Permanent Deformation Characteristics of Silty Sand Subgrades from Multistage RLT Tests
2015 (English)In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268XArticle in journal (Refereed) Published
Rutting is one of the main forms of distresses in thin flexible pavement structures, often associated with accumulation of permanent deformation in unbound granular layers and subgrade soils under traffic loading. Realistic prediction of surface rutting requires models that can reliably capture the cumulative plastic deformation of pavement unbound layers under repeated loads. This study presents an evaluation of three models that incorporate the time-hardening concept for prediction of permanent deformation of silty sand subgrade materials. A series of multistage repeated load triaxial (RLT) tests, in which the material underwent a wide range of continuous stress conditions, were carried out on two silty sand subgrades. The RLT tests were conducted at four different moisture contents in which pore suctions were measured throughout the test. In the modelling of the permanent deformations, the effective stress approach was used taking into account the effects of soil suctions. The material parameters of the predictive models were optimised using the RLT test data and the effect of moisture content (matric suction) on the permanent deformation characteristics of the materials and the predictive model parameters were investigated. Generally, it was observed that the modified models that are based on the shakedown approach performed reasonably well in capturing the permanent deformation behaviour of the selected subgrade materials with minor discrepancies between the models. This indicates that using multistage RLT tests can be an efficient approach for characterising the permanent deformation behaviour of subgrade soils.
Place, publisher, year, edition, pages
Taylor & Francis , 2015.
Permanent deformation, subgrade soil, multistage repeated load triaxial test, moisture content, matric suction, modelling
IdentifiersURN: urn:nbn:se:kth:diva-162094DOI: 10.1080/10298436.2015.1065991OAI: oai:DiVA.org:kth-162094DiVA: diva2:796874
QC 201603102015-03-202015-03-202016-03-10Bibliographically approved