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    Methodology to Develop Pervious Concrete Mixtures for Target Properties Emphasizing the Selection of Mixture Variables

    Source: Journal of Transportation Engineering, Part B: Pavements:;2018:;Volume ( 144 ):;issue: 003
    Author:
    Chandrappa Anush K.;Biligiri Krishna Prapoorna
    DOI: 10.1061/JPEODX.0000061
    Publisher: American Society of Civil Engineers
    Abstract: Pervious concrete (PC) has gained a lot of attention over the last few years due to its environmental benefits in managing stormwater runoff and reducing urban heat island effects. The pervious concrete mix design is the most discussed aspect owing to its importance in achieving the desired properties. However, since there are no proper guidelines/methods to compact pervious concrete specimens in the laboratory, no particular methodology is available to prepare pervious concrete for its desired strength and permeability. Considering this as a research gap, 18 pervious concrete mixtures were prepared and tested for various properties including hardened density, porosity, compressive strength, and permeability. Based on the laboratory data, a methodology was developed that could guide the user to obtain targeted properties of pervious concrete through a series of regression equations. The regression equations developed had excellent goodness of fit, which indicated high accuracy and low bias. The validation process depicted that equations can perform excellently in predicting the pervious concrete properties. The models were checked against previous studies, which depicted a reasonable accuracy in predicting PC properties from other studies as well. It is envisioned that the methodology could be further improvised considering various other important factors to advance it at the global level.
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      Methodology to Develop Pervious Concrete Mixtures for Target Properties Emphasizing the Selection of Mixture Variables

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    contributor authorChandrappa Anush K.;Biligiri Krishna Prapoorna
    date accessioned2019-02-26T07:37:12Z
    date available2019-02-26T07:37:12Z
    date issued2018
    identifier otherJPEODX.0000061.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248310
    description abstractPervious concrete (PC) has gained a lot of attention over the last few years due to its environmental benefits in managing stormwater runoff and reducing urban heat island effects. The pervious concrete mix design is the most discussed aspect owing to its importance in achieving the desired properties. However, since there are no proper guidelines/methods to compact pervious concrete specimens in the laboratory, no particular methodology is available to prepare pervious concrete for its desired strength and permeability. Considering this as a research gap, 18 pervious concrete mixtures were prepared and tested for various properties including hardened density, porosity, compressive strength, and permeability. Based on the laboratory data, a methodology was developed that could guide the user to obtain targeted properties of pervious concrete through a series of regression equations. The regression equations developed had excellent goodness of fit, which indicated high accuracy and low bias. The validation process depicted that equations can perform excellently in predicting the pervious concrete properties. The models were checked against previous studies, which depicted a reasonable accuracy in predicting PC properties from other studies as well. It is envisioned that the methodology could be further improvised considering various other important factors to advance it at the global level.
    publisherAmerican Society of Civil Engineers
    titleMethodology to Develop Pervious Concrete Mixtures for Target Properties Emphasizing the Selection of Mixture Variables
    typeJournal Paper
    journal volume144
    journal issue3
    journal titleJournal of Transportation Engineering, Part B: Pavements
    identifier doi10.1061/JPEODX.0000061
    page4018031
    treeJournal of Transportation Engineering, Part B: Pavements:;2018:;Volume ( 144 ):;issue: 003
    contenttypeFulltext
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