Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering |
| Publisher | Civil-Comp Press |
| Volume | 87 |
| ISBN (print) | 9781905088201 |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 9th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2007 - St. Julians, Malta Duration: 18 Sept 2007 → 21 Sept 2007 |
Abstract
In this paper, a concept for the lifetime prediction of novel materials in civil engineering is presented. The approach does not need experiments over the structural lifetime and solves the problems of limited data and limited mechanical and physical insight in initial stages of investigation. Specifically, a combination of accelerated life testing with a neural network solution for function approximation over the time coordinate is proposed. The capabilities of the concept are demonstrated by way of a numerical example for validation purposes and a practical application for predicting the lifetime of textile-reinforced concrete specimens under service load.
Keywords
- Accelerated life testing, Acceleration function, Lifetime prognosis, Model-free prediction, Neural network, Textile reinforcement
ASJC Scopus subject areas
- Environmental Science(all)
- Environmental Engineering
- Engineering(all)
- Civil and Structural Engineering
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Artificial Intelligence
Cite this
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Proceedings of the 9th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering. Vol. 87 Civil-Comp Press, 2007.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Lifetime prediction with neural networks
AU - Freitag, S.
AU - Beer, M.
AU - Graf, W.
AU - Kaliske, M.
PY - 2007
Y1 - 2007
N2 - In this paper, a concept for the lifetime prediction of novel materials in civil engineering is presented. The approach does not need experiments over the structural lifetime and solves the problems of limited data and limited mechanical and physical insight in initial stages of investigation. Specifically, a combination of accelerated life testing with a neural network solution for function approximation over the time coordinate is proposed. The capabilities of the concept are demonstrated by way of a numerical example for validation purposes and a practical application for predicting the lifetime of textile-reinforced concrete specimens under service load.
AB - In this paper, a concept for the lifetime prediction of novel materials in civil engineering is presented. The approach does not need experiments over the structural lifetime and solves the problems of limited data and limited mechanical and physical insight in initial stages of investigation. Specifically, a combination of accelerated life testing with a neural network solution for function approximation over the time coordinate is proposed. The capabilities of the concept are demonstrated by way of a numerical example for validation purposes and a practical application for predicting the lifetime of textile-reinforced concrete specimens under service load.
KW - Accelerated life testing
KW - Acceleration function
KW - Lifetime prognosis
KW - Model-free prediction
KW - Neural network
KW - Textile reinforcement
UR - http://www.scopus.com/inward/record.url?scp=84893917855&partnerID=8YFLogxK
U2 - 10.4203/ccp.87.35
DO - 10.4203/ccp.87.35
M3 - Conference contribution
SN - 9781905088201
VL - 87
BT - Proceedings of the 9th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering
PB - Civil-Comp Press
T2 - 9th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, AICivil-Comp 2007
Y2 - 18 September 2007 through 21 September 2007
ER -