Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods

Pazouki, Gholamreza

doi:10.1016/j.measurement.2022.111916

Cited by 20 publications

(2 citation statements)

References 53 publications

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“…The suggested models could also be used for simulating the growth of engineering projects focused on durability as well as to anticipate the lifespan of concrete structures. In the study by Pazouki [36], three different models including, group data processing technique, optimization algorithm of ant colony and ANN supported radial based functional neural network are proposed for predicting the compressive strength of the fly ash originated from geopolymer concrete. In this study, 360 samples of fly ash-based geopolymer concrete were used to generate the data set for this investigation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

Adamu

Çolak

Ibrahim

et al. 2023

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The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionally, concrete fine aggregate has indeed been largely replaced by waste materials like crumb rubber (CR), thus it reduces the mechanical properties but improved some other properties of the concrete. To decrease the detrimental effects of the CR, concrete is therefore enhanced with nanomaterials such nano silica (NS). The concrete mechanical properties are essential for the designing and constRuction of concrete structures. Concrete with several variables can have its mechanical characteristics predicted by an artificial neural network (ANN) technique. Using ANN approaches, this paper predict the mechanical characteristics of concrete constructed with FA as a partial substitute for cement, CR as a partial replacement for fine aggregate, and NS as an addition. Using an artificial neural network (ANN) technique, the mechanical characteristics investigated comprise splitting tensile strength (Fs), compressive strength (Fc), modulus of elasticity (Ec) and flexural strength (Ff). The ANN model was used to train and test the dataset obtained from the experimental program. Fc, Fs, Ff and Ec were predicted from added admixtures such as CR, NS, FA and curing age (P). The modelling result indicated that ANN predicted the strength with high accuracy. The proportional deviation mean (MoD) values calculated for Fc, Fs, Ff and Ec values were −0.28%, 0.14%, 0.87% and 1.17%, respectively, which are closed to zero line. The resulting ANN model’s mean square error (MSE) values and coefficient of determination (R2) are 6.45 × 10−2 and 0.99496, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

Adamu

Çolak

Ibrahim

et al. 2023

Axioms

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“…This signifies that its manufacture is based on activating aluminosilicate-based source materials with alkali hydroxide/alkali silicate [14]. There is a wealth of literature that demonstrates the use of recycled agricultural and industrial materials as promising geopolymer (aluminosilicate) precursors, including fly ash (FAS), ground granulated blast furnace slag (GGBFS), rice husk ash (RHA), silica fume (SF), red mud (RM), and metakaolin (MK) [15][16][17][18][19][20]. In contrast to GPC, the prospective application of GGBFS in the production of environmentally and economically viable concrete appears promising, yielding good cost benefit and very less environmental impact [21,22], increased stiffness [23], and high resistance to chemical attacks [24,25] compared to conventional concrete.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

Oyebisi

Alomayri

2023

Construction and Building Materials

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AI-Assisted Geopolymer Concrete Mix Design: A Step Towards Sustainable Construction

Zia ul Haq,

Sood,

Kumar

2023

Communications in Computer and Information Science

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Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods

Cited by 20 publications

References 53 publications

Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

AI-Assisted Geopolymer Concrete Mix Design: A Step Towards Sustainable Construction

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