## 5 Conclusions  
This paper presents an integrated experimental and machine learning approach regarding the effective reuse of industrial waste silica fume as a sustainable mineral filler in asphalt mixtures for base courses of road pavements. Comparative laboratory analysis by varying the SF, OPC, and bitumen contents was carried out. The following conclusions can be drawn based on volumetric, durability, and strength tests along with the numerical analysis:  
* Asphalt mixtures made with silica fume fulfil all the acceptance requisites prescribed by Indian regulations in terms of volumetric properties, Marshall stability, Marshall quotient, indirect tensile strength, and water sensitivity. In particular, in order to satisfy all acceptance requirements, it is recommended to prepare mixtures with an SF content equal to or higher than 6% and a bitumen content equal to or higher than 5%. * The analysis carried out to investigate the aging effects demonstrated a good mechanical resistance of the SF asphalt mixes, with MMSR values up to approximately 70% achieved at 5.0% bitumen and 4.0% SF. The use of SF instead of OPC improved the mechanical behavior in terms of MMSR from 0.82 to 1.27% depending on bitumen and filler contents. * The comparison between mixes with SF and OPC outlines an overall physical-mechanical equivalence, demonstrating the technical feasibility to substitute cement with silica fume without any substantial worsening from a mechanical point of view. Differences in terms of VMA and VFB ranged from 0.18% to approximately 1.20%. In terms of the highest MS, the SF mixture achieved a 2.90% higher value than OPC mixtures, with a slightly higher long-term aging resistance. With respect to ITS, SF mixtures achieved higher values than OPC mixtures, with differences ranging from 0.06 to 1.64%. Finally, differences in terms of resistance to water damage ranged between 0.18 and 1.17% depending on bitumen percentage. * With respect to the recommended bitumen content (5%), the mix with 6% SF achieves an MS of 13.61 kN which is close to the MS of 13.63 kN obtained for the mix prepared with 8% OPC. Even if SF is characterized by a higher price (average price: 145 $/ton [67; 68]) than OPC (average price 105 $/ton [69]), the cost of the mixtures prepared with SF is lower (roughly $19 of SF for cubic meter of mix) than that of OPC mixtures (roughly $24 of OPC for cubic meter of mix). In fact, the cementitious filler-based mix requires a higher amount of cement which, moreover, is characterized by a higher specific gravity. * Artificial neural networks have allowed accurate modeling of the experimental data related to the main volumetric and mechanical parameters considered in the study as a function of bitumen, SF, and OPC contents. * For the future development of the modeling procedure, the adoption of an optimization technique for the selection of the ANN structure to reduce computational time is recommended, such as Bayesian statistics. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13116664/s1](https://www.mdpi.com/article/10.3390/app13116664/s1)]([https://www.mdpi.com/article/10.3390/app13116664/s1](https://www.mdpi.com/article/10.3390/app13116664/s1)), Table S1: Summary of experimental results; Table S2: Results of the grid search approach; Figure S1: Model prediction surface over the feature variability ranges for ITS, WS, and MQ parameters; Figure S2: Model prediction surface over the feature variability ranges for VMA and Vv parameters. **Author Contributions:** Conceptualization, N.T., F.R., N.S. and N.B. ; methodology, N.T., F.R. and N.B. ; software, F.R. ; validation, N.T., F.R. and N.B. ; formal analysis, N.T., F.R., N.S. and N.B. ; investigation, N.T. ; resources, N.S. and N.B. ; data curation, N.T., F.R., N.S. and N.B. ; writing--original draft preparation, N.T., F.R. and N.B. ; writing--review and editing, N.T., F.R., N.S. and N.B. ; visualization, N.T. and F.R. ; supervision, N.S. and N.B. ; project administration, N.S.