Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures
Wael Emad(Soran University), Parveen Sihag(Chandigarh University), Rawaz Kurda(Duhok Polytechnic University), A.M.T. Hassan(American University of Iraq Sulaimani), Ana Brás(Liverpool John Moores University), Zhyan Muhammed(University of Sulaimani), Panagiotis G. Asteris(School of Pedagogical and Technological Education), Ahmed Salih Mohammed(University of Sulaimani), Shaker Qaidi(University of Duhok)
Cited by 116
Related Papers
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
|Cement and Concrete Research|2021|668
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
|Neural Computing and Applications|2020|456
Concrete compressive strength using artificial neural networks
|Neural Computing and Applications|2019|432
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
|Engineering With Computers|2020|349
Mathematical Macromodeling of Infilled Frames: State of the Art
|Journal of Structural Engineering|2011|329