### Abstract

Properties of a random porous material such as pervious concrete are strongly dependent on its pore structure features. This study describes the development of different models to understand the material structure - property relationships in pervious concretes. Several pervious concrete mixtures with different pore structure features are proportioned. The pore structure features such as pore area fractions, pore sizes, mean free spacing of the pores, specific surface area, and the three-dimensional pore distribution density are extracted using image analysis methods. The performance features modeled as a function of the pore structure features are: (1) the unconfined compressive strength, (2) permeability, and (3) permeability reduction due to particle trapping in the pores (clogging). A statistical model is used to relate the compressive strength to the relevant pore structure features, which is then used as a base model in a Monte-Carlo simulation for feature sensitivity evaluation. Permeability prediction is accomplished using the well-known Katz-Thompson equation that employs the pore structure features. An idealized 3-D geometry obtained from 2-D planar images of pervious concrete sections is used along with a probablistic particle capture model to predict the particle retention associated with clogging material addition and simulated runoff. These models are anticipated to be useful in designing pervious concrete systems of desired pore structure for requisite performance.

Original language | English (US) |
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Title of host publication | American Concrete Institute, ACI Special Publication |

Pages | 55-73 |

Number of pages | 19 |

Edition | 282 SP |

State | Published - 2009 |

Event | The Leading Edge of Pervious Concrete 2009 at the ACI Fall 2009 Convention - New Orleans, LA, United States Duration: Nov 8 2009 → Nov 12 2009 |

### Other

Other | The Leading Edge of Pervious Concrete 2009 at the ACI Fall 2009 Convention |
---|---|

Country | United States |

City | New Orleans, LA |

Period | 11/8/09 → 11/12/09 |

### Fingerprint

### Keywords

- Clogging
- Compressive strength
- Particle capture model
- Permeability
- Pervious concrete
- Pore structure
- Porosity
- Statistical analysis

### ASJC Scopus subject areas

- Civil and Structural Engineering
- Building and Construction
- Materials Science(all)

### Cite this

*American Concrete Institute, ACI Special Publication*(282 SP ed., pp. 55-73)

**Models for property prediction of pervious concretes.** / Deo, Omkar; Sumanasooriya, Milani S.; Neithalath, Narayanan.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*American Concrete Institute, ACI Special Publication.*282 SP edn, pp. 55-73, The Leading Edge of Pervious Concrete 2009 at the ACI Fall 2009 Convention, New Orleans, LA, United States, 11/8/09.

}

TY - GEN

T1 - Models for property prediction of pervious concretes

AU - Deo, Omkar

AU - Sumanasooriya, Milani S.

AU - Neithalath, Narayanan

PY - 2009

Y1 - 2009

N2 - Properties of a random porous material such as pervious concrete are strongly dependent on its pore structure features. This study describes the development of different models to understand the material structure - property relationships in pervious concretes. Several pervious concrete mixtures with different pore structure features are proportioned. The pore structure features such as pore area fractions, pore sizes, mean free spacing of the pores, specific surface area, and the three-dimensional pore distribution density are extracted using image analysis methods. The performance features modeled as a function of the pore structure features are: (1) the unconfined compressive strength, (2) permeability, and (3) permeability reduction due to particle trapping in the pores (clogging). A statistical model is used to relate the compressive strength to the relevant pore structure features, which is then used as a base model in a Monte-Carlo simulation for feature sensitivity evaluation. Permeability prediction is accomplished using the well-known Katz-Thompson equation that employs the pore structure features. An idealized 3-D geometry obtained from 2-D planar images of pervious concrete sections is used along with a probablistic particle capture model to predict the particle retention associated with clogging material addition and simulated runoff. These models are anticipated to be useful in designing pervious concrete systems of desired pore structure for requisite performance.

AB - Properties of a random porous material such as pervious concrete are strongly dependent on its pore structure features. This study describes the development of different models to understand the material structure - property relationships in pervious concretes. Several pervious concrete mixtures with different pore structure features are proportioned. The pore structure features such as pore area fractions, pore sizes, mean free spacing of the pores, specific surface area, and the three-dimensional pore distribution density are extracted using image analysis methods. The performance features modeled as a function of the pore structure features are: (1) the unconfined compressive strength, (2) permeability, and (3) permeability reduction due to particle trapping in the pores (clogging). A statistical model is used to relate the compressive strength to the relevant pore structure features, which is then used as a base model in a Monte-Carlo simulation for feature sensitivity evaluation. Permeability prediction is accomplished using the well-known Katz-Thompson equation that employs the pore structure features. An idealized 3-D geometry obtained from 2-D planar images of pervious concrete sections is used along with a probablistic particle capture model to predict the particle retention associated with clogging material addition and simulated runoff. These models are anticipated to be useful in designing pervious concrete systems of desired pore structure for requisite performance.

KW - Clogging

KW - Compressive strength

KW - Particle capture model

KW - Permeability

KW - Pervious concrete

KW - Pore structure

KW - Porosity

KW - Statistical analysis

UR - http://www.scopus.com/inward/record.url?scp=84861745345&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861745345&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781618397997

SP - 55

EP - 73

BT - American Concrete Institute, ACI Special Publication

ER -