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(4) Using trained CSPNN and SPNN to predict compressive strength and slump of experimental and numerical data, respectively.
where [f'.sub.c,CSPNN] and [S.sub.SPNN] are compressive strength and slump predicted by CSPNN and SPNN, respectively.
Using collected experimental data of pozzolanic concrete mixture proportioning from the literature to train CSPNN and SPNN. Using trained CSPNN and SPNN to predict compressive strength ([f'.sub.c,p]) and slump ([S.sub.p]) of experimental and numerical data, respectively.
Training and Testing of the SPNN Using Collected Experimental Data.
Sensitivity Analysis of the SPNN. Figure 12 shows the distribution of slump and SP for the training samples of the SPNN.
Experimental specimens were also made in the laboratory to study the prediction accuracy of the CSPNN and SPNN in terms of pozzolanic concrete conforming to the ACI concrete mixture code.
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