Abstract-In concrete industry, there is a need for water-to-cement ratio (w/c) estimation of cement-based materials since the w/c ratio of cement mixtures is typically given at the batch plant, and this ratio, sometimes, is deliberately changed to have a more workable cement mixture. To meet the requirements of accurate w/c ratio determination of cement-based materials, in this research paper, we propose an artificial neural network approach for w/c ratio estimation of these materials using free-space non-contact reflection and transmission measurements of mortar specimens with w/c ratios of 0.40, 0.45, 0.50, 0.55 and 0.60. We have tested the network and observed less than 5 percent difference between the estimated and known values of w/c = 0.50.

The adsorption of gallium-67, which is used in nuclear medicine, was investigated in this study by using waste acorns of Quercus ithaburensis (WAQI). The experimental parameters were determined to be as follows: temperature, (283 to 313) K; pH, (2.0 to 10.0); stirring speed, (300 to 720) rpm; particle size, (0.15 to 1.40) mm; and adsorbent dose, (1.0 to 15.0) g 3 L À1 . The most effective parameters were pH, temperature, particle size, and adsorbent ratio. The WAQI adsorption mechanism was analyzed by Fourier transform infrared (FTIR) spectra. Adsorption kinetics were studied, and it was seen that WAQI is an excellent Ga-67 adsorbent. An artificial neural network (ANN) was constructed that closely estimates the adsorption amount against temperature, adsorbent ratio, and processing time. The R 2 value of the ANN model was 0.997.