5) were examined. Our findings reveal that the decrease in subgrade resilient modulus has a more pronounced effect on the GB/GB structure compared to the GB/GNT structure, with the latter demonstrating superior performance. By assessing subgrade resilient modulus, this research contributes to a better understanding and improving road infrastructure investigation not only in Algeria but also in broader contexts

On the variants of SVM method for the estimation of soil elastic modulus from TSD model: Numerical parametric study

The accurate estimation of the subgrade resilient modulus MR is a crucial element in designing durable and resilient pavements as it quantifies the soil’s bearing capacity to withstand traffic loads without excessive deformation or failure. Conventional methods and devices that are used to estimate MR, such as the Falling Weight Deflectometer (FWD) have several limitations due to operational and security constraints when measuring at the network level. To overcome these limitations, Traffic Speed Deflectometer (TSD) has been developed, which can continuously assess the pavement bearing capacity without requiring traffic control. However, processing the massive amounts of data generated by TSD requires specialized processing techniques such as Machine Learning (ML). Therefore, the primary objective of this paper is to develop an efficient and effective model that can accurately estimate MR by combining two techniques: (TSD + ML). The study uses numerical simulations of deflection velocity slope SV under TSD as the forward model. The research employs Support Vector Machine (SVM) algorithm for ML to classify and estimate MR modulus values from a synthetic and controlled dataset. SVM model exhibits robust performance in both classification and regression analysis, whereas, 95% of the discrepancies between estimated and tested values did not exceed ±15MPa. Advanced numerical validation, sensitivity analysis and parameter estimation methods were also conducted to optimize the model’s performance. Overall, the approach proposed in this paper has the potential to make a significant impact in the field of subgrade evaluation and can facilitate the development of better road infrastructure.

A flexible bearing plate based on steel plate and rubber mat

The bearing plates used in plate load test for highway engineering are typically rigid. However, due to limitations in obtaining the accurate distribution of compressive stress at the bottom of the bearing plate, there is often a significant deviation between the measured subgrade resilient modulus and the actual condition. To address this issue, a flexible bearing plate can be used to test the subgrade and obtain a more accurate resilient modulus. In this study, we use variance and degree of mean deviation to quantitatively evaluate the distribution uniformity of compressive stress. To create a rigid-flexible bearing plate that is similar to a flexible bearing plate, we explore the combinatorial design of steel plates and rubber mats. We examine factors such as the thickness (10, 20, and 30 mm) of the steel plate, elastic modulus (5, 10, and 20 MPa) and thickness (10, 20, and 30 mm) of the rubber mat, friction coefficient (μ:0, 0,2, 0.4, 0.6, 0.8, ∞) between the bearing plate and subgrade, and the combined shape characteristics of the rubber mat and steel plate. To reduce friction between the rubber mat and subgrade, we use lubricant, and through our design process, we develop a flexible bearing plate with relatively uniform compressive stress. Our computations show that when μ = 0.05, the variance is 0.0001, and the degree of mean deviation is 0.0780. These results indicate that the distribution uniformity of the compressive stress is very close to the uniform distribution load, which meets the necessary accuracy requirements for engineering applications.

Effect of the Field-Stress State on the Subgrade Resilient Modulus for Pavement Rutting and IRI

The new Mechanistic-Empirical Pavement Design Guide (MEPDG) uses the subgrade resilient modulus (MR) as the key input parameter to represent the subgrade soil behavior for pavement design. The resilient modulus increases with an increase in confining pressure, whereas, for an increase in deviatoric stress, it increases for granular soils and decreases for fine-grained soils. The value of MR is highly stress dependent, with the stress state (i.e., bulk stress) a function of the position of the materials in the pavement structure and applied traffic loading. Applying excessive vertical stress at the top of the subgrade without knowing the appropriate stress state can result in permanent deformation. In situ stress must be calculated so the correct resilient modulus can be determined. To facilitate the implementation of MEPDG, this study develops a methodology to select the appropriate subgrade resilient modulus for predicting rutting and IRI. A comprehensive research methodology was undertaken to study the effect of in situ or undisturbed subgrade MR on pavement performance using the MEPDG. Results show that MR obtained from in situ stress is approximately 1.4 times higher than the MR estimate from NCHRP-285. Thus, the in situ stress significantly affects the calculation of subgrade MR and, subsequently, the use of MR in the predicted rutting, with IRI using the AASHTOWare pavement mechanistic-empirical design. Results also show that the pavement sections were classified as in “Good” and “Fair” conditions for rutting and IRI, respectively, considering in situ MR.

