journal article·2015-10-02

Prediction of permanent deformation model parameters of unbound base course aggregates under repeated loading

Makhaly Ba, James M. Tinjum, Meissa Fall

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Abstract

Prediction of rutting is a key element in the newly developed mechanistic-empirical pavement design guide (M-EPDG). The model developed by Tseng and Lytton (1989. Prediction of permanent deformation in flexible pavement materials. ASTM STP 1016, pp. 154–172: American Society for Testing and Materials) is used in the M-EPDG to estimate the permanent deformation of unbound materials, but does not predict parameters of the model satisfactorily. This research was conducted to study the effect of water content on permanent deformation model parameters of unbound aggregate base layers. Different aggregates were compacted at different water contents and were subjected to repeated loading tests. For the material tested, the permanent deformation increases rapidly when the water content exceeds that of the optimum. Under optimum water content, wopt, limestone is more resistant to permanent deformation than basalt and quartzite. However, above wopt, the permanent deformations of limestone grow rapidly and become higher than those of Basalt. This indicates a high sensitivity of limestone to water content, which increases the plastic deformation. An exponential relationship is more appropriate to describe the evolution of permanent deformation as a function of water content and gives a higher coefficient of determination. Assuming that the rutting parameters decrease exponentially with increasing water content, empirical relationships are proposed for predicting these parameters. These results should be used for further development of the M-EPDG rutting model.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Geotechnical Engineering and Underground Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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