Calculation method of ship collision force on bridge using artificial neural network

Abstract：Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software. 作者： Author： Wei FAN[1]  Wan-cheng YUAN[1]  Qi-wu FAN[2] 作者單位： State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, ChinaState Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China;Department of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China 期 刊： 浙大學報（英文版）（A輯：應用物理和工程）   ISTICEISCI Journal： JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A 年，卷(期)： 2008, 9(5) 分類號： U44 U66 Keywords： Ship-bridge collision force    Finite element method (FEM)    Artificial neural network (ANN)    Radial basis function neural network (RBFNN)    機標分類號： U66 TP3 機標關鍵詞： artificial neural network    collision force    finite element method    radial basis function    simulation results    network parameters    neural networks    dynamic process    impact force    static load 基金項目： 國家自然科學基金，國家重點基礎研究發展計劃(973計劃)

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