Geodesy

Quantum mechanics

Benchmark (surveying)

Linguistics

Engineering

Philosophy

Reliability engineering

Materials science

Stress (linguistics)

Marine engineering

Bivariate analysis

Ship motions

Curse of dimensionality

Power (physics)

Computer science

Geology

Container (type theory)

Paleontology

Mechanical engineering

Physics

Nonlinear system

Hull

Environmental science

Reliability (semiconductor)

Artificial intelligence

Series (stratigraphy)

Structural engineering

Forensic engineering

Deck

Machine learning

Ocean Engineering

Classic reliability methods, dealing with time series, do not have the advantage of dealing efficiently with system high dimensionality and cross-correlation between different dimensions. This study validates a novel structural reliability method suitable for multi-dimensional structural responses versus a well-established bivariate statistical method. An example of this reliability study was a chosen container ship subjected to large deck panel stresses during sailing. The risk of losing containers due to extreme motions is the primary concern for ship transport. Due to the non-stationarity and complicated nonlinearities of both waves and ship motions, it is challenging to model such a phenomenon. In the case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned. Therefore, data measured on actual ships during their voyages in harsh weather provides a unique insight into statistics of ship motions. This work aims to benchmark and validate state of the art method, which makes it possible to extract the necessary information about the extreme response from onboard measured time histories. The method proposed in this paper opens up the possibility of predicting simply and efficiently failure probability for the nonlinear multi-dimensional dynamic system as a whole.

Cargo vessel coupled deck panel stresses reliability study