An intelligent system is proposed within INCASS (Inspection Capabilities for Enhanced Ship Safety) project for evaluating ship structural reliability and assisting in fatigue damage and structure response assessment. The system combines hydrodynamic, finite element and structural reliability models.. The hydrodynamic analysis model is not discussed in this paper. The finite element model input is a mesh for the midship part of the vessel. Finally, the in-house structural reliability model input is the calculated output of the previous model as well as models for estimating crack development and propagation, corrosion growth and fatigue loading. The output includes the probability of failure for all the investigated components versus time which can be used to assess safe operation through the developed decision support software. The database can receive information from various sources including inspection and robotic systems data. The case study of a capsize bulk carrier the presents structural evaluation process.
<p>For the bridge maintenance strategy and planning, prediction of future performance based on the current performance must be required and it is possible more rational decision-making through the higher accuracy of the prediction model. While performing a detailed inspection of the entire bridge can reduce a significant part of the uncertainty, it is impossible to reduce the uncertainty of inspection result and it is always evaluated by probability. In this study, to solve this problem, a Bayesian update method is applied to the optimal maintenance strategy in Bridge Management System (BMS) considering the uncertainty of inspection data. Also, examples of application are presented, showing the effects of inspection and updating on the bridge maintenance strategies. In this study, application possibility and availability of domestic bridge management system are evaluated by referring to the proposed method in the existing trends.</p>
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