Subgrade resilient modulus variation in low-volume roads design in north-eastern Brazil

The current asphalt pavement design method instituted in Brazil, MeDiNa, requires tests to determine the parameters of resilient modulus and permanent deformation of the granular layers. As these tests have a long execution time and high cost, this study analysed the variation of the resilient modulus (using composite model tested and linear predicted values) of low-volume road subgrade and the impact on the pavement design. This study simulated four different subgrades considering the soils of the metropolitan region of Fortaleza, Ceara, north-eastern Brazil and three predefined pavement structures. Total and subgrade rutting provided by the MeDiNa software were used as evaluation parameters. The simulation results allowed the authors to verify that the calculated rutting values are very close despite the resilient modulus adopted. In conclusion, road designers can consider any predicted linear value of the resilient modulus or composite models in the design of these highways, even with forecast errors greater than 20%. The study also proposes a low-volume road design catalogue considering the subgrades analysed.

Predicting Flexible Pavement Distress and IRI Considering Subgrade Resilient Modulus of Fine-Grained Soils Using MEPDG.

This paper highlights the subgrade resilient modulus (MR), which is recognized as an important parameter to characterize the stiffness of the subgrade soil for designing flexible pavement. In this study, 18 thin-walled Shelby tube samples of fine-grained subgrade soils were collected from two sites in South Carolina (Laurens/SC-72 and Pickens/SC-93) and tested in the laboratory using AASHTO T307-99 to obtain the MR. In addition, falling weight deflectometer (FWD) tests were performed on the same pavement sections to obtain the back-calculated MR(FWD) per the AASHTOWare 2017 back-calculation tool. A subgrade MR catalog was established and used to select hierarchical Input Level 2 for Pavement Mechanistic-Empirical design (PMED) analysis (v 2.6.1). The PMED analysis was run for 20 years. The Mechanistic-Empirical Pavement Design Guide (MEPDG) and global calibration values were used to predict asphalt concrete (AC) pavement distresses (e.g., rutting, bottom-up fatigue, top-down fatigue, and transverse cracking) and International Roughness Index (IRI) for each pavement section. The predicted values were compared to the field-measured values to determine bias and the standard error of the estimate to validate each distress prediction model for local calibration.

Characterisation and modelling of subgrade resilient modulus for pavement structural design in Egypt

In Egypt, estimation of subgrade resilient modulus (MR) for the structural pavement design is based on empirical correlation with California Bearing Ratio (CBR) adopted from literature. Thus, this study aims at verifying if this correlation is still valid for different Egyptian subgrade soils, otherwise calibration is warranted. A comprehensive experimental program is conducted on local sixteen subgrade materials. The testing program includes MR, CBR, and index soil properties such as gradation, Atterberg limits, Proctor compaction characteristics, and shear strength parameters. Four simple regression models correlating MR and CBR values are developed for local subgrade materials based on the anticipated field stresses under two different traffic loading conditions pertinent to Egypt. The proposed correlations yield more accurate MR values for the local materials and traffic loading conditions. Consequently, this is expected to contribute to a realistic pavement structural design, and hence better pavement performance over the service life.

Resilient modulus estimation using in-situ modulus detector: performance and factors

ABSTRACT The resilient modulus of subgrade should be estimated for pavement designs that undergo repetitive loading from vehicular movement. In this study, an in-situ modulus detector (IMD) with flat, wedge, and cone tips is employed to estimate the resilient modulus. The specimens with three different levels of water content are compacted, and the dynamic responses for two hammer drop heights are analysed. For all specimens, elastic waves are acquired to estimate the shear modulus and Poisson’s ratio using bender and piezo-disk elements. The resilient modulus estimated from the IMD is compared with the lightweight deflectometer (LWD) modulus. The experimental results show a stronger relationship between the flat and wedge tips than between the cone and wedge tips. Furthermore, higher drop heights result in strong relationships between the resilient, shear, and LWD moduli. This study proposes an intrinsic design and verification of an IMD for reliable resilient modulus estimation in pavement design.

