This study analyzed the last 20 years` data available on power plant coal
ashes used in clay brick production. The statistical analysis has been
carried out for a total of 302 cases based on the relevant parameters
reported in the literature. The chemical composition of the clays and coal
ashes, percentage incorporation and maximum particle size of ash, size of
fired samples, peak firing temperature, and the corresponding soaking time
were selected as inputs for modeling. The product characteristics i.e. open
porosity, water absorption, and compressive strength was taken as output
parameters. An artificial neural network model has been developed and showed
a satisfactory fit to experimental data and predicted the observed output
variables with the overall coefficient of determination (r2) of 0.972 during
the training period. Besides, the reduced chi-square, mean bias error, root
mean square error, and mean percentage error were utilized to check the
correctness of the obtained model, which proved the network generalization
capability. The sensitivity analysis of the model suggested that the
quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O
coming from ashes, and MgO coming from clays were the most influential
parameters in descending order for the ash-clay composite bricks` quality,
mostly owing to the influence of fluxes during firing.