Prediction Of Resilient Modulus Research Articles

Prediction of resilient modulus of compacted saprolitic soils by CBR approach for road pavement subgrade: a re-examination

The scope of the study as presented in this paper is to re-examine the prediction of resilient modulus (M R) by the classical California Bearing Ratio (CBR) approach, specifically for compacted saprolitic subgrade soils. Through the extensive experiments carried out for this research, a more precise model yielded to estimate M R based on CBR values and the relative degrees of soil compaction. Likewise, comments are also made critically for the suitability of using the well-known models which were developed during the past decades. In addition to the core results, this study has produced a number of other important findings: (1) the influence of soil compaction densities on CBR and M R is relatively significant when soil is relatively dry, whereas saturation ratio becomes a dominant factor while the soil is in wetter condition; (2) when the saturation ratio becomes dominant, the soil material could rarely reach a CBR of 5% or above, which was often regarded as competent materials by many design standards; (3) the moisture ratio (R m) of the saprolitic subgrade materials is one of the useful parameters in estimating M R and (4) the distinctive behaviours of saprolitic subgrade soils under CBR, unconfined compressive strength and repeated load tests are considered to be related to the degree of freedom of volume change for soil samples during the tests.

Undrained deformation behavior of saturated soft clay under long-term cyclic loading

Subgrade soils of traffic infrastructures are subjected to large numbers of load applications at a stress level below their shear strength. It is therefore of great practical relevance to study the deformation behavior of soft clay under long-term cyclic loading. In this study, a series of monotonic triaxial tests and long-term cyclic (50,000 cycles) triaxial tests have been carried out to investigate the undrained deformation behavior of undisturbed soft clay from Wenzhou, China. The stress–strain hysteretic loop, resilient modulus and permanent strain of the tested samples were found significantly dependent on CSR and confining pressure. With an increase of CSR and confining pressure, the resilient modulus decreases more significantly with increasing number of cycles and the accumulation rate of permanent strain increases. Furthermore, the shape of the stress–strain hysteretic loop almost remains unchanged and the resilient modulus tends to a steady value after a large number of cycles. Based on the experimental results, two equations are established for the prediction of long-term resilient modulus and permanent strain. Finally, a new critical value of 0.65 is suggested for CSR. When CSR>0.65, the resilient modulus for large number of cycles is reduced to a so called “asymptotic stiffness” and the accumulation rate of permanent strain significantly increases.

Estimation of Resilient Modulus of Unbound Granular Materials from Senegal (West Africa)

The resilient modulus (Mr) is an important parameter which describes the mechanical behavior of unbound granular materials. However, this parameter can be determined from physical properties. This paper presents the relationship between resilient modulus and physical properties of Quartzite from Bakel (GB), Basalt from Diack and Bargny and Bandia limestones. Simple and multiple regression method by stepwise are used to establish linear and nonlinear relations to predict the resilient modulus. The results showed no significant correlation for Basalt, a weak estimation of the modulus for GB and good prediction of resilient modulus for limestone. These results also showed that the model of Uzan is more suitable to predict the resilient modulus than NCHRP model and the resilient modulus is better predicted in nonlinear relationship.

Use of Recycled Concrete in Hot Mix Asphalt and an ANN model for Prediction of Resilient Modulus

Environmental wastes increase more and more as a result of household and industrial waste production and landfill sites for those wastes are decreasing progressively. Environmental pollution would also be prevented by use of these wastes in various construction materials or structures. In this study, the effects of recycled concrete (RC) on the properties of Hot Mix Asphalt (HMA) are investigated. For this purpose, RC was added to asphalt concrete in 10, 20, 30 and 40 percent by weight of aggregate. The Marshall Stability (MS), flow, resilient modulus (RM), and creep tests were applied to the specimens. MS and flows slightly decreased with the addition of RC. RM values for 10 percent RC was higher than the Control Specimen (CS) and the others. RM values decreased with increasing RC. RC specimens showed increases in terms of creep stiffness compared with CS. In addition, RM test results for different test conditions are evaluated by the Artificial Neural Network (ANN) method. The results of the ANN model of RM with a coefficient of determination of 0.98 conformed very closely to those obtained by the experimental results. Consequently it can be used as an aggregate in RC concrete and the modelling can be made by means of ANN. In this way instead of being an environmental pollution, concrete waste can be used as a construction material in the context of the sustainable environment