Evaluating environmental effects on the structural behavior of the railroad track subgrade considering different sub-ballast design solutions

The subgrade of a freight railroad track must have sufficient bearing capacity to prevent excessive plastic strains and geometry-related problems to ensure its good performance. The track sub-ballast layer is used to distribute the loads from the ballast to the subgrade, as well as to mitigate climate-based subgrade problems. This study aims to evaluate the effects of Brazilian southeastern tropical climate on subgrade hydraulic and structural behavior, as well as its effect on the railroad track substructure. This is achieved by considering both granular and bituminous sub-ballast solutions. Accordingly, suction sensors were installed at the subgrade for the real-time monitoring and calibration of both track sections. The subgrade water retention was characterized by considering the effect of hydraulic hysteresis. Two independent finite element method (FEM) numerical models were developed. Water flow analyses were performed using a 2-D model to obtain the suction variation in the track substructure profile using climate-track interaction simulations. A 3-D model was used to evaluate the railroad track structural behavior in terms of wheel load–track (interaction simulations) displacement, stress, and strain. Hence, it was possible to calculate the subgrade resilient modulus using a constitutive model considering different hydraulic conditions obtained from a 2-D model and in-track monitoring. The results show that both the groundwater level and climate are crucial for evaluating the subgrade structural behavior, track design, and maintenance work. Suction variation and the hysteresis effect on subgrade hydraulic characterization significantly affect the resilient modulus and track performance of the subgrade. Bituminous sub-ballast proved to be more efficient than granular sub-ballast, as revealed by service life-cycle predictions.

Environmental and economic comparison of semi-rigid and flexible base asphalt pavement during construction period

Sustainable design of pavements is necessary for achieving carbon neutrality, which is the important goal for green pavements. In order to investigate the impacts of major design parameters on the environmental and economic performances of asphalt pavement, process life cycle assessment (LCA) and process life cycle cost (LCC) methods were used. And this paper mainly focuses on the effects of pavement type, resilient modulus of subgrade, thickness of adjustable layer, traffic level and the bidirectional annual average daily traffic volume of two-axis six-wheel and above vehicles (AADTT) on environmental and economic performances of asphalt pavement. Then energy consumption, greenhouse gas (GHG) emission, construction budget and environmental economy cost of heavy traffic and medium traffic asphalt pavement were quantitatively analyzed during construction period. The results indicate that the energy consumption, GHG emission, construction budget and environmental economy cost of each layer of semi-rigid and flexible base asphalt pavement are the largest in the materials preparation stage, followed by the transportation stage and the construction stage. The above environmental and economic indicators are mainly influenced by AADTT, adjustable layer thickness and pavement type of asphalt pavement during construction period, while the resilient modulus of subgrade has minor effect on environmental and economic indicators. Overall, the energy consumption and GHG emission of the flexible base asphalt pavement are lower than that of the semi-rigid base asphalt pavement, although flexible base asphalt pavement has higher construction budget and environmental economy cost.

Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus

Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purposes, the two tree ensemble methods were evaluated against a regression tree model and a multiple linear regression model, demonstrating their superior performance. The results revealed that a single tree model generally suffers from high variance, while providing a similar performance to the traditional multiple linear regression model. By leveraging a collection of trees, both tree ensemble methods, Random Forest and eXtreme Gradient Boosting, significantly reduced variance and improved prediction accuracy, with the eXtreme Gradient Boosting being the best model, with an R2 of 0.95 on the test dataset.

Variation of Resilient Modulus of Subgrade Soils over a Wide Range of Suction States

AbstractDesign of flexible pavements over fine-grained soils requires determination of subgrade resilient modulus over a wide range of degree of saturation and suction for different soils. A novel ...

Analysis of CBR design value selection methods on flexible pavement design: Colombia case study

A comparative analysis was carried out to observe the variation of a flexible pavement structural thickness, due to the use of different meth-ods to calculate the CBR design value, as an essential variable to estimate the Subgrade Resilient Modulus (Mr) through an empirical corre-lation. The Asphalt Institute Method and the Mean Criterion Method were applied to calculate de Design CBR value of a homogeneous roadway division from a representative track section located in the Bolivar Department, Colombia. As a result, the Design Percentiles of the CBR design unit were expanded for the Asphalt Institute method, thus, allowing the approach of more reliable and safe designs, considering that this method limits the selection percentiles to three traffic levels. Â

Resilient Modulus of Expansive Soils at High Suction Using Vapor Pressure Control

Abstract Robust design of pavement sections underlain by high-plasticity subgrade soils requires determination of subgrade resilient modulus over a wide range of suction to account for seasonal and diurnal variation of moisture. A laboratory experimental apparatus capable of performing suction-controlled repeated load triaxial tests at high suction (5 to 600 MPa) has been developed by integrating automated vapor pressure control to the specimen gas phase within a cyclic triaxial setup. The system eliminates limitations on applicable upper-bound suction that exist in more commonly adopted axis-translation approaches. This article describes the experimental setup, its working principle, and test procedures. Results are presented for a high-plasticity clayey soil to demonstrate testing capability for suction ranging from 5 to 100 MPa. Results show repeatability having an average coefficient of variation of less than 6 % for all loading sequences.

