[HTML][HTML] Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review

M Rathnayaka, D Karunasinghe, C Gunasekara… - … and Building Materials, 2024 - Elsevier
Geopolymer concrete is a sustainable replacement to the Ordinary Portland Cement (OPC)
concrete as it mitigates some of the associated problems of OPC manufacturing such as …

Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning

SFA Shah, B Chen, M Zahid, MR Ahmad - Construction and Building …, 2022 - Elsevier
In recent years, alkali activated material (AAM) or geopolymer has emerged as a sustainable
and eco-friendly alternative to cement. It is owing to its low power consumption and …

[HTML][HTML] Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer

S Nazar, J Yang, MN Amin, K Khan, M Ashraf… - Journal of Materials …, 2023 - Elsevier
This study used three artificial intelligence-based algorithms–adaptive neuro-fuzzy inference
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …

Proposition of geopolymers obtained through the acid activation of iron ore tailings with phosphoric acid

AR de Carvalho, BR da Silva Calderón-Morales… - … and Building Materials, 2023 - Elsevier
This study aims to develop a geopolymer using iron ore tailings (IOT) as a geopolymer
precursor and phosphoric acid (H 3 PO 4) as an activating agent. IOT was characterized …

Prediction of compressive strength of alkali-activated construction demolition waste geopolymers using ensemble machine learning

J Shen, Y Li, H Lin, H Li, J Lv, S Feng, J Ci - Construction and Building …, 2022 - Elsevier
This paper mainly employed the random forest (RF), gradient boosting (GB) and extreme
gradient boosting (XGB) to predict the compressive strength of alkali-activated construction …

Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

EH Alakara, S Nacar, O Sevim, S Korkmaz… - Construction and Building …, 2022 - Elsevier
This study has two main objectives:(i) to investigate the parameters affecting the
compressive strength (CS) of perlite-containing slag-based geopolymers and (ii) to predict …

Novel approach to synthesize clay-based geopolymer brick: Optimizing molding pressure and precursors' proportioning

M Ahmad, K Rashid - Construction and Building Materials, 2022 - Elsevier
A novel approach has been proposed to synthesize clay-based geopolymer brick; series of
experiments have been performed and three mandatory steps have been evolved. In Step-1 …

Physico-mechanical performance of fly ash based geopolymer brick: influence of pressure− temperature− time

M Ahmad, K Rashid, R Hameed, EU Haq… - Journal of Building …, 2022 - Elsevier
Trillions of masonry fired-clay bricks are being produced globally every year, however, their
manufacturing process is far from eco-friendly. This work was designed to develop fly ash …

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical …

E Yaghoubi, E Yaghoubi, A Khamees… - Neural Computing and …, 2024 - Springer
Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

Prognosis of compressive strength of fly‐ash‐based geopolymer‐modified sustainable concrete with ML algorithms

A Kumar, HC Arora, NR Kapoor, K Kumar - Structural Concrete, 2023 - Wiley Online Library
Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly‐ash‐
based geopolymer (FLAG) concrete has been used in the construction industry for more …