TY  - JOUR
AU  - Kostić, Srdan
AU  - Vasović, Nebojša
AU  - Marinković, Boban
PY  - 2017
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3690
AB  - A new approach is proposed for the robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation. The essence of the suggested procedure lies in the reliable prediction of concrete strength as a simple function of unit mass, water/cement ratio, age and superplasticizer content. The derived model provides sufficiently accurate results for the calibration and verification phases, the latter of which is conducted using data that were not used for model development. The results of additional analysis indicate that residuals in the calibration and verification stages have a normal distribution. Is its also shown that the uncertainty of estimated coefficient values has a statistically insignificant effect on concrete strength, confirming the reliability of the proposed model. Moreover, analysis of the effect of laboratory measurement errors indicates the robustness of concrete compressive strength against the variation in measured parameter values.
PB  - Taylor & Francis Ltd, Abingdon
T2  - Engineering Optimization
T1  - Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation
EP  - 877
IS  - 5
SP  - 864
VL  - 49
DO  - 10.1080/0305215X.2016.1211432
ER  - 

@article{
author = "Kostić, Srdan and Vasović, Nebojša and Marinković, Boban",
year = "2017",
abstract = "A new approach is proposed for the robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation. The essence of the suggested procedure lies in the reliable prediction of concrete strength as a simple function of unit mass, water/cement ratio, age and superplasticizer content. The derived model provides sufficiently accurate results for the calibration and verification phases, the latter of which is conducted using data that were not used for model development. The results of additional analysis indicate that residuals in the calibration and verification stages have a normal distribution. Is its also shown that the uncertainty of estimated coefficient values has a statistically insignificant effect on concrete strength, confirming the reliability of the proposed model. Moreover, analysis of the effect of laboratory measurement errors indicates the robustness of concrete compressive strength against the variation in measured parameter values.",
publisher = "Taylor & Francis Ltd, Abingdon",
journal = "Engineering Optimization",
title = "Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation",
pages = "877-864",
number = "5",
volume = "49",
doi = "10.1080/0305215X.2016.1211432"
}

Kostić, S., Vasović, N.,& Marinković, B.. (2017). Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation. in Engineering Optimization
Taylor & Francis Ltd, Abingdon., 49(5), 864-877.
https://doi.org/10.1080/0305215X.2016.1211432

Kostić S, Vasović N, Marinković B. Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation. in Engineering Optimization. 2017;49(5):864-877.
doi:10.1080/0305215X.2016.1211432 .

Kostić, Srdan, Vasović, Nebojša, Marinković, Boban, "Robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation" in Engineering Optimization, 49, no. 5 (2017):864-877,
https://doi.org/10.1080/0305215X.2016.1211432 . .