TY  - JOUR
AU  - Šarkić Glumac, Anina
AU  - Jadhav, Onkar
AU  - Despotović, Vladimir
AU  - Blocken, Bert
AU  - Bordas, Stephane PA
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3292
AB  - Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency. The study shows that the accurate prediction of distributions of mean and rms pressure over a high-rise building for the entire wind rose can be obtained by utilizing only 3 LES-related wind directions. The artificial neural network is shown to perform best among considered machine learning models. Moreover, hyperparameter optimization significantly improves the model predictions, increasing the 𝑅��2 value in the case of rms pressure by 60%. Dominant and ineffective features are determined that provide a route to solve a similar application more effectively.
PB  - Elsevier
T2  - Building and Environment
T1  - A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case
VL  - 234
DO  - 10.1016/j.buildenv.2023.110135
ER  - 

@article{
author = "Šarkić Glumac, Anina and Jadhav, Onkar and Despotović, Vladimir and Blocken, Bert and Bordas, Stephane PA",
year = "2023",
abstract = "Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency. The study shows that the accurate prediction of distributions of mean and rms pressure over a high-rise building for the entire wind rose can be obtained by utilizing only 3 LES-related wind directions. The artificial neural network is shown to perform best among considered machine learning models. Moreover, hyperparameter optimization significantly improves the model predictions, increasing the 𝑅��2 value in the case of rms pressure by 60%. Dominant and ineffective features are determined that provide a route to solve a similar application more effectively.",
publisher = "Elsevier",
journal = "Building and Environment",
title = "A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case",
volume = "234",
doi = "10.1016/j.buildenv.2023.110135"
}

Šarkić Glumac, A., Jadhav, O., Despotović, V., Blocken, B.,& Bordas, S. P.. (2023). A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case. in Building and Environment
Elsevier., 234.
https://doi.org/10.1016/j.buildenv.2023.110135

Šarkić Glumac A, Jadhav O, Despotović V, Blocken B, Bordas SP. A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case. in Building and Environment. 2023;234.
doi:10.1016/j.buildenv.2023.110135 .

Šarkić Glumac, Anina, Jadhav, Onkar, Despotović, Vladimir, Blocken, Bert, Bordas, Stephane PA, "A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case" in Building and Environment, 234 (2023),
https://doi.org/10.1016/j.buildenv.2023.110135 . .

TY  - JOUR
AU  - Kovačević, Miljan
AU  - Ivanišević, Nenad
AU  - Petronijević, Predrag
AU  - Despotović, Vladimir
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2336
AB  - Seven state-of-the-art machine learning techniques for estimation of construction
costs of reinforced-concrete and prestressed concrete bridges are investigated in this
paper, including artificial neural networks (ANN) and ensembles of ANNs, regression
tree ensembles (random forests, boosted and bagged regression trees), support
vector regression (SVR) method, and Gaussian process regression (GPR). A database
of construction costs and design characteristics for 181 reinforced-concrete and
prestressed-concrete bridges is created for model training and evaluation
PB  - Građevinar
T2  - Časopis Građevinar
T1  - Construction cost estimation of reinforced and prestressed concrete bridges using machine learning
VL  - 73
DO  - 10.14256/JCE.2738.2019
ER  - 

@article{
author = "Kovačević, Miljan and Ivanišević, Nenad and Petronijević, Predrag and Despotović, Vladimir",
year = "2021",
abstract = "Seven state-of-the-art machine learning techniques for estimation of construction
costs of reinforced-concrete and prestressed concrete bridges are investigated in this
paper, including artificial neural networks (ANN) and ensembles of ANNs, regression
tree ensembles (random forests, boosted and bagged regression trees), support
vector regression (SVR) method, and Gaussian process regression (GPR). A database
of construction costs and design characteristics for 181 reinforced-concrete and
prestressed-concrete bridges is created for model training and evaluation",
publisher = "Građevinar",
journal = "Časopis Građevinar",
title = "Construction cost estimation of reinforced and prestressed concrete bridges using machine learning",
volume = "73",
doi = "10.14256/JCE.2738.2019"
}

Kovačević, M., Ivanišević, N., Petronijević, P.,& Despotović, V.. (2021). Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. in Časopis Građevinar
Građevinar., 73.
https://doi.org/10.14256/JCE.2738.2019

Kovačević M, Ivanišević N, Petronijević P, Despotović V. Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. in Časopis Građevinar. 2021;73.
doi:10.14256/JCE.2738.2019 .

Kovačević, Miljan, Ivanišević, Nenad, Petronijević, Predrag, Despotović, Vladimir, "Construction cost estimation of reinforced and prestressed concrete bridges using machine learning" in Časopis Građevinar, 73 (2021),
https://doi.org/10.14256/JCE.2738.2019 . .