Details
| Original language | English |
|---|---|
| Number of pages | 8 |
| Publication status | Published - 2019 |
| Event | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 - Seoul, South Korea, Seoul, Korea, Republic of Duration: 26 May 2019 → 30 May 2019 Conference number: 13 |
Conference
| Conference | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 |
|---|---|
| Abbreviated title | ICASP13 |
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 26 May 2019 → 30 May 2019 |
Abstract
The constitutive model of concrete is of paramount significance for the design of concrete structures and the corresponding reliability assessment. In the present paper, the uniaxial damage model of concrete based on Chinese design code is introduced. It is noticed that there are seven crucial parameters in this model, while five of them are of physical significance and generally should be regarded as random variables. Therefore, the major task of the present paper is to study the effects, variations and randomness of these five parameters. Starting with the fuzzy analysis method (FAM), a brief uncertainty quantification scheme is described. This method is straightforward and easy to implement. Nevertheless, the prior knowledge (i.e., the engineering experience of designers or published literature) is required in FAM. Alternatively, the probability density evolution method (PDEM) is utilized with less needs of prior knowledge, while the type of marginal distribution of parameters is still required or assumed. Thus the epistemic uncertainty may be, more or less, brought in when applying these two methods. To improve this situation, i.e., to reduce the involvement of prior knowledge, a probabilistic learning method (PLM) is applied, in which the prior knowledge is reduced as it is nearly of data-driven background. The research results indicate that these three different methods of uncertainty quantification provide some basic and common conclusions, showing that all of them can capture the main characters of the experimental data. In addition, they individually offer various aspects of information due to different perspectives of these three methods. Therefore, these three methods might derive a series of powerful tools for uncertainty quantification in structural engineering, and be of future interest for opening new perspectives.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Mathematics(all)
- Statistics and Probability
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2019. Paper presented at 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019, Seoul, Korea, Republic of.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Pathways for uncertainty quantification through stochastic damage constitutive models of concrete
AU - Wan, Zhiqiang
AU - Chen, Jianbing
AU - Beer, Michael
N1 - Conference code: 13
PY - 2019
Y1 - 2019
N2 - The constitutive model of concrete is of paramount significance for the design of concrete structures and the corresponding reliability assessment. In the present paper, the uniaxial damage model of concrete based on Chinese design code is introduced. It is noticed that there are seven crucial parameters in this model, while five of them are of physical significance and generally should be regarded as random variables. Therefore, the major task of the present paper is to study the effects, variations and randomness of these five parameters. Starting with the fuzzy analysis method (FAM), a brief uncertainty quantification scheme is described. This method is straightforward and easy to implement. Nevertheless, the prior knowledge (i.e., the engineering experience of designers or published literature) is required in FAM. Alternatively, the probability density evolution method (PDEM) is utilized with less needs of prior knowledge, while the type of marginal distribution of parameters is still required or assumed. Thus the epistemic uncertainty may be, more or less, brought in when applying these two methods. To improve this situation, i.e., to reduce the involvement of prior knowledge, a probabilistic learning method (PLM) is applied, in which the prior knowledge is reduced as it is nearly of data-driven background. The research results indicate that these three different methods of uncertainty quantification provide some basic and common conclusions, showing that all of them can capture the main characters of the experimental data. In addition, they individually offer various aspects of information due to different perspectives of these three methods. Therefore, these three methods might derive a series of powerful tools for uncertainty quantification in structural engineering, and be of future interest for opening new perspectives.
AB - The constitutive model of concrete is of paramount significance for the design of concrete structures and the corresponding reliability assessment. In the present paper, the uniaxial damage model of concrete based on Chinese design code is introduced. It is noticed that there are seven crucial parameters in this model, while five of them are of physical significance and generally should be regarded as random variables. Therefore, the major task of the present paper is to study the effects, variations and randomness of these five parameters. Starting with the fuzzy analysis method (FAM), a brief uncertainty quantification scheme is described. This method is straightforward and easy to implement. Nevertheless, the prior knowledge (i.e., the engineering experience of designers or published literature) is required in FAM. Alternatively, the probability density evolution method (PDEM) is utilized with less needs of prior knowledge, while the type of marginal distribution of parameters is still required or assumed. Thus the epistemic uncertainty may be, more or less, brought in when applying these two methods. To improve this situation, i.e., to reduce the involvement of prior knowledge, a probabilistic learning method (PLM) is applied, in which the prior knowledge is reduced as it is nearly of data-driven background. The research results indicate that these three different methods of uncertainty quantification provide some basic and common conclusions, showing that all of them can capture the main characters of the experimental data. In addition, they individually offer various aspects of information due to different perspectives of these three methods. Therefore, these three methods might derive a series of powerful tools for uncertainty quantification in structural engineering, and be of future interest for opening new perspectives.
UR - http://www.scopus.com/inward/record.url?scp=85083952236&partnerID=8YFLogxK
U2 - 10.22725/ICASP13.249
DO - 10.22725/ICASP13.249
M3 - Paper
T2 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019
Y2 - 26 May 2019 through 30 May 2019
ER -