Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder-Reference-Cited by-同舟云学术

Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Published:2023-12 Issue: Volume:19 Page:e02557
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Mahmood Muhammad Sarmad^ORCID,Elahi Ayub,Zaid Osama,Alashker Yasser,Șerbănoiu Adrian A.,Grădinaru Cătălina M.,Ullah Kiffayat,Ali Tariq

Funder

Deanship of Scientific Research, King Khalid University

Publisher

Elsevier BV

Subject

Materials Science (miscellaneous)

Reference76 articles.

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2. Mechanical performance, water and chloride permeability of hybrid steel-polypropylene fiber-reinforced recycled aggregate concrete;El Ouni;Case Stud. Constr. Mater.,2022

3. Mechanical, environmental and economic performance of structural concrete containing silica fume and marble industry waste powder;Khodabakhshian;Constr. Build. Mater.,2018

4. Partial replacement of cement by ground granulated blast furnace slag and silica fume in two-stage concrete (preplaced aggregate concrete);Das;Struct. Concr.,2021

5. Evaluation of pozzolanic activity of ternary blended supplementary cementitious material with rice husk ash and GGBS;Shahas;Mater. Today Proc.,2023

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