Utilization of a novel artificial intelligence technique (ANFIS) to predict the compressive strength of fly ash-based geopolymer

Ahmad, Madiha; Rashid, Khuram; Tariq, Zainab; Ju, Man Ki

doi:10.1016/j.conbuildmat.2021.124251

Cited by 37 publications

(17 citation statements)

References 51 publications

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“…Various parameters influence the Geo‐p synthesis, hence it also affects the mechanical strength. The following are the most frequently used constituents of the FLAG concrete: fly ash (Fa), coarse aggregate (CA), fine aggregate (FA), sodium hydroxide (NaOH), sodium silicate (Na 2 SiO 3 ), silicon dioxide (SiO 2 ), the molarity of NaOH (NaOH‐M) and sodium oxide (Na 2 O) and same have been used in this study 20,37–39 . The only output variable considered is the CS ( f ck ) of fly‐ash‐based Geo‐p concrete.…”

Section: Methodsmentioning

confidence: 99%

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

Kumar

Arora

Kapoor

et al. 2022

Structural Concrete

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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 than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete. Although analytical methods exist to estimate the CS of concrete, but these models cannot forecast the CS of concrete with better precision due to the complexity of the design mixes. The machine learning (ML)‐based models have been helpful in estimating the CS of concrete with high accuracy and reliability. In this article, four ML algorithms (support vector machine [SVM], linear regression [LR], ensemble learning [EL], and Gaussian process regression [GPR]) and three optimized ML algorithms (EL, SVM, and GPR) have been used to estimate the CS of FLAG concrete. The R‐value of LR, EL, SVMR, GPR, optimized EL, optimized SVMR and optimized GPR models are 0.8916, 0.9172, 0.9313, 0.9529, 0.9459, 0.9348 and 0.9590, respectively. The accuracy of the optimized GPR model with an R‐value of 0.9590 and RMSE value of 1.7132 MPa outperformed all other ML models. The performances of all the developed models have been illustrated through Taylor diagram and error plot. The feature importance of the input parameters has been explained with the explainable ML technique. The developed, optimized GPR model can be reliable tool to estimate the CS with greater accuracy and also reducing time and cost.

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Section: Methodsmentioning

confidence: 99%

“…The adaptive neuro‐fuzzy interface system (ANFIS) algorithm was used by Ahmad et al 20 to estimate the CS of FLAG concrete. When the analytical findings of the ANFIS model were compared to those of the multivariate adaptive regression spline (MARS) model in terms of MAE, RMSE, and R 2 .…”

Section: Introductionmentioning

confidence: 99%

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

Kumar

Arora

Kapoor

et al. 2022

Structural Concrete

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“…A trial-and-error method is used to define membership parameters and rules in a fuzzy system. [19,[21][22][23] It was required to use the ANFIS system to develop an appropriate model between observed inputs and target values that would be accurate. As a part of this experiment, five layers have been identified as performing various functions: the product, the normalized layer, the fuzzy layer, the defuzzy layer, and the final layer are shown in Figure 3A.…”

Section: Adaptive Neuro-fuzzy Inference Systems (Anfis)mentioning

confidence: 99%

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Banza

Rutto

2022

Can J Chem Eng

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The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory-size reactor.An artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg-Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R 2 for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R 2 were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.

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“…Recent research on the single-layer artificial neural network has focused on the neural network modeling of geopolymer concrete [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], while fewer studies have addressed neural network modeling for geopolymer pastes. An additional problem involves the generalizability of the model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

et al. 2023

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The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.

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Utilization of a novel artificial intelligence technique (ANFIS) to predict the compressive strength of fly ash-based geopolymer

Cited by 37 publications

References 51 publications

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

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

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

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