Utilization of Fly Ash in Construction

Ghazali, Norhaiza; Muthusamy, Khairunisa; Ahmad, Saffuan Wan

doi:10.1088/1757-899x/601/1/012023

Cited by 59 publications

(20 citation statements)

References 34 publications

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“…The hazardous materials in FA like silica, alumina, and oxides such as a ferric oxide (Fe 2 O 3 ) are intervening factors in water, soil, and air pollution. This ultimately leads to health issues and different geo-environmental problems [ 6 , 7 ]. A good waste management employment is desirable for the sustainability of a safe environment [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

et al. 2021

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For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

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Section: Introductionmentioning

confidence: 99%

“…Consequently, causing physiological disorders and other related health issues like cancer, hepatic disorder, anemia, dermatitis, and gastroenteritis. FA pollutes surface and underground water which stresses aquatic life and causes skin diseases and diarrhea [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

et al. 2021

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“…Every year, about 375 million tons of FA is generated with a retention cost of $20-$40 per ton (Dwivedi and Jain, 2014). FA contains hazardous minerals like alumina, ferric oxide, and silica; putting to dump fill sites without sufficient treatment creates a destructive and harmful effect on the ecology (Carlson and Adriano, 1993;Kumar Tiwari et al, 2016;Nadesan and Dinakar, 2017;Ghazali et al, 2019). Virtuous waste management is needed for the sustainability of a healthy environment.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the ultrafine particles of FA, when reached to the respiratory system, cause different health issues like cancer, anemia, physiological disorder, dermatitis, and diarrhea. It also pollutes the ground water and alarms the aquatic life (Carlson and Adriano, 1993;Kumar Tiwari et al, 2016;Ghazali et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Geopolymer Concrete Compressive Strength via Artificial Neural Network, Adaptive Neuro Fuzzy Interface System, and Gene Expression Programming With K-Fold Cross Validation

et al. 2021

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The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f′c) of fly ash–based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f′c of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (ρ). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. Generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f′c of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

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“…Rather, eco-friendly and low-cost available materials are much beneficial for the improvement works. Fly ash is abundant in coal based power plant, mostly dumped in open space causes hazards for nature [9]. Fly ash represents pozzolanic behavior in presence of water that release calcium oxides.…”

Section: Introductionmentioning

confidence: 99%

Effect of Fly-Ash on the Strength Development of Coastal Soils-a Case Study for the Southern Coastal Zone of Bangladesh

Debanath

Rahman

Chowdhury

et al. 2021

MJCE

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The rapid demand for urbanization expands the requirements of infrastructures and owing to the scarcity of available firm land; people are now built structures on soft soils. However, the application of a deep foundation for a low-rise structure may not be economically feasible for developing countries. The economic, as well as safe foundation, can be ensured by adopting traditional ground stability approaches. However, replacement of the industrial by-product, which possess minimum environmental threat may be a plausible option for ground stability. Therefore, an attempt is taken in this research to study the improvement of soft coastal soil by replacing with eco-friendly fly ash. The eastern bank of the river Karnaphuli, which has increasing industrial and residential demand is taken as a case study in this research. A series of experimental set-ups have been conducted to evaluate the strength development with different fly ash contents. It is found that the strength of fly ash treated soils increases with fly ash content up to a threshold value, and beyond that, the strength decreases. In addition, compaction and plasticity characteristics are also investigated through experimental observations and show better performance criteria with increasing fly ash contents. In a nutshell, this approach of replacing fly ash is suitable for coastal soil, and the experimental investigation reveals that an optimum 20% of fly ash content is justified.

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Utilization of Fly Ash in Construction

Cited by 59 publications

References 34 publications

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Geopolymer Concrete Compressive Strength via Artificial Neural Network, Adaptive Neuro Fuzzy Interface System, and Gene Expression Programming With K-Fold Cross Validation

Effect of Fly-Ash on the Strength Development of Coastal Soils-a Case Study for the Southern Coastal Zone of Bangladesh

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