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In this study, two issues related to the pervious concrete are addressed, and a proper solution is drawn. The first issue is related to strength and the second one is related to permeability. We have used some of the available industrial waste like fly ash, silica fume, and furnace slag in replacement of cement at 20%, 35%, and 50%. The study is conducted to check whether this waste materials replacement can increase the strength and permeability of pervious concrete. Results revealed that fly ash replacement at 35% gave the best result than no cement replacement in both strength and permeability

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Due to the associated economic, environmental, and performance advantages, using natural pozzolans as a substitute for cement is regarded as a standard guidelines in the construction sector. In order to achieve, it is beneficial to use GGBS in the production of concrete, which was identified as the most effective supplementary cementitious material in resisting environmental threats. According to the literature review, GGBS concrete is more impermeable than PPC concrete (concrete with fly ash) or pure OPC concrete. However, it has the potential to increase the strength of concrete, which is a significant disadvantage of pervious concrete. Hence this study investigates the variation of strength and permeability of pervious concrete in order to obtain the optimal proportion of GGBS inclusion in concrete production. Along with that, the research uses artificial neural networks to estimate the compressive strength, water permeability, and porosity of pervious concrete blended with several proportions of GGBS. The study results showed that the inclusion of larger aggregate sizes significantly increased the strength but the effect of adding GGBS in partial replacement of cement could not. As a result, GGBS might replace some of the cement in concrete without significantly altering its mechanical qualities. Further, the developed ANN models outperformed the conventional MLR model and it can act as a useful alternative to analytical models for predicting strength and permeability values in pervious concrete.