This study aims to contribute to the prediction of the pervious concrete (PC) behavior considering the density control and coarse aggregate granulometry variations. Twenty PC mixtures considering four coarse aggregate granulometries (Dm ' ax and granulometric distribution) and varying compaction levels were produced. The molding process follows the Risson et al. (2021) method, which focuses on density control, for then asses the density, porosity, permeability, compressive strength, and flexural tensile strength of these mixtures. An analytical model to correlate the density (rho) and the compaction blows number (N) was proposed. This model demonstrates that grain size influences both the minimum and maximum density, with smaller sizes (B0 =9.5 mm ) achieving higher densities during compaction. Furthermore, a comprehensive protocol for the analysis of PC, enabling a thorough characterization of PC using the same specimen was introduced. To assist construction professionals, a user-friendly graphical tool that offers predictive insights into PC performance (both hydraulic and mechanical) based on the key correlations found in the literature was devised. This tool reveals that, for hydraulic and mechanical correlations, granulometry is not the preeminent factor, and the data can be adjusted using a single curve that depends on the initial production conditions of the PC. This valuable tool aids in predicting performance in two ways: first, by considering the initial compaction level of the PC to determine its hydraulic and mechanical properties, and second, by determining the compaction level required to meet specific technical requirements.