On the Applicability of Time-Series Models for Structural Reliability Analysis

Resumo

The assessment of time-dependent reliability problems is still a challenging task. Besides the difficulty
to characterize a problem from real-world data, most of known solutions rely on approximations suitable only

for specific cases or on burdensome simulation approaches. This is due to the difficulty in working with gen-
eral stochastic processes, particularly for situations of non-ergodicity. A time-series model is a particular case of

stochastic process that operates in continuous state space and discrete time set. Such models can be used to repre-
sent a wide range of random phenomena that spans through time, usually with simpler formulation. They are also

relatively simple to build from data tables, which are usually all the information available about time-dependent

behavior of random engineering systems. This work presents a preliminary study where data generated from con-
tinuous stochastic processes commonly used in structural reliability are used to build different time-series models,

which are then used to replace the original stochastic process in reliability analysis. Auto Regressive, Moving Av-
erage and Auto Regressive Moving Average models are considered. The same time-dependent reliability problem

is solved considering each case, and details about the solutions are addressed.