scholarly journals Comparative Study of Marshall Properties and Durability of Superpave and AC-WC Pavement by Using Starbit E-55 and Pen 60/70

2021 ◽  
Vol 933 (1) ◽  
pp. 012003
Author(s):  
M A Hadi ◽  
M Fauziah ◽  
Subarkah

Abstract The challenges in high traffic volumes, excessive loads and environmental problems need to be anticipated through modification of pavement materials, both for gradations as well as for binding materials. This paper presents a comparative study of Marshall characteristics and its durability between Superpave and AC-WC graded mixtures using Starbit E-55 and Pen 60/70. The experimental laboratory begins with physical testing of aggregate and bitumen material, then, determination of optimum bitumen content for each of the mixture was conducted, and Marshall Standard and Index of Retained Strength (IRS) at optimum bitumen content were then run. Results show that in general Superpave mixture with Starbit has a significantly better Marshall performance in terms of its stability and Marshall Quotient, however, its volumetric properties, such as void in total mix, void filled with asphalt and density were slightly lower quality than AC-WC. It also has proven that Starbit E-55 gives a significantly better mechanical performance on Superpave mixture rather than on AC-WC, with only a slightly lower volumetric quality. Superpave mixtures tend to have a more durable performance then the AC-WC mixture. It also has been found that Starbit E-55 generates a more significantly durable mixture of Superpave rather than on AC-WC.

2017 ◽  
pp. 95-98
Author(s):  
N. L. Venediktov ◽  
A. N. Venediktov ◽  
I. M. Kovenskiy

The experimental laboratory-scale plant for the fatigue testing of samples with electrolytic coating were designed and constructed. Shows the kinematic scheme and considered the principle of the plant. The dependences for determination of test parameters and the method of testing samples with coatings under variable loads were obtained.


2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.


2021 ◽  
pp. e00242
Author(s):  
Raquel Lahoz ◽  
Juan Pelegrín Sánchez ◽  
Silvia Górriz ◽  
Pilar Calmarza

2021 ◽  
pp. 115252
Author(s):  
Mauricio Maldonado ◽  
Edilma Sanabria ◽  
Astrid Velasquez-Silva ◽  
José Luis Casas-Hinestroza ◽  
Miguel A. Esteso

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