### Abstract

The porosity of cement paste affects its mechanical and thermal properties. Even when two specimens have the same degree of porosity each other, the void distribution considerably affects the behavior of material. To evaluate the material properties of cement pastes statistically, a sensitivity analysis using a First-Order Second-Moment (FOSM) method can be used. This is a probabilistic method to determine the probability distribution of output variables with random input variables. The porosity(φ) and continuity of void (L _{p} area, Ω) are selected as input variables, and the thermal conductivity and stiffness of cement paste are selected as output variables. When a virtual specimen is generated from micro-level computerized tomographic (μ-CT) images of a real cement paste specimen, the specimens that have objective microstructures can be obtained using a reconstruction process. In this study, statistical distributions of input variables are from 64 virtual specimens and output variables are estimated from reconstructed specimens using finite element analysis. Based on sensitivity analysis, sensitivity measures of material properties on both characterizations are evaluated. From this results, the probability distributions of the responses can be estimated and the relation between input and output variables can be evaluated.

Original language | English |
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Title of host publication | Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018 |

Editors | Bernhard Pichler, Jan G. Rots, Günther Meschke |

Publisher | CRC Press/Balkema |

Pages | 141-148 |

Number of pages | 8 |

ISBN (Print) | 9781138741171 |

DOIs | |

Publication status | Published - 2018 Jan 1 |

Event | Conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018 - Bad Hofgastein, Austria Duration: 2018 Feb 26 → 2018 Mar 1 |

### Publication series

Name | Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete&amp;amp;amp;amp;amp;amp;amp;nbsp;and Concrete Structures, EURO-C 2018 |
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### Conference

Conference | Conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018 |
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Country | Austria |

City | Bad Hofgastein |

Period | 18/2/26 → 18/3/1 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Modelling and Simulation
- Civil and Structural Engineering

### Cite this

*Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018*(pp. 141-148). (Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete&amp;amp;amp;amp;amp;amp;amp;nbsp;and Concrete Structures, EURO-C 2018). CRC Press/Balkema. https://doi.org/10.1201/9781315182964-17

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*Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018.*Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete&amp;amp;amp;amp;amp;amp;amp;nbsp;and Concrete Structures, EURO-C 2018, CRC Press/Balkema, pp. 141-148, Conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018, Bad Hofgastein, Austria, 18/2/26. https://doi.org/10.1201/9781315182964-17

**Sensitivity estimation of cement paste properties in the microstructural characteristics.** / Kim, J. S.; Han, T. S.

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

TY - GEN

T1 - Sensitivity estimation of cement paste properties in the microstructural characteristics

AU - Kim, J. S.

AU - Han, T. S.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The porosity of cement paste affects its mechanical and thermal properties. Even when two specimens have the same degree of porosity each other, the void distribution considerably affects the behavior of material. To evaluate the material properties of cement pastes statistically, a sensitivity analysis using a First-Order Second-Moment (FOSM) method can be used. This is a probabilistic method to determine the probability distribution of output variables with random input variables. The porosity(φ) and continuity of void (L p area, Ω) are selected as input variables, and the thermal conductivity and stiffness of cement paste are selected as output variables. When a virtual specimen is generated from micro-level computerized tomographic (μ-CT) images of a real cement paste specimen, the specimens that have objective microstructures can be obtained using a reconstruction process. In this study, statistical distributions of input variables are from 64 virtual specimens and output variables are estimated from reconstructed specimens using finite element analysis. Based on sensitivity analysis, sensitivity measures of material properties on both characterizations are evaluated. From this results, the probability distributions of the responses can be estimated and the relation between input and output variables can be evaluated.

AB - The porosity of cement paste affects its mechanical and thermal properties. Even when two specimens have the same degree of porosity each other, the void distribution considerably affects the behavior of material. To evaluate the material properties of cement pastes statistically, a sensitivity analysis using a First-Order Second-Moment (FOSM) method can be used. This is a probabilistic method to determine the probability distribution of output variables with random input variables. The porosity(φ) and continuity of void (L p area, Ω) are selected as input variables, and the thermal conductivity and stiffness of cement paste are selected as output variables. When a virtual specimen is generated from micro-level computerized tomographic (μ-CT) images of a real cement paste specimen, the specimens that have objective microstructures can be obtained using a reconstruction process. In this study, statistical distributions of input variables are from 64 virtual specimens and output variables are estimated from reconstructed specimens using finite element analysis. Based on sensitivity analysis, sensitivity measures of material properties on both characterizations are evaluated. From this results, the probability distributions of the responses can be estimated and the relation between input and output variables can be evaluated.

UR - http://www.scopus.com/inward/record.url?scp=85061300947&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061300947&partnerID=8YFLogxK

U2 - 10.1201/9781315182964-17

DO - 10.1201/9781315182964-17

M3 - Conference contribution

AN - SCOPUS:85061300947

SN - 9781138741171

T3 - Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete&amp;amp;amp;amp;amp;amp;amp;amp;nbsp;and Concrete Structures, EURO-C 2018

SP - 141

EP - 148

BT - Computational Modelling of Concrete Structures - Proceedings of the conference on Computational Modelling of Concrete and Concrete Structures, EURO-C 2018

A2 - Pichler, Bernhard

A2 - Rots, Jan G.

A2 - Meschke, Günther

PB - CRC Press/Balkema

ER -