Probabilistic demand models and analytical fragility quantification for ship hulls under ultimate bending conditions

Abstract

Fragility analysis is a key performance assessment approach that quantifies the structural performance under a wide spectrum of possible hazard intensities. Fragility profiles can be established for a particular vessel using simulation techniques. However, simulation-based fragility assessment of ship hulls is computationally intensive, and its applicability is limited to the investigated vessel. In contrast, analytical fragility models provide a computationally efficient alternative allowing for wider applicability to cover a particular class of vessels. This paper proposes a novel framework for developing analytical fragility profiles for hulls of a specific vessel class considering ultimate bending conditions. The framework includes the development of probabilistic demand models needed to estimate the statistical characteristics of the engineering demand parameters based on the ship main particulars and hazard intensity measures. Nonlinear regression analysis, using constrained nonlinear optimization algorithms, is conducted to estimate the parameters of the proposed models. The framework is demonstrated on tankers, where probabilistic demand measures for five double-hull tankers are quantified while accounting for uncertainties in the applied loads and their combination factors. The capacity thresholds, representing the maximum demand a vessel can withstand before reaching a specific damage state, are then quantified probabilistically. Analytical fragility profiles are then established and validated against simulation-based fragility results obtained using artificial neural network-assisted finite element simulation. The results show that the developed probabilistic demand models can effectively estimate the statistical descriptors of the demand measure, and the established framework provides a highly computationally efficient alternative to simulation-based fragility assessment.