Soft computing-based models for the prediction of masonry compressive strength
Panagiotis G. Asteris(School of Pedagogical and Technological Education), Humberto Varum(National Technical University of Athens), Athanasia D. Skentou(School of Pedagogical and Technological Education), Mohsen Hajihassani(Urmia University), Minas E. Lemonis(School of Pedagogical and Technological Education), Paulo B. Lourénço(Meisei University), Hoang Nguyen(Duy Tan University), Hugo Rodrigues(University of Aveiro), Chrissy-Elpida Adami(School of Pedagogical and Technological Education), Rui Marques(University of Minho)
Cited by 128
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