A combination of DOE – multi-criteria decision making analysis applied to additive assessment in porous asphalt mixture

Abstract

Porous asphalt (PA) mixture is setting off an attractive alternative to be used as surface layer in pavements due to the many profits this mixture provides in terms of noise, safety and environmental aspects. Nonetheless, its use is quite limited due to its low durability in comparison to dense graded asphalt mixtures, reason for which the incorporation of different additives is recommended. In this study, the impact of different types of binders and additives in porous asphalt mixtures are experimentally assessed. A total of 54 experimental designs were defined through the Taguchi design of experiments method. Total air voids, interconnected air voids, particle loss in dry and wet conditions and binder drain down were the responses obtained from the experimental tests. Since more than one response was obtained, three Multi-Criteria Decision-Making (MCDM) methods were performed to turn the multiple response optimisation problem into single-objective optimisation problem. Based on the experimental results and statistical analysis, polymer modified binders improve the ravelling resistance without affecting the functional performance of the mixture and without presenting the risk of binder drain down.

Data-Driven Prediction of Cement-Stabilized Soils Tensile Properties

“…Design Of Experiment (DOE) techniques have become crucial for dosage evaluations and the mixing optimization of CSSs [30,31]. DOE techniques, for example, can be implemented in numerical works to assess the influence of sampling on the accuracy of prediction models [29].…”