Mohammed Ghazi Ibrahim

Year: 2025

Academic Degree: Master

Supervisor Type: Supervisor

Supervisor State: In Progress

Bachelor Degree in Building and Construction Engineering

Oct 1, 1997 - Jun 27, 2001

Structural Engineering

Higher Diploma degree in Bridges Engineering

Oct 1, 2001 - Jul 20, 2002

Master degree in Highway and Transportation Engineering

Oct 1, 2002 - Jun 5, 2006

Transportation planning and design

PhD in Highway and Transportation Engineering

May 5, 2014 - Mar 20, 2019

Evaluation of measured and predicted resilient modulus of rubberized Stone Mastic Asphalt (SMA) modified with truck tire rubber powder

Dec 1, 2021

publisher Case Studies in Construction Materials

DOI https://doi.org/10.1016/j.cscm.2021.e00633

Volume Volume 15

Rubberized asphalt is known for its elastic deformation recovery and good resilience in response to loads owing to the elastic characteristics of tire rubber powder. There are several methods for the prediction of the stiffness modulus of asphalt mixtures. However, there are limited studies on predicting the stiffness modulus incorporating both wet and dry rubberization methods based on the available methods of Asphalt Institute (AI) and IDOT (Illinois department of transportation). In this research, Stone Mastic Asphalt (SMA) mixtures were modified with truck tire rubber powder (TRP) with two different processes: SMA-WP (SMA mixtures modified in the wet process) and SMA-DP (SMA mixtures modified in the dry process). In both methods, 3%, 6%, and 9% of TRP were used for modification, and the performance of the control and modified mixtures was evaluated under indirect tensile strength (ITS) and indirect tensile stiffness modulus (ITSM) tests. Finally, the results of ITSM were compared to predicted resilient modulus based on the Asphalt Institute (AI) and Illinois Department of Transportation (IDOT). The experiments revealed that SMA-DP mixes have higher ITS than SMA-WP. At the same time, both methods showed a decrease in ITS as TRP content increases. Furthermore, the SMA-WP samples showed a lower phase angle than SMA-DP samples, indicating higher elasticity for the mixtures. In addition, SMA-WP showed lower horizontal deformation than SMA-DP, which helps reduce rutting on the surface layer. Finally, the prediction results showed that the IDOT method could not predict the Stiffness Modulus, while the AI method was more accurate and can be used for prediction

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