Machine learning model for dynamical response of nano-composite pipe conveying fluid under seismic loading

Machine learning approaches including support vector regression (SVR) and multi-layer feedforward backpropagation neural network (FFBNN) were used in the present study along with classic theory for predicting maximum displacement of nanocomposite pipe conveying fluid under seismic load. The FFBNN consisted of three layers: 1) three neurons in input layer including length-to-radius ratio (L/R), fluid velocity (V) and volume percent of carbon nanotube; 2) hidden layer with 11 neurons obtained via trial and error; 3) maximum displacement-based seismic load. SVR model was obtained via three-input data with maximum likelihood estimator. Model predicted results were compared using three metrics, including Nash-Sutcliffe efficiency, root mean squared error and coefficient of correlation for 100 testing and 255 training data points. Results indicated that SVR achieved best predictions in the training phase, while FFBNN provided superior prediction in the testing phase. Increasing L/R, V and decreasing VCNT, increased maximum displacements under seismic load.