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Statistical Regression and Soft Computing in Estimating Permanent Deformation of HMA with the Inclusion of AIMS Coarse Aggregate Angularity Index
Estimation of the effects of aggregate angularity on permanent deformation of asphalt concrete is understudied because of its complexity. Existing permanent deformation models are based on linear and non-linear regression techniques and sometimes are not capable of dealing with a multiplicity of factors that contributes to hot mix asphalt (HMA) behaviour. This paper presents expressions that estimate the effects of aggregate angularity and other common factors on the permanent deformation of HMA. Laboratory repeated load creep test data of cylindrical samples of varying coarse aggregate angularity classifications (rounded, sub-rounded, sub-angular, angular), heights (100, 150 mm), temperature (25, 45°C) and loading (70, 200 kPa) were used. The study used non-linear regression analysis and soft computing technique to develop the mathematical expression. Statistically these two techniques provided acceptable prediction results. However, parametric and perturbation analysis of the proposed equations indicated that soft computing is the most accurate since it is capable of capturing the realistic dynamics of the physical process being modelled. The influence of a particular angularity classification maybe useful to road engineers when evaluating the mechanical behaviour, mix designs, field quality control, and assurance as well as the performance prediction of asphalt concrete.
Statistical Regression and Soft Computing in Estimating Permanent Deformation of HMA with the Inclusion of AIMS Coarse Aggregate Angularity Index
Estimation of the effects of aggregate angularity on permanent deformation of asphalt concrete is understudied because of its complexity. Existing permanent deformation models are based on linear and non-linear regression techniques and sometimes are not capable of dealing with a multiplicity of factors that contributes to hot mix asphalt (HMA) behaviour. This paper presents expressions that estimate the effects of aggregate angularity and other common factors on the permanent deformation of HMA. Laboratory repeated load creep test data of cylindrical samples of varying coarse aggregate angularity classifications (rounded, sub-rounded, sub-angular, angular), heights (100, 150 mm), temperature (25, 45°C) and loading (70, 200 kPa) were used. The study used non-linear regression analysis and soft computing technique to develop the mathematical expression. Statistically these two techniques provided acceptable prediction results. However, parametric and perturbation analysis of the proposed equations indicated that soft computing is the most accurate since it is capable of capturing the realistic dynamics of the physical process being modelled. The influence of a particular angularity classification maybe useful to road engineers when evaluating the mechanical behaviour, mix designs, field quality control, and assurance as well as the performance prediction of asphalt concrete.
Statistical Regression and Soft Computing in Estimating Permanent Deformation of HMA with the Inclusion of AIMS Coarse Aggregate Angularity Index
Leon, Lee P. (author)
International Conference on Transportation and Development 2020 ; 2020 ; Seattle, Washington (Conference Cancelled)
2020-08-31
Conference paper
Electronic Resource
English
Development of Permanent Deformation Models with the Inclusion of AIMS Coarse Aggregate Angularity
| Springer Verlag | 2021
| British Library Online Contents | 2011