This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downs...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

the nonlinear static procedures (nsps) proposed by design codes do not lead to reliable results especially for tall buildings. they generally provide inconsistent estimates of inelastic seismic demands, especially for the top floors due to their inabilities in considering the higher modes effects. in this paper, a new enhanced pushover procedure is proposed which is based on the envelope of the...

In this study, the complex behavior of steel encased reinforced concrete (SRC) composite beam–columns in biaxial bending is predicted by multilayer perceptron neural network. For this purpose, the previously proposed nonlinear analysis model, mixed beam-column formulation, is verified with biaxial bending test results. Then a large set of benchmark frames is provided and P-Mx-My triaxial ...

the graphical technique for nonlinear circuits was described that enable us to optimize circuits to obtain maximum output power, maximum efficiency or minimum intermodulation. according to this method a high power amplifier in the ka band was designed. using a nonlinear model of the transistor, optimum slope for load-line was determined so that maximum power at the output was obtainable, then ...