Response Surface Methodology Optimization in Asphalt Mixtures: A Review

Abstract

The application of statistical modeling and optimization approaches such as response surface methodology (RSM) is important for the excellent potential to tackle different constraints and goals and the analysis of the relationships between independent factors influencing a particular response. This chapter provides a simple yet detailed literature review on the utilization of RSM for the design of experiments, modeling, and optimization of virgin and alternative materials into asphalt binder and mixtures for sustainability. Meanwhile, an in-depth analysis based on the literature reviewed in terms of asphalt binder modification employing RSM with various independent parameters were summarized. Also, a critical review of the application of RSM to optimize the engineering and mechanical performance characteristics of asphalt concrete mixtures is presented in this chapter. The current chapter concluded that the use of RSM statistical analysis in a highway materials perspective provides a broader understanding of the factors that control pavement performance throughout the pavement service life.

Modelling of the Effects of Antimicrobial Agents on the Compressive Strength of High-Performance Concrete Using Response Surface Methodology

“…This is validated by the results obtained from the ANOVA in Table 2 which shows that based on the F-value of 23.4 and low p-values for the model as well as model terms A and B, both ZnO and TiO 2 had significant effect on the compressive strength of HPC. According to [22,27] the probability of low and F-and p-values of such magnitude being the result of noise is extremely low, at only 0.01%. Therefore, suggesting that the quadratic model presented as Equation ( 1) and developed based on the experimental values are well adequate to predict the effects of both NPs on the 28-day compressive strength of HPC.…”

“…Up until now, in-stances where these substances have been implemented, the emphasis has primarily been on their individual effects, with only a limited number of attempts made to investigate the combined effects of these antimicrobial agents on the mechanical properties of concrete [19][20][21]. The use of Response Surface Methodology (RSM) provides an avenue to explore this option most especially in situations where several input variables (that is, independent variables) determines the characteristics or performance measure of a process [22,23]. The effectiveness of this approach has been demonstrated by various authors [22,24,25].…”

“…The use of Response Surface Methodology (RSM) provides an avenue to explore this option most especially in situations where several input variables (that is, independent variables) determines the characteristics or performance measure of a process [22,23]. The effectiveness of this approach has been demonstrated by various authors [22,24,25]. RSM helps to understand individualistic and synergistic relationships between input fac-tors and output also known as responses.…”

Presenting a Model to Predict Cracking Rate in Geocomposite-Reinforced Asphalt Overlays Under Different Loading and Temperature Conditions

“…The coe cient of determination (R 2 ) and the lack of t (LOF) are commonly presented to show the model's validity [39,40]. The LOF indicates that how much the points are not well distributed around the model, and the model cannot be used to predict the values of the dependent variable.…”

Effect of nanosized titanium dioxide and zinc oxide as antimicrobial agents on early compressive strength of high-performance concrete

“…With the model F-value of 60.24 obtained from the analysis of variance (ANOVA), it can be concluded that the model is statistically significant. The probability of an F-value of this magnitude being the result of noise is extremely low, at only 0.01% [27][28][29][30].…”