Effect of Soil Parameters on Resilient Modulus Using Cyclic Tri-Axial Tests on Lateritic Subgrade Soils from Dakshina Kannada, India

Kumar, Anil; George, Varghese

doi:10.1007/s10706-018-0550-7

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…This result was a good indication of granular materials as expected for A-1-b/SP soil. This is referred to as the 'strain-hardening' effect due to the reorientation of the grains into the denser state [30][31][32][33][34]. For the fine-grained soil at L72-1, the M R decreased with increasing cyclic stress, and a lower M R value was obtained.…”

Section: Resilient Modulus Test Resultsmentioning

confidence: 99%

“…It is important to note that the resilient modulus for subgrade soils is highly stress dependent, with the stress state being a function of the position of the material in the pavement structure and applied traffic loading [30]. The M R of fine-grained soil generally decreases with increasing deviatoric stress (this is referred to as 'stress-softening' behavior; with increased stress, deformation increases and modulus decreases), whereas the M R of coarse-grained soils generally increases with increasing deviatoric stress (this is referred to as the 'strain-hardening' effect due to the reorientation of the grains into the denser state) [30][31][32][33][34]. Model forms characterizing the relationship between M R and deviatoric stress are bi-linear, hyperbolic, semi-log, and log-log [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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

Islam

Gassman

2023

Geotechnics

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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.

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Section: Resilient Modulus Test Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Islam

Gassman

2023

Geotechnics

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“…Já nos solos laterıticos, a cimentação das partıćulas produzida pela laterização tem o efeito de reduzir a de-pendência de MR com estado de tensões. Ao mesmo tempo, o próprio valor do módulo tende a aumentar, juntamente com a resistência (DNIT, 2006;Oliveira, 2007;Kumar e George, 2018;Serra et al, 2020).…”

Section: Comportamento Resilienteunclassified

Análise da aplicabilidade de Sistema de Classificação Universal para solos lateríticos utilizados em camadas de pavimentos

2023

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O desenvolvimento de um novo procedimento de classificação para solos lateríticos, que leve suas propriedades mineralógicas e físicas em consideração e, ademais, as concepções apropriadas do meio ambiente tropical úmido, permitirá prever suas utilizações em camadas de rodovias com uma porcentagem mais elevada. A pesquisa em questão avalia o potencial uso da Metodologia de Classificação Universal de Solos Lateríticos para fins de construção de estruturas de rodovias. O procedimento metodológico compreendeu um programa de ensaios para a classificação e avaliação do comportamento resiliente dos solos. Com base no comportamento resiliente e nas ponderações sobre as propriedades de deformação permanente adotadas, a análise mecanística-empírica indicou bom comportamento dos solos analisados quando empregados em camadas de base em tráfegos menores. Entretanto, mesmo apresentando boas condições, os solos poderiam necessitar de estabilização, seja granulométrica com adição de solo granular, e/ou química com incorporação de cal ou cimento, principalmente na tentativa de combater a retração.

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“…The application of unconventional road construction methods is widespread worldwide. However, such projects are still used extremely infrequently in India owing to a lack of legislation and explicit guidelines, uncertainties in the outcomes, and the functioning of roads throughout their life span [41,42].…”

Section: Study Areamentioning

confidence: 99%

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Nagaraju¹,

Bahrami

Chinta

et al. 2023

Buildings

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The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties.

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Effect of Soil Parameters on Resilient Modulus Using Cyclic Tri-Axial Tests on Lateritic Subgrade Soils from Dakshina Kannada, India

Cited by 14 publications

References 18 publications

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

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

Análise da aplicabilidade de Sistema de Classificação Universal para solos lateríticos utilizados em camadas de pavimentos

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

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