Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA
%0 Journal Article
%1 ArastehKhoshbin2024
%A Arasteh-Khoshbin, O.
%A Seyedpour, Seyed Morteza
%A Mandl, Luis
%A Lambers, Lena
%A Ricken, Tim
%D 2024
%I Taylor & Francis
%J European Journal of Environmental and Civil Engineering
%K EXC2075 PN2 PN2-2 isd myown rg-expmech-enveng rg-ml simliva
%P 1–20
%R 10.1080/19648189.2024.2393881
%T Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA
@article{ArastehKhoshbin2024,
added-at = {2024-10-02T17:42:07.000+0200},
author = {Arasteh-Khoshbin, O. and Seyedpour, Seyed Morteza and Mandl, Luis and Lambers, Lena and Ricken, Tim},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23ebccdce9fddc93484ee48096a113de2/lmandl},
doi = {10.1080/19648189.2024.2393881},
interhash = {d29fca572c3bd0719c2f956c5c086e66},
intrahash = {3ebccdce9fddc93484ee48096a113de2},
issn = {2116-7214},
journal = {European Journal of Environmental and Civil Engineering},
keywords = {EXC2075 PN2 PN2-2 isd myown rg-expmech-enveng rg-ml simliva},
month = sep,
pages = {1–20},
publisher = {Taylor & Francis},
timestamp = {2024-10-02T17:42:07.000+0200},
title = {Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA},
year = 2024
}
