Probabilistic Assessment of Container Crane Under Wind Loading

Abstract

This chapter discusses current research and opportunities for uncertainty quantification in performance prediction and risk assessment of engineered systems. Model-based simulation becomes attractive for systems that are too large and complex for full-scale testing. However, model-based simulation involves many approximations and assumptions, and thus, confidence in the simulation result is an important consideration in risk-informed decision-making. Sources of uncertainty are both alea-tory and epistemic, stemming from natural variability, information uncertainty, and modeling approximations. The chapter draws on illustrative problems in aerospace, mechanical, civil, and environmental engineering disciplines to discuss (1) recent research on quantifying various types of errors and uncertainties, particularly focusing on data uncertainty and model uncertainty (both due to model form assumptions and solution approximations); (2) framework for integrating information from multiple sources (models, tests, experts), multiple model development activities (calibration, verification, validation), and multiple formats; and (3) using uncertainty quanti-fication in risk-informed decision-making throughout the life cycle of engineered systems, such as design, operations, health and risk assessment, and risk management.