A new mechanistic framework for evaluation of cyclic behaviour of unsaturated unbound granular materials
© Int. J. of GEOMATE. The unsaturated unbound granular materials (UUGMs) as a base course layer play a major role in the overall performance of the multi-layered flexible pavement system. In theory, the cyclic response of UUGMs (under traffic loading) depends greatly upon moisture content and matric...
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Main Authors: | , , , , |
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Format: | Journal |
Published: |
2017
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Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85027444474&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41104 |
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Institution: | Chiang Mai University |
Summary: | © Int. J. of GEOMATE. The unsaturated unbound granular materials (UUGMs) as a base course layer play a major role in the overall performance of the multi-layered flexible pavement system. In theory, the cyclic response of UUGMs (under traffic loading) depends greatly upon moisture content and matric suction, but these effects have been conventionally difficult to quantify. This paper presents a new mechanistic framework for characterising the cyclic behaviour of UUGMs with differing levels of moisture content and density, and in various in-service stress conditions in pavements without real cyclic testing on UUGMs. These parameters would typically be considered to gain a more precise pavement evaluation. In this study, a normalisation procedure was performed to incorporate matric suction into the cyclic response evaluation of UUGMs with a range of moisture contents and without actual suction measurement. A n ew soil suction model with three density-independent parameters was derived from a series of static triaxial compression tests based on the traditional triaxial facilities. The suction model developed can be used, with the Bishop effective stress constitutive model, to successfully evaluate the resilient response of UUGMs under the stated conditions. With the inclusion of matric suction, this new mechanical framework provides a more reliable resilient modulus prediction model. |
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