2022
DOI: 10.28991/cej-2022-08-08-011
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Evaluating the Compressive Strength of Recycled Aggregate Concrete Using Novel Artificial Neural Network

Abstract: In this work, the compressive strength of concrete made from recycled aggregate is studied and an intelligent prediction is proposed by using a novel artificial neural network (ANN), which utilizes a sigmoid function and enables the proposal of closed-form equations. An extensive literature search was conducted, which gave rise to 476 data points containing cement, sand, aggregates, recycled aggregates of fine to coarse texture, water, and plasticizer as the constituents of the concrete and the input variables… Show more

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Cited by 22 publications
(15 citation statements)
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“…In the case of the P models, all the predicted models lie within the RMSE envelop of between 0.5% and 1.0%, a coefficient of determination sector of 95% and above, and a standard deviation between 2.0 and 3.0 points of impact. This performance agrees with the previous performance published in literature [58][59][60][61]. In Figure 10, the variance between measured and modeled values is illustrated.…”
Section: Model (3) -Using Epr Techniquesupporting
confidence: 91%
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“…In the case of the P models, all the predicted models lie within the RMSE envelop of between 0.5% and 1.0%, a coefficient of determination sector of 95% and above, and a standard deviation between 2.0 and 3.0 points of impact. This performance agrees with the previous performance published in literature [58][59][60][61]. In Figure 10, the variance between measured and modeled values is illustrated.…”
Section: Model (3) -Using Epr Techniquesupporting
confidence: 91%
“…Equations 1 and 2 present the output linear formulas for Fc28 and P, respectively, while Figures 8-a and 8-b show their fitness. The average error % of the total dataset is 26.5% and 7.9%, while the R 2 values are 0.481 and 0.912 in that order for Fc28 and P. This agrees with the performance of previous studies [57][58][59][60].…”
Section: Model (1) -Using Tlr or Lnr Techniquesupporting
confidence: 90%
“…On the other hand, the R 2 values of the first submodel were ranged between 0.925 and 0.965 with an average value of 0.945, and for the second sub-model, the R 2 values were ranged between 0.878 and 0.949 with an average value of 0.894, and finally, the R 2 value of the third submodel was 0.960. This compares well with previous research works which utilized fly ash-silica combination and fly ash alone in concrete production [91][92][93][94][95][96], and had used other intelligent methods in their prediction of the concrete mechanical properties. Figure 8 illustrates the relative importance values of each input parameter for P and it indicates that all input parameters have almost the same importance level except (Ag/B) which has a slightly higher importance.…”
Section: Using Ann Techniquesupporting
confidence: 87%
“…Figure 9 compare the accuracies of the developed models graphically. Although the developed predicting models for (Fc28) using (ANN) and (EPR) are accurate enough, they are still hard to implement in practical mix designs, especially for manual calculations [91][92][93][94][95][96]. Hence, concrete mix design tools were developed by substituting in the developed ANN model different combinations of input parameter values that varied at constant intervals.…”
Section: Using Ann Techniquementioning
confidence: 99%
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