Composite sigmoidal model for asphalt concrete phase angle mastercurve construction.

Jukte, Nishigandha Rajeshwar; Swamy, Aravind Krishna

It is well-known fact that dynamic modulus and phase angle affect distress mechanisms occurring within the pavement. Thus, accurate prediction of dynamic modulus and phase angle at different temperatures and loading frequencies plays a vital role. Towards improving the accuracy of phase angle prediction, present work proposes rational model to describe the shape of phase angle mastercurve. The proposed model is a combination of two sigmoidal functions having seven parameters. To evaluate the effectiveness of the proposed model, phase angle measurements reported in NCHRP Project 9-19 were used. Eight binders and five aggregate gradations originating from 201 distinct asphalt mixtures were identified. The proposed 7-parameter model was compared with well-known sigmoidal, generalised sigmoidal and CAM mastercurve models. Results indicate that proposed model accurately describes the shape of phase angle mastercurve than other models. Statistical analysis showed reasonable correlation among phase angle mastercurve characteristics (peak phase angle and its location) and other variables (i.e. mixture volumetrics and mixture gradation). The mixturewise regression models were also proposed to predict phase angle mastercurve characteristics. Goodness-of-fit indicators verified that the proposed predictive models were able to predict phase angle mastercurve characteristics well.

PREDICTION models; REGRESSION analysis; STATISTICS; ANGLES; PAVEMENTS

International Journal of Pavement Engineering, 2024, Vol 25, Issue 1, p1

1029-8436

Academic Journal

10.1080/10298436.2024.2420249