A Continuous Ship Unloader (CSU) is a facility in which multiple buckets rotate to excavate cargo from a ship to land. It is typically designed to have a lifespan of 20 years. However, fatigue damage is likely to occur before the end of its designated lifespan. This study aims to examine the possibility of extending the component"s lifespan by evaluating the remaining useful life of L-holder, a part of CSU, that has been in use for 20 years. Fatigue load history was predicted by measuring the strain with or without strain at the L-holder part requiring periodic replacement. Through tensile and fatigue tests, the remaining life was evaluated when cracks were not present. In addition, the remaining life in the presence of cracks was evaluated through destructive toughness test and fatigue crack propagation test. Life prediction results based on test cycles were obtained. The proposed guidelines are expected to be helpful for preventing CSU accidents.

Abstract It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.