EXPERIMENTAL INVESTIGATION AND INTELLIGENT PREDICTION OF MECHANICAL STRENGTH OF PERVIOUS CONCRETE

This study determines the mechanical strength characteristics of pervious concrete using fuzzy logic. During construction, the process of determining the quality of concrete produced takes about 28 days before testing, training the fuzzy logic model through the research data gotten from the laboratory experiments in order to intelligently predict the mechanical strength of pervious concrete. Crushed granite was used in this experiment to ensure the pervious concrete has a little degree of fine aggregates and the maximum size of coarse aggregate used was 20 mm. Compressive, flexural and split tensile strength tests were carried out on pervious concrete of mixing ratios 1:4, 1:5 and 1:6 with each of the mixing ratios having varying water to cement ratios of 0.4, 0.5, 0.6 and 0.7. The developed fuzzy logic model was applied to predict the mechanical strengths of the concrete by using data obtained from experimental results, with input parameters of water content, crushing value, weight and density and the mechanical strengths as the output parameters employing the triangular membership function. The model was calibrated using the error measures of the root mean square error (RMSE), mean absolute percentage error (MAPE) and R squared were computed for the model. In this study, it was observed that higher water to cement ratio slightly reduces the mechanical strength of PC but increases the workability compared to increase in cement to granite ratio which notably increases the strength and reduced the workability, alternatively, higher water to cement ratio causes a notable decrease in permeability of the pervious concrete. The correlation between the experimental and predicted values was excellent. This shows that fuzzy logic can be used to predict the mechanical properties of pervious concrete, and fuzzy logic can also be used to appropriately deduce missing values in the mechanical properties of PC subject to further research.