Adoption of Computational Response Surface Methodology (RSM) in Asphalt Research - I W. Sutapa; Hikmayani; G. R. Lempang; L. O. Kadidae; I N. Sudiana; A. Bandjar (Faculty Mathematics and Natural Sciences, Halu Oleo University, 2026)
Response Surface Methodology (RSM) is a powerful statistical and computational tool used to model and optimize complex systems involving multiple variables and responses. In asphalt research, RSM has gained significant attention for its efficiency in optimizing mix design, evaluating modified asphalt properties, and analyzing the effects of processing and environmental conditions. This review highlights the application of RSM in key aspects of asphalt studies, including experimental design (CCD, BBD, factorial), mathematical modeling, response visualization, and multi-response optimization. The use of RSM enables researchers to identify optimal binder content, additive dosage, and process conditions with reduced experimental effort. Additionally, the integration of RSM with contour and 3D surface plots provides intuitive understanding of parameter interactions. Despite its advantages, RSM faces limitations such as model assumptions, sensitivity to data distribution, and challenges in field validation. Future research is encouraged to combine RSM with artificial intelligence techniques for more robust predictions and to validate laboratory models under real-world conditions. Overall, RSM remains a valuable methodology for advancing asphalt technology, especially in the development of sustainable and high-performance pavement materials.