INFLUENCE OF AMBIENT TEMPERATURE ON STIFFNESS OF ASPHALT PAVING MATERIALS

Asphalt pavement properties in Iraq are highly affected by elevated summer air temperatures. One of these properties is stiffness (resilient modulus). To explain the effect of air temperatures on stiffness of asphalt concrete, it is necessary to determine the distribution of temperatures through the pavement asphalt concrete layers. In this study, the distribution of pavement temperatures at three depths (2cm,7cm, 10cm) below the pavement surface is determined by using the temperature data logger instrument. A relationship for determining pavement temperature as related to depth and air temperature has been suggested. To achieve the objective of this thesis, the prepared specimens have been tested for indirect tension in accordance with ASTM D4123, using the pnuematic repeated load apparatus, in order to determine the values of resilient modulus at three different temperatures (10, 25, 40) °C. From results of testing, it is observed that the resilient modulus decreases with increase in test temperature by a rate of 8.78×10 Psi/C' for asphalt concrete wearing courses. An increase in optimum asphalt content by 0.1% (by weight of total mixture) causes a decrease in resilient modulus by 22% at a temperature of 40C". A statistical model for the prediction of resilient modulus has been developed depending on mixture variables of: asphalt content, asphalt hinder viscosity, surface area of combined aggregates, air voids of compacted mixture and test temperature.

English

Resilient modulus of subgrade soils is an important input in mechanistic pavement design. The primary objective of this work is to investigate the resilient modulus of four typical Victorian fine-grained subgrade soils under traffic-like repeated loading and to suggest empirical predictive models incorporating physical properties and/or strength of the soils along with the stress state. A repeated load triaxial testing procedure was developed, which is capable of collecting resilient and permanent deformation data from the same specimen.  Stress levels for testing were defined as percentages of the confined and/or unconfined soil static strengths. Stress dependency of resilient modulus was studied through the models (such as bilinear model, power model, deviatoris stress model and octahedral stress model) found in the literature and other possible combinations of deviator, confining and octahedral stresses. A semi-logarithmic model was proposed for the prediction of resilient modulus of the fine-grained subgrade soils. Calibration of model constants by soil properties was investigated.  An altervative prediction model was also developed based on unconfined compressive strength and deviator stress. Resilient modulus values were back calculated using both the semi-logarithmic model and the model based on unconfined compressive strength and deviator stress. Predicted values were compared with the measured values. Predictive capability of the proposed models were proven for use in flexible pavement design.   Key words: Resilient modulus, fine-grained subgrade soils, repeated loading, flexible pavement design.

Resilient Modulus for Unsaturated Unbound Materials

ABSTRACT The suitability of the current resilient modulus test protocol (NCHRP 1-28A) for its application to unsaturated soils was assessed. Modifications to the stress state conditions of the protocol are necessary due to the axis-translation needed during the test when measuring matrix suction. This study presents the modulus of unbound materials resulting from tests performed under unsaturated soil conditions. Two different materials were tested. The base material was tested under drained and undrained boundary conditions, while the subgrade was tested under drained boundary condition. The results allowed for the enhancement of the Universal Model for resilient modulus prediction by incorporating suction as a stress state. This model predicts the resilient response of unbound materials as a function of external stress state and matrix suction levels and therefore, it is independent of moisture variation.

Prediction of Resilient Modulus of Granular Subgrade Soils and Subbase Materials using Artificial Neural Network

Prediction of Resilient Modulus of Granular Subgrade Soils and Subbase Materials using Artificial Neural Network

Estimation of Subgrade Soils Resilient Modulus from in-situ Devices Test Results

Abstract Field and laboratory testing programs were conducted to develop resilient modulus prediction models for application in the design and evaluation procedures of pavement structures. The field testing program included conducting several in-situ tests such as Geogauge, Light Falling Weight Deflectometer, and Dynamic Cone Penetrometer (DCP). The laboratory program consisted of performing repeated load triaxial resilient modulus tests, physical properties, and compaction tests on soil samples obtained from tested sections. A total of four subgrade soil types at different moisture-dry unit weight levels were considered. Comprehensive statistical analyses were conducted on the field and laboratory test results. Two sets of models were developed. The first set (direct model) directly relates the laboratory measured resilient modulus values with the results of each of the three in-situ devices, whereas the second set (soil property model) incorporates soil properties in addition to the results of each of the three in-situ devices. A good agreement was observed between the predicted and measured values of the resilient modulus. Furthermore, the results showed that the resilient modulus prediction was enhanced when the soil properties were included as variables within the models. Among the models developed, the DCP-soil property model had the best prediction of resilient modulus followed by the DCP-direct model. The effectiveness of the DCP models were further evaluated during a forensic analysis of pavement section failure in a highway within Louisiana.