English

English

Calculation and Control Methods for Equivalent Resilient Modulus of Subgrade Based on Nonuniform Distribution of Stress

The resilient modulus of subgrade is a design parameter of the pavement structure, which is significantly affected by the overlying load and traffic load. It is important to calculate the equivalent resilient modulus of the top surface of subgrade based on the nonuniform distribution of resilient modulus in subgrade. This paper takes the fully weathered granite soil as the research object. Firstly, the soil density of different layers of the subgrade structure is calculated by the degree of compaction of different subgrade layers. Secondly, the overlying load of each point in the subgrade is determined based on the quality of subgrade. Thirdly, the subprogram of the finite element software is compiled and redeveloped based on the elastic constitutive model, and the calculation method for the resilient modulus of each point in the subgrade under the traffic load is proposed when the convergence criterion is set up. Finally, according to the deflection equivalence of the elastic double layer and elastic half‐space, the calculation and control methods for equivalent resilient modulus of the top surface of subgrade under nonuniform stress distribution are put forward, and the field verification tests are carried out. The results show that the error range between numerical calculation and field measurement of equivalent resilient modulus of subgrade is 10%. It shows that the calculation method for equivalent resilient modulus of subgrade proposed in this study is reasonable and effective. The equivalent resilient modulus of subgrade decreases with the increase of traffic load. And the resilient modulus of subgrade soil increases with the increase of subgrade depth. The resilient modulus of subgrade soil has a significant impact on the equivalent resilient modulus of subgrade after the overlaying gravel layer. The equivalent resilient modulus of the subgrade with the gravel layer increases with the increase of the resilient modulus of the subgrade soil.

Modeling the Resilient Modulus Variation of In Situ Soils due to Seasonal Moisture Content Variations

LTRC is conducting a research project to determine the seasonal variation of subgrade resilient modulus (MR) in an effort to implement PavementME. One objective of that project, which is presented in this paper, was to locally calibrate the Enhanced Integrated Climate Model’s (EICM Fenv) curve for seasonal subgrade MR changes. Shelby tube sampling was conducted on six different roadways to a depth of approximately 7.92 m beneath the shoulder pavement’s base course. The AASHTO T‐99 MR test method was used on all samples with an additional eight specimens being tested with NCHRP 1–28A MR test method. Four soils from Louisiana which were not from the six roadways were also tested and included in the analyses. Once the MR tests were completed and plotted, it was noticed that there was a rather large scatter (R2 = −0.266) around the EICM Fenv curve. The authors hypothesized that this occurred due to the density differences between in situ and remolded specimens. Further analyses confirmed this hypothesis. LTRC developed a new method based on the EICM Fenv method to determine the relationship between changes in subgrade MR as a function of changes in moisture content with the in situ moisture content and MR used as the control. This method differs from the EICM Fenv in that the EICM Fenv uses optimum moisture content as the controlling parameter. The LTRC method can be used for design purposes as well as level 2 inputs into the EICM.

Pavement Design Comparative Analysis Using Bina Marga Design Method (Pt T-01-2002-B) And Pavement Design Manual (No.04/Se/Db/2017) (Case Study: Trisakti Port - Liang Anggang)

Traffic load repetition is the main variable in flexible pavement layers design. In addition, a soil bearing capacity factor is also required for determining the thickness of the flexible pavement layer so that the pavement had been designed will be in good perfomance during the that period. The determination of thickness layers using the 2002 method (Pt T-01-2002 B) is based on the traffic load during the design period and subgrade resilient modulus value. Meanwhile the 2017 method (Pavement design manual No. 04/SE/Db/2017), layers thickness was determined based on traffic load and CBR subgrade value. Based on the calculation using both methods, the pavement layers thickness with the pavement design manual 2017 method is more thick than 2002 method. While the ESAL calculation using both methods, the 2002 method value is more larger than the pavement design manual 2017 method.