Modeling of Strength Properties of Porous Concrete
-
Published:2024-04
Issue:
Volume:
Page:
-
ISSN:2214-7853
-
Container-title:Materials Today: Proceedings
-
language:en
-
Short-container-title:Materials Today: Proceedings
Author:
Nasier SandeepORCID,
Lallotra Balwinder,
Hooda Nishtha
Reference32 articles.
1. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates;Hammoudi;Constr. Build. Mater.,2019
2. Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm;Kandiri;Constr. Build. Mater.,2020
3. A. Mohammed , S.Rafiq, P.Sihag, R. Kurda, W.Mahmood, K.Ghafor, W. Sarwar ; ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash, J. Mater. Res. Technol. 9(6) (2020) 12416–1242.
4. A. Toghroli, P. Mehrabi., M. Shariati., N.T. Trung., S. Jahandari., H. Rasekh, Evaluating the use of recycled concrete aggregate and pozzolanic additives in fiber-reinforced pervious concrete with industrial and recycled fibers, Constr. Build. Mater. 252 (2020) 11899.
5. Investigating porous concrete with improved strength: testing at different scales;Azar-Ozbek;Constr. Build. Mater.,2013