Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses

Author:

Kashem Abul,Karim Rezaul,Das Pobithra,Datta Shuvo Dip,Alharthai Mohammad

Funder

Najran University

Deanship of Scientific Research, King Saud University

Ministry of Education – Kingdom of Saudi Arabi

Publisher

Elsevier BV

Reference108 articles.

1. Climate and resource footprint assessment and visualization of recycled concrete for circular economy;Mostert;Resour. Conserv. Recycl.,2021

2. Experimental study on damage evaluation, pore structure and impact tensile behavior of 10-year-old concrete cores after exposure to high temperatures;Chen;Int. J. Concr. Struct. Mater.,2020

3. Compressive strength prediction of rice husk ash concrete using a hybrid artificial neural network model;Li;Materials,2023

4. P. Friedlingstein, M.W. Jones, M. O’Sullivan, R.M. Andrew, D.C.E. Bakker, Global Carbon Budget 2021, raport Earth System Science Data, Earth Syst Sci Data (2022).

5. Comparison of machine learning approaches with traditional methods for predicting the compressive strength of rice husk ash concrete;Amin;Crystals (Basel),2021

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