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 of the intelligent model. The compressive strength (fc) of the recycled aggregate concrete (RAC), which was studied through multiple experiments, was the output variable of the model. The data points of concrete strength collected through literature show a consistent and sustained strength improvement with the increase in the recycled aggregate proportions. However, the outcome of the concrete compressive strength predictive model shows remarkable performance indices as follows; r is 0.99 and 0.99, R2is 0.98 and 0.97, MSE is 28.67% and 44.64%, RMSE is 5.35% and 6.68%, MAE is 4.12% and 5.01%, and MAPE is 12.73% and 13.83% for the model training and testing respectively. These results compared well with previous studies conducted on RAC with less data, different activation functions, and different techniques. Generally, the closed-form equation, which performed at an average accuracy of 97.5% with an internal consistency of 99%, has shown its potential to be applied in RAC design and construction activities for a sustainable performance evaluation of recycled aggregate concrete. Doi: 10.28991/CEJ-2022-08-08-011 Full Text: PDF

AI Mix Design of Fly Ash Admixed Concrete Based on Mechanical and Environmental Impact Considerations

“…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.…”

“…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].…”

Optimization of Green Concrete Containing Fly Ash and Rice Husk Ash Based on Hydro-Mechanical Properties and Life Cycle Assessment Considerations

“…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.…”

“…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.…”

“…From the corresponded cells in Figure 10, both binder content (B) (ton/m 3 ) and water-binder ratio (W/B) could be determined. Finally, the absolute values of water, plasticizer, cement, (fly ash or rich husk ash), and fine and coarse aggregates [92,94] could be calculated by multiplying their ratios by the binder content, considering that the coarse aggregate content is twice the fine aggregate content. For example, to design a concrete mix with (Fc28=60MPa) and (C/B=80%), the middle left chart in Figure 11 will be used, (Ag/B) is 3.5 and (PL/B) is 1%.…”