Key parameters controlling strength and resilient modulus of a stabilised dispersive soil

2021 ◽  
pp. 1-16
Author(s):  
Rubén Alejandro Quiñónez Samaniego ◽  
Hugo Carlos Scheuermann Filho ◽  
Mariana Tonini de Araújo ◽  
Giovani Jordi Bruschi ◽  
Lucas Festugato ◽  
...  
Keyword(s):  
Resilient Modulus ◽  
Dispersive Soil

Climatic effect on resilient modulus of recycled unbound aggregates

2015 ◽  
Vol 16 (4) ◽  
pp. 836-853 ◽  
Author(s):  
Ali Soleimanbeigi ◽  
Ryan F. Shedivy ◽  
James M. Tinjum ◽  
Tuncer B. Edil
Keyword(s):  
Resilient Modulus ◽  
Climatic Effect ◽  
Unbound Aggregates

scholarly journals Evaluation of Measured and Predicted Resilient Modulus of Rubberized Stone Mastic Asphalt (SMA) Modified with Truck Tire Rubber Powder

2021 ◽  
pp. e00633
Author(s):  
Sajjad Noura ◽  
Abdulnaser M. Al-Sabaeei ◽  
Gailan Ismat Safaeldeen ◽  
Ratnasamy Muniandy ◽  
Alan Carter
Keyword(s):  
Resilient Modulus ◽  
Tire Rubber ◽  
Rubber Powder ◽  
Mastic Asphalt ◽  
Truck Tire ◽  
Stone Mastic Asphalt

scholarly journals Performance Evaluation of WMA Containing Re-Refined Acidic Sludge and Amorphous Poly Alpha Olefin (APAO)

2021 ◽  
Vol 13 (6) ◽  
pp. 3315
Author(s):  
Mansour Fakhri ◽  
Danial Arzjani ◽  
Pooyan Ayar ◽  
Maede Mottaghi ◽  
Nima Arzjani
Keyword(s):  
Resilient Modulus ◽  
Permanent Deformation ◽  
Road Construction ◽  
Moisture Damage ◽  
Asphalt Mixtures ◽  
Modified Bitumen ◽  
Alternative Source ◽  
The Road ◽  
Marshall Test ◽  
Alpha Olefin

The use of waste materials has been increasingly conceived as a sustainable alternative to conventional materials in the road construction industry, as concerns have arisen from the uncontrolled exploitation of natural resources in recent years. Re-refined acidic sludge (RAS) obtained from a waste material—acidic sludge—is an alternative source for bitumen. This study’s primary purpose is to evaluate the resistance of warm mix asphalt (WMA) mixtures containing RAS and a polymeric additive against moisture damage and rutting. The modified bitumen studied in this research is a mixture of virgin bitumen 60/70, RAS (10, 20, and 30%), and amorphous poly alpha olefin (APAO) polymer. To this end, Marshall test, moisture susceptibility tests (i.e., tensile strength ratio (TSR), residual Marshall, and Texas boiling water), resilient modulus, and rutting assessment tests (i.e., dynamic creep, Marshall quotient, and Kim) were carried out. The results showed superior values for modified mixtures compared to the control mix considering the Marshall test. Moreover, the probability of a reduction in mixes’ moisture damage was proved by moisture sensitivity tests. The results showed that modified mixtures could improve asphalt mixtures’ permanent deformation resistance and its resilience modulus. Asphalt mixtures containing 20% RAS (substitute for bitumen) showed a better performance in all the experiments among the samples tested.

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

2021 ◽  
pp. 036119812110237
Author(s):  
Laura Camarena
Keyword(s):  
Machine Learning ◽  
Material Properties ◽  
Resilient Modulus ◽  
Nonlinear Behavior ◽  
Machine Learning Techniques ◽  
Design Parameters ◽  
Nonlinear Parameters ◽  
State Highway ◽  
Learning Techniques ◽  
State Highway Agencies

The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

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