Optimization of Quaternary Blended Cement for Eco-Sustainable Concrete Mixes Using Response Surface Methodology-Reference-Cited by-同舟云学术

Optimization of Quaternary Blended Cement for Eco-Sustainable Concrete Mixes Using Response Surface Methodology

Published:2023-07-06 Issue:10 Volume:48 Page:14079-14094
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Harith Iman Kattoof

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-023-08071-6.pdf

Reference55 articles.

1. Papatzani, S.; Paine, K.: Optimisation of low-carbon footprint quaternary and quinary (37% fly ash) cementitious nanocomposites with polycarboxylate or aqueous nanosilica particles. Adv. Mater. Sci. Eng. 2019, 5931306 (2019)

2. Müller, H.S.; Breiner, R.; Moffatt, J.S.; Haist, M.: Design and properties of sustainable concrete. Procedia Eng. 95, 290–304 (2014). https://doi.org/10.1016/j.proeng.2014.12.189

3. Awolusi, T.; Taiwo, A.; Aladegboye, O.; Oguntayo, D.: Materials Today: proceedings optimisation of quinary blended supplementary cementitious material for eco-friendly paving unit using taguchi orthogonal array design. Mater. Today Proc. 65, 2221–2227 (2022). https://doi.org/10.1016/j.matpr.2022.06.263

4. Du, S.; Zhao, Q.; Shi, X.: High-volume fly ash-based cementitious composites as sustainable materials : an overview of recent advances. Adv. Civil Eng. 2021, 1–22 (2021)

5. Fernández, Á.; Calvo, J.L.G.; Alonso, M.C.: Ordinary Portland cement composition for the optimization of the synergies of supplementary cementitious materials of ternary binders in hydration processes. Cem. Concr. Compos. 89, 238–250 (2018). https://doi.org/10.1016/j.cemconcomp.2017.12.016

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimisation and prediction modeling of hardened concrete characteristics incorporating coal bottom Ash (CBA) via the response surface methodology (RSM);Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-08-24

2. Prediction of high-performance concrete strength using machine learning with hierarchical regression;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-05-16

3. Comparison of artificial neural network and hierarchical regression in prediction compressive strength of self-compacting concrete with fly ash;Innovative Infrastructure Solutions;2024-02-10

4. Investigation of some cellulose derivatives effects on concrete properties using response surface methodology;Construction and Building Materials;2024-02

5. Total Words: 6,835;2024