The life cycle characterisation of engineering structures in terms of an anticipated service life remains a significant aspect of sustainability in the construction industry. In the past few years, there has been an increasing societal and industrial demand for the reliable assessment and design of structural systems with service-life criteria of at least several decades. The performance and efficiency of the proposed techniques for the estimation of the failure probability are compared from different aspects, which can be a useful implementation to indicate the complexity of handling the uncertainties provided by corroded pipelines. Illustrative examples that comprise three candidate pipelines made of X52, X65, and X100 steel grade are employed. These LSFs are designed for describing the collapse failure mode for pipelines constructed of low, mid, and high strength steels and are subjected to corrosion degradation. To implement the proposed approaches, three limit state functions (LSFs) using probabilistic burst pressure models are established. This includes five simulation approaches, i.e., Monte Carlo Simulation (MCS), Directional Simulation (DS), Line Sampling (LS), Subset Simulation (SS), and Importance Sampling (IS), and two meta models based on MCS as Kriging-MCS and Artificial Neural Network based on MCS (ANN MCS). In this paper, the reliability analysis of corroded pipelines is investigated using different simulation and metamodel methods. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. By using simulation, the consistency of the log-normal marginal distribution obtained is analysed herein.Įstimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The reliability of the bridges is determined according to their ultimate limit states and statistical load distribution. Basic Monte Carlo Simulation (MCS) is used to simulate the load and resistance of the bridge. The Bayesian approach is used to update the statistical properties of the steel material. Statistical parameters of the bridges are determined both analysing the existing body of knowledge available in the literature and conducting specimen tests. ![]() This study represents a simulation-based reliability analysis of steel girder bridges in the railway lines. However, advances in computer technology allow implementing a fully-probabilistic approach. Reliability indexes are widely used in the analysis of these concepts within a semi-probabilistic approach. Due to scarcity of resources, an economical way should be determined to design and maintain bridges and the transportation system in general. Bridges are an essential component of the transportation system and safety and sustainability of bridges are critical for the efficient operation thereof.
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