An Efficient Calculation Method for Stress and Strain of Concrete Pump Truck Boom Considering Wind Load Variation

Abstract

Digital twin is the development trend of concrete pump trucks to realize digitalization and intellectualization. The realization of digital twin requires high calculation efficiency and accuracy of the model. As the concrete pump truck works under the wind load, the wind speed and direction on site change frequently and intensely. However, existing methods, such as the finite element method, have the problems of low computational efficiency, high time complexity, and the update frequency being far lower than the frequency of wind change on site. We propose an efficient calculation model for the stress and strain of the pump truck boom based on the back propagation (BP) neural network. The novelty of this work is that when calculating the stress and strain of the boom, the change of the boom posture and the change of the site wind conditions are considered, and the calculation efficiency can be significantly improved. Compared with the finite element simulation, the fitting and prediction accuracy of the stress and strain are more than 99.7%, which can meet the requirements for real-time calculation of the stress and strain of the boom under different attitudes and wind loads in digital twins.