Prediction of Resilient Modulus of Granular Subgrade Soils and Subbase Materials using Artificial Neural Network

ABSTRACT Experimental estimation of the modulus is excessively costly and complex owing to the large number of factors that affects the modulus. Thus, it is necessary to determine the resilient modulus of granular pavement materials such as subgrade soils and subbase materials from an empirical model for the design of a lower level pavement system. In this study, ANN (Artificial Neural Network) models were used to develop an empirical model for the resilient modulus of subgrade soils and subbase materials from basic material properties and in-situ conditions related to stresses in reference to data obtained from tests conducted in this study. It can be concluded that the ANN models serve as a reliable and simple predictive tool for the resilient modulus of granular subgrade soils and subbase materials.

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.

Prediction of Resilient Modulus of Cohesive Subgrade Soils from Dynamic Cone Penetrometer Test Parameters

Current pavement design procedures recommend the resilient modulus of subgrade materials for pavement design and analysis. The objective of this paper is to develop two statistical models to predict the resilient modulus of subgrade cohesive soils from the dynamic cone penetration (DCP) test parameters and soil properties. The first model correlates resilient modulus to the dynamic cone penetration test parameters, while the second model correlates resilient modulus to both the dynamic cone penetration test parameters and soil properties. Field and laboratory experiments were conducted at 31 sites in Louisiana that contain four common soil types (A-4, A-6, A-7-5, and A-7-6). Field tests included DCP tests and soil sampling, while laboratory tests included determining basic soil properties and resilient modulus. Statistical analyses were conducted on the collected data, and two statistical models were developed for the prediction of resilient modulus of cohesive subgrade soils from the DCP test parameters and soil properties. The models predicted a separate data set that was not used in their development, indicating the success of the application of the dynamic cone penetration test in evaluating the resilient modulus of pavement subgrade soils. The predicted values obtained from the proposed models corresponded well with the measured resilient modulus values from the repeated load triaxial test.

Resilient Modulus Prediction Models Based on Analysis of LTPP Data for Subgrade Soils and Experimental Verification

Resilient modulus ( MR ) value of a subgrade soil is the primary property needed for pavement design and analysis. It is the elastic modulus based on the recoverable strain under repeated loads, and depends on several factors including soil properties, soil type, and state of stresses. This paper presents prediction equations developed using regression analysis for six AASHTO soil types (A-1-b, A-3, A-2-4, A-4, A-6, and A-7-6) for estimating MR . Data extracted from the Long-Term Pavement Performance Information Management System (LTPP IMS) database for 258 test specimens (approximately 3,870 MR values) collected in 19 states in New England and the nearby regions in the United States and two provinces in Canada were used in this study. A generalized constitutive model that captures the effect of both bulk stress and octahedral shear stress was used to predict the MR of subgrade soils by developing regression equations that relate the k coefficients to the soil physical properties. The prediction models de...

Measurement of Soil Resilient Properties Using Noncontacting Proximity Sensors

The utility of noncontacting proximity sensors for the measurement of small strains in resilient modulus tests is evaluated. The proximity sensor measurements of resilient modulus were compared with those obtained from linear variable differential transformers. The important issue of granular soil sample preparation and its effect on the magnitude of resilient modulus was evaluated. Two methods were proposed to facilitate sample preparation and to provide good contact between the soil and the loading platen. The effect of conditioning sequence on sample integrity was also investigated for the two proposed methods of compaction. In addition to the experimental program, a number of constitutive models, used for prediction of resilient modulus of granular soils, were examined; and model predictions were compared with the test data.

Measurement of Soil Resilient Properties Using Noncontacting Proximity Sensors

The utility of noncontacting proximity sensors for the measurement of small strains in resilient modulus tests is evaluated. The proximity sensor measurements of resilient modulus were compared with those obtained from linear variable differential transformers. The important issue of granular soil sample preparation and its effect on the magnitude of resilient modulus was evaluated. Two methods were proposed to facilitate sample preparation and to provide good contact between the soil and the loading platen. The effect of conditioning sequence on sample integrity was also investigated for the two proposed methods of compaction. In addition to the experimental program, a number of constitutive models, used for prediction of resilient modulus of granular soils, were examined; and model predictions were compared with the test data.