Dynamic soil–structure interaction analysis of buildings by neural networks

Prediction and control of surface roughness in CNC lathe using artificial neural network

“…The output layer, which processes extracted features to obtain the pattern class, has one neuron. However, there is no general rule for selection of the number of neurons in a hidden layer and the number of hidden layers (Pala et al, 2008). Hence, the numbers of hidden layers and neurons in the hidden layer have been determined by trial and are based upon the least effective error and the optimal neural network architecture has been designed using the MATLAB Neural Network Toolbox.…”

Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines

“…Wilson (1998) implemented dynamic experiments using a large centrifuge apparatus on soil-pile-cap specimens and recorded the time history of acceleration response of the pile cap (Wilson, 1998). As one of the rare applications of the ANN to an SSI problem, Pala et al used the finite element method to analyze a direct SSI problem and produced the data needed for the DBM (Pala, Caglar, Elmas, Cevik, Saribiyik, 2006). Stewart et al gathered and sorted the earthquake data recorded in real buildings to calibrate the theoretical equations existing for dynamic characteristics of soil-structure systems (Stewart, Seed, Fenves, 1999).…”

Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

“…Plevris and Asteris [46] applied ANN to approximate the failure surface for masonry materials. Another interesting application of ANN is soil-structure interaction (SSI) problems which can be seen in reference [47]. Ahmadi-Nedushan proposed an ANFIS model and three nonlinear regression models to predict the elastic modulus of normal and high strength concrete [48].…”