Neural networks for predicting compressive strength of structural light weight concrete

Neural networks procedures provide a reliant analysis in several science and technology fields. Neural network is often applied to develop statistical models for intrinsically non-linear systems because neural networks behave the advantages of simulating complex behavior of many problems. In this investigation, the neural networks (NNs) are used to predict the compressive strength of light weight concrete (LWC) mixtures after 3, 7, 14, and 28 days of curing. Two models namely, feed-forward back propagation (BP) and cascade correlation (CC), were used. The compressive strength was modeled as a function of eight variables: sand, water/cement ratio, light weight fine aggregate, light weight coarse aggregate, silica fume used in solution, silica fume used in addition to cement, superplasticizer, and curing period. It is concluded that the CC neural network model predicated slightly accurate results and learned very quickly as compared to the BP procedure. The finding of this study indicated that the neural networks models are sufficient tools for estimating the compressive strength of LWC. This undoubtedly will reduce the cost and save time in this class of problems.