Fatigue Life Assessment of Tower Crane Based on Neural Network to Obtain Stress Spectrum

In order to reduce the probability of crane safety accidents, a method based on radial basis neural network is proposed to quickly obtain the stress spectrum and calculate the remaining life of the crane. Firstly, taking an in-service tower crane as an example, an ANSYS finite element model is established based on actual parameters, and the finite element model is statically analyzed to obtain the location of the dangerous point. Secondly, the typical operating conditions of the crane are simulated. The position of the trolley and the lifting load are used as the input layer while the equivalent stress value at any point is used as the output layer to train the radial basis neural network model. Using the trained radial basis neural network model can obtain time-stress curve at any point quickly. Finally the remaining life is assessed based on the fracture mechanics method. The results show that this method that using the radial basis function neural network model to obtain the time-stress curve at any point can greatly save the cumbersome process and a lot of investment in the field measurement of the crane, and also provides a reliable basis for the long-term safe use and later maintenance of the crane.