文献类型：期刊文献

英文题名：An Introduction to the Probabilistic Linear Matching Method Framework for Structural Integrity Assessment Under Uncertain Design Conditions

作者：Wang, Xiaoxiao[1]; Chen, Haofeng[2]

机构：[1] Department of Mechanical & Aerospace Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ, United Kingdom; [2] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

卷号：101

起止页码：69

外文期刊名：Lecture Notes in Applied and Computational Mechanics

收录：EI(收录号：20233514655678)

语种：英文

外文关键词：Fatigue of materials - Neural networks - Professional aspects - Reliability analysis - Risk management - Structural analysis - Structural integrity

摘要：The novel probabilistic Linear Matching Method (pLMM) framework is developed by extending the current direct method, the Linear Matching Method (LMM), to deal with the probabilistic structural integrity assessment for engineering components under uncertain operating conditions. The pLMM framework covers several physics-based failure evaluation modules related to cyclic loads at elevated temperatures, including shakedown analysis, ratcheting analysis, low cycle fatigue (LCF) analysis and creep-fatigue analysis. To further improve the prediction efficiency, artificial neural network (ANN) technology is employed to build the data-driven surrogate relationship between the design parameters and the key responses regarding specified failure behaviour, with a series of probabilistic evaluation boundaries and assessment diagrams of engineering structures established to describe the uncertainty of the structural resistance. The reliability analysis techniques are involved as well, by which the failure probability is estimated considering the randomness of engineering problems. The pLMM framework