Artificial neural networks for mechanical strength prediction of lightweight mortar

In this paper, the practical results of mechanical strength of different lightweight mortars made with 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95 and 100% of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content have been used to generate artificial neural networks (ANNs). Totally, 52 feed-forward back-propagation neural networks (FFBNN) with different parameters have been investigated in the case of 80 data for training, 15 data for verifying, and 10 data for testing. The performance for producing networks was evaluated by root mean squared error (RMSE) and the correlation coefficient between data. The two selected networks, N1 (Net Architecture 2-10-2) and N2 (Net Architecture 2-10-5-2) had (0.020, 0.027) and (0.017, 0.018) as (Training, Testing) RMSE set and 0.997 and 0.982 as testing correlation coefficient. Key word: Scoria, artificial neural networks, feed-forward back-propagation neural networks.