Improving the Strength of Cold Mix Asphalt Concrete through Numerical Optimization

Abstract

Compared to conventional hot-mix asphalt concrete (HMAC), the production and placement of cold mix asphalt concrete (CMAC) is a more environmentally sustainable alternative that offers significant reduction in energy use and greenhouse gas emissions. It has been widely used; however, its poor mechanical performance restricts its use to low traffic roads and temporary repair works. This limitation emphasises the need for performance enhancement to enable broader structural application, particularly in urban and high traffic roads. Opportunities exist to improve CMAC properties through the incorporation of suitable additives. Introduction of additives to the conventional bitumen mix requires systematic investigation and optimized design approaches capable of addressing complex mix behaviour. Conventional trial and error method are experimentally intensive, time consuming, and inadequate for capturing the nonlinear interactions among multiple constituents. This study uses the statically driven mix design optimization framework using Response Surface Methodology (RSM) for CMAC mix design optimization incorporating hydrated lime, silica fume, bitumen emulsion, and recycled PET (polyethylene terephthalate). A total of 83 different mixtures and 161 specimens were prepared and tested as per American Association of State Highway and Transportation Officials (AASHTO) standards for Marshall stability, flow, density and air-void content. The optimized job mix, comprising of 1.0% hydrated lime, 1.5% silica fume, 9.45% bitumen emulsion, and 0.35% recycled PET fibres (all percentages based on total dry weight of aggregate), achieved a Marshall stability of 7.36 kN. The results demonstrated that the optimized mix achieved mechanical performance comparable to that of HMAC. The incorporation of the mineral fillers like hydrated lime and silica fume improved the adhesion between emulsion and the aggregates, while PET fibres improved the tensile strength, bonding properties and resistance to deformation. These finding highlights the potential of using modified CMAC mixtures for high traffic roads. The developed optimization framework provides a practical decision-support tool for pavement engineers, enabling more systematic, efficient and sustainable CMAC mix design while reducing reliance on conventional trial-and-error approaches. RSM enabled the development of predictive models that captures both linear and nonlinear relationship, significantly reducing experimental effort while improving analytical precision. While the prediction model performs well within the calibrated range, further validation is required to assess long term durability, moisture resistance and field performance under traffic and field conditions.