Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete

Gunasekara, Chamila; Atzarakis, Peter; Lokuge, Weena; Setunge, Sujeeva

doi:10.3390/polym13060900

Cited by 30 publications

(5 citation statements)

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“…The results show that no single parameter can be used to determine the optimised mix design, and that while it may be possible to identify a suitable range of mix parameters from which to optimise the design it is necessary to undertake a series of trials to determine the specific mix design required for each individual ash. Indeed, current research is focusing on improving this design process using computer modelling techniques such as machine learning, fuzzy logic and neural networks to identify the key parameters and the inter relationship of these parameters [36]. A number of models have been proposed but no definitive solution has yet been identified to enable an optimum mix design to be specified for an individual fly ash at present.…”

Section: Resultsmentioning

confidence: 99%

“…Air and Water permeability were determined utilizing Autoclam permeability equipment (100 mm × 200 mm × 200 mm specimens). Air permeability was measured applying a pressure of 0.5 bar and monitoring the reduction in pressure for 15 min [36]. Water permeability testing was undertaken on the same specimens following a 24 h period.…”

Section: Activator Modulus (Am) =mentioning

confidence: 99%

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Development of durable class F fly ash based geopolymer concretes

Law

Gunasekara

Patrisia

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The production of Portland Cement (PC) has been shown to be responsible for 5-8% of global CO2 emissions. This has led to class F fly ash based geopolymer concrete being developed as a substiture for PC concrete for to reduce these global CO2 emissions. However, research has shown that each fly ash has unique characteristics and requires a specific mix design for each fly ash. This is can occupy a significant amount of time. Furthermore, before the mix can be adopted for commercial application it requires the long-term durability to be established. This gap is one of the primary limitations delaying the adoption of geopolymer concrete. While each fly ash is unique, they do have common characteristics which can be utilized to optimise the mix design process. Geopolymers are also known to have good durability characteristics, in particular for acid and sulphate exposure. This paper reports the mix design optimisation process for six class F fly ashes from Australia, Sri Lanka and Indonesia. The strength properties and durability performance of the optimised mixes is reported including the compressive strength development, chloride and carbonation resistance together with performance when exposed to sulphate and acidic media for the Indonesian fly ash, including to a simulated peat soil designed to replicate the conditions experienced in Indonesia.

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Section: Resultsmentioning

confidence: 99%

Section: Activator Modulus (Am) =mentioning

confidence: 99%

Development of durable class F fly ash based geopolymer concretes

Law

Gunasekara

Patrisia

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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“…The concrete material was prepared and used in the study with the nine parameters (coarse aggregate, FA, fine aggregate, sodium hydroxide, Na 2 SiO 3 , silicon dioxide, Na 2 O, NaOH molarity, and curing time) to obtain the C-S, as described in the literature [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ]. A total of 151 data points has been collected from the mentioned literature for running the selected models.…”

Section: Research Strategymentioning

confidence: 99%

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Cao

Fang

et al. 2022

Materials

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Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

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“…According to Chu et al (Chu et al 2021), models developed from a comprehensive database are accurate in predicting unseen data. Lokuge et al, Toufigh and Jafari, Khan et al, and Gunasekara et al (Lokuge et al 2018;Toufigh and Jafari 2021;Gunasekara et al 2021;Khan et al 2021) employed rigorous databases acquired from previous studies to predict compressive strength of fly ash-based geopolymer concrete cured at elevated temperatures using ANN, MARS, GEP (gene expression programming), and MEP (multi expression programming). Results obtained presented good correlation with experimental data.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Predicting ML Model for Compressive Strength of Fly Ash/GGBFS Geopolymer Concrete

Paswan,

Pain,

Sonkar

et al. 2024

Preprint

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This research investigated the prediction of compressive strength in fly ash/GGBFS geopolymer concrete using three machine learning techniques: artificial neural network (ANN), multivariate adaptive regression splines (MARS), and MultiGene Genetic Programming (MGGP). The performance of these techniques was compared with traditional linear and nonlinear methods. Evaluation metrics such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) were used, along with Taylor diagrams, to conduct a thorough comparative analysis of the prediction models. Sensitivity and parametric analyses were performed to assess the contribution and effectiveness of individual input variables. The results indicated that MGGP outperformed the other models in predicting the compressive strength of fly ash/GGBFS geopolymer concrete. The study demonstrates the potential of predictive tools for concrete strength and emphasizes the importance of considering input parameters' impact on strength prediction. Experimental validation of the selected model further supported its accuracy.

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Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete

Cited by 30 publications

References 50 publications

Development of durable class F fly ash based geopolymer concretes

Development of durable class F fly ash based geopolymer concretes

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

A Comparative Predicting ML Model for Compressive Strength of Fly Ash/GGBFS Geopolymer Concrete

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