Optimization of fly ash concrete mix – a solution for sustainable development

Hashmi, A. Fuzail; Shariq, M.; Baqi, Abdul; Haq, Moinul

doi:10.1016/j.matpr.2020.02.908

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…In the Pandas library, data cleaning and analysis features are applied. A total of 324 and 147 SM concrete mix specimens (Laboratory cast) were checked with various researchers' data for considering compressive strength and split tensile strength, respectively [5][6][7][8][9][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The available categorical parameters used for the prediction model are noted in Table 1 and non-categorical mention in Table 2.…”

Section: 𝑍 𝑗"mentioning

confidence: 99%

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An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Mahajan¹,

Bhagat²

2022

Res. Eng. Struct. Mater.

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Supplementary materials (SM) for cement replacement became more feasible in the previous decade due to their pozzolanic strength and durability properties. The strength variation according to the age of the binding material is a critical subject for SM concrete. The time of water curing is critical in order to maintain the pozzolanic reaction in SM concrete, which assists in the development of strength in cementitious properties.In this study, the laboratory results of concrete specimens were assessed for various mix designs, and the obtained corresponding strengths were also predicted with ANN techniques. The difference between experimental and ANN predicted values was marginally low. Thus, the ANN model applicability emphasised the productive use in predicting the strength of supplementary materials like fly ash or any similar pozzalanic mineral admixture. This research proposes an ANN-based technique for predicting the strength of fly ash added SM concrete. Typical experimental data is used to build, train, and test the artificial neural network (ANN) model. With ANN and input parameters, a total of 324 distinct data points for SM concrete were utilised to estimate SM concrete strength. Various combinations of layers, number of neurons, and learning rate were examined during the training phase. When the root mean square error (RMSE) reached or remained below 0.001, the training was stopped, and the findings were verified using a test data set. With respect to the relative error provided for trained model data, the results achieved were typically below 10% for compressive strength and below 5% for split tensile strength. The ANN models predict concrete strength with excellent accuracy, and the findings show that utilising ANNs to predict concrete strength is both practicable and advantageous.

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Section: 𝑍 𝑗"mentioning

confidence: 99%

“…The most significant material attribute is the mechanical strength of any cemented composite because it is designed primarily for compression as a construction material. Average compressive strengths were found in the literature ranging from 25 to 115 MPa [2][3][4][5][6][7][8][9][10][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Mahajan¹,

Bhagat²

2022

Res. Eng. Struct. Mater.

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“…Furthermore, no reports were found that this material was forbidden for use as a substitute for cement binder. However, it was shown that replacement of cement with FA is an important task from the ecological point of view and is clearly environmentally friendly activity, through such actions as [91][92][93][94]:…”

Section: Dry Process Wet Processmentioning

confidence: 99%

Energy Savings Associated with the Use of Fly Ash and Nanoadditives in the Cement Composition

Golewski

2020

Energies

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The paper presented herein investigates the effects of using supplementary cementitious materials (SCMs) in quaternary mixtures on the compressive strength and splitting tensile strength of plain concrete. In addition, environmental benefits resulting from the proposed solutions were analysed. A total of four concrete mixtures were designed, having a constant water/binder ratio of 0.4 and total binder content of 352 kg/m3. The control mixture only contained ordinary Portland cement (OPC) as binder, whereas others incorporated quaternary mixtures of: OPC, fly ash (FA), silica fume (SF), and nanosilica (nS). Based on the obtained test results, it was found that concretes made on quaternary binders containing nanoadditives have very favorable mechanical parameters. The quaternary concrete containing: 80% OPC, 5% FA, 10% SF, and 5% nS have shown the best results in terms of good compressive strength and splitting tensile strength, whereas the worst mechanical parameters were characterized by concrete with more content of FA additive in the concrete mix, i.e., 15%. Moreover, the results of compressive strength and splitting tensile strength are qualitatively convergent. Furthermore, reducing the amount of OPC in the composition of the concrete mix in quaternary concretes causes environmental benefits associated with the reduction of: raw materials that are required for burning clinker, electricity, and heat energy in the production of cement.

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“…In parallel, it follows a reduction in the pore interconnectivity of concrete, thus decreasing its permeability. The pozzolanic reactions are long-term processes; therefore, concrete containing fly ash may show a lower strength at a curing period of 28 days [39,42]. To improve the early-age strength, the development of fly ash concrete is possible by using an elevated curing temperature [43,44].…”

Section: Introductionmentioning

confidence: 99%

Fly Ash as an Ingredient in the Contaminated Soil Stabilization Process

Banaszkiewicz¹,

Marcinkowski²,

Pasiecznik³

2022

Energies

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Fly ash is the main by-product of coal combustion characterized by a large specific surface area. In addition to oxides, it also contains unburned coal and trace elements. This study aimed to investigate the possibility of using fly ash from pit-coal combustion (CFA) for the treatment of benzene-contaminated soil (S). The CFA was used as a mixture with Portland cement (PC) (70% PC + 30% CFA). The soil was treated with a PC-CFA mixture in amounts of 40, 60, and 80% of soil mass. During the process, the concentration of benzene was monitored with the flame-ionization detector. Produced monoliths (S+(PC-CFA)x) were tested for compressive strength and capillary water absorption. The experiment confirmed that the PC-CFA mixture limited benzene emission. The highest reduction in benzene concentration (34–39%) was observed for samples treated with the PC-CFA mixture in an amount of 80% (S+(PC-CFA)80). The average compressive strength of monoliths S+(PC-CFA)40, S+(PC-CFA)60, and S+(PC-CFA)80 was 0.57, 4.53, and 6.79 MPa, respectively. The water absorption values were in the range of 15–22% dm.

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Optimization of fly ash concrete mix – a solution for sustainable development

Cited by 18 publications

References 10 publications

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Energy Savings Associated with the Use of Fly Ash and Nanoadditives in the Cement Composition

Fly Ash as an Ingredient in the Contaminated Soil Stabilization Process

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