Jamal Yousaf

Plastic materials are extensively utilized in various aspects of daily life. However, the substantial amount of plastic waste generated can cause significant global environmental issues without efficient waste management practices. To address this problem, the utilization of waste plastics in high-value applications such as highway construction through hot mix asphalt (HMA) has been deemed ideal. Polyethylene terephthalate (PET), low-density polyethylene (LDPE), and high-density polyethylene (HDPE) are the three most prevalent types of plastic waste found in municipal solid waste. The impact of plastic-type and content on the mechanical, volumetric, and durability properties of hot mixed asphalt (HMA) was investigated by utilizing varying plastic contents of PET, LDPE, and HDPE. Tests were conducted in accordance with ASTM standards on twelve HMA mixtures made with an optimal binder content (OBC) of 4.3%, and plastic contents of 3%, 6%, 9%, and 12% by weight of OBC. Marshall stability and flow tests were conducted to ascertain the optimal plastic content for each tested mixture. The findings indicated that the stability and flow values improved as the plastic content was raised. 9% LDPE mix gave a maximum Marshal stability value of 12 kN. Additionally, as the plastic content increased, the values for air voids and voids filled with asphalt decreased in all 12 mixes tested. This research aligns with the United Nations 2030 sustainable development goals (SDGs), including SDGs 9, 11, 12, 13, 14, and 15, and presents a viable method for implementing the 3Rs approach towards sustainable plastic waste management.

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B_x, B_y) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (B_x, B_y), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (B_x) bandwidth was found to increase with increasing defect width and the y-component (B_y) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (B_x). The defect areas showed a higher regression coefficient (R² = 0.9079) for the x-component (B_x) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.