Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Civil Engineering

Committee Chair/Advisor

Putman, Bradley J

Committee Member

Rangaraju, Prasada R

Committee Member

Poursaee, Amir

Committee Member

Sawyer, Calvin


Porous pavements are sustainable features that are used to help manage the quantity and quality of stormwater runoff. These pavements may include porous asphalt, permeable interlocking concrete pavers and pervious concrete. Since pavements that are purposefully designed to drain water through their matrix are relatively new, contractors and engineers are faced with various challenges such as improper design and installation, poor workability, and excessive finishing which may lead to clogged pores. Therefore, this study on porous pavements examined pervious concrete mixtures to evaluate an optimization process for the preparation of porous pavement mixtures based on aggregate structure to meet desired performance criteria. Pervious concrete mixtures typically consist of aggregate, cement, water, little to no fines and admixtures. Since aggregate makes up a large portion of the pervious concrete mix, aggregate properties and proportioning were the main focus of this study. Two aggregate sources (L and C) were used in the preparation of pervious concrete mixtures. From these sources, three single-sized aggregate fractions were used in making blends, the #8 (2.36 mm), the #4 (4.75 mm) and the in. (9.5 mm). Aggregate properties such as uniformity coefficient were calculated and others were measured including specific gravity, absorption, density (dry rodded and dry Proctor), void content, percent flat and elongated, shape and surface texture (particle index), California Bearing Ratio penetration stress, and compaction indices. From source L, fifteen (15) sample groups of twelve (12) 6 in. × 6 in. cylindrical specimens were made and from source C, fourteen (14) sample groups were made similar to source L. The fresh pervious concrete had a water-cement ratio of 0.25, with a cement-aggregate ratio of 0.23 for source L and 0.25 for source C, and the unit weights (ASTM C1688 and an alternative method) and gravimetric air content were determined. Each sample group was divided into 4 subgroups of three specimens that had permeability values that were not statistically different from each other. Other tests conducted on the different subgroups included effective porosity, compressive strength, split tensile strength, and abrasion loss. The aggregate test results showed that source L, had higher specific gravities, percent absorption, and densities than source C, but lower void contents, percent flat and elongated, particle index, and California Bearing Ratio penetration stress at 0.2 inches. The approach taken in evaluating an optimization process was to use regression analysis in combination with the simplex-centroid design of the three aggregate sizes. Relationships were analyzed within and across aggregate properties and pervious concrete properties. The augmented simplex-centroid design with the polynomial special quartic model was used to predict the aggregate proportions that best fit the desired aggregate property or pervious concrete property. This design of experiment tool is a triangle with an elevated response surface on which contour lines present the predicted parameter values. For this study, the simplex triangle consisted of ten design points representing the aggregate proportions associated with the predicted parameters. The design points were located at the vertices, at the halfway point along the edges, and at the centroid, and three additional points within the triangle around the centroid on imaginary lines that run perpendicularly from the midpoint of an axis to the opposite vertex. The lack-of-fit test with α = 0.01 was used to check the adequacy of the model based on all the data points and also on only the validation points. Based on the lack-of-tests, the special quartic model was over 50% adequate for source L mixtures and over 80% adequate for source C. The optimization process included two options: Option 1 - A regression analysis is done to predict an aggregate property that relates well to a pervious concrete property. The contour line on the simplex response surface that represents the predicted aggregate property is then used to predict aggregate proportions that meet the desired aggregate property. Option 2 - The contour line for the desired pervious concrete property could be located on the simplex response surface and used to predict the aggregate proportions that meet the desired pervious concrete property.



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