Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches-Reference-Cited by-同舟云学术

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Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

Published:2022-10 Issue: Volume:133 Page:104721
ISSN:0958-9465
Container-title:Cement and Concrete Composites
language:en
Short-container-title:Cement and Concrete Composites

Author:

Salami Babatunde Abiodun,Iqbal Mudassir,Abdulraheem Abdulazeez,Jalal Fazal E.,Alimi Wasiu,Jamal Arshad,Tafsirojjaman T.,Liu Yue,Bardhan Abidhan

Funder

Beijing University of Technology

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction

Reference125 articles.

1. Assessment of longstanding effects of fly ash and silica fume on the compressive strength of concrete using extreme learning machine and artificial neural network;Shariati;J. Adv. Eng. Computation,2021

2. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement;Shariati;Eng. Comput.,2022

3. Design efficiency, characteristics, and utilization of reinforced foamed concrete: a review;Amran;Crystals,2020

4. Properties and applications of foamed concrete; a review;Amran;Construct. Build. Mater.,2015

5. Experimental investigation on the compressive strength of foamed concrete: effect of curing conditions, cement type, foaming agent and dry density;Falliano;Construct. Build. Mater.,2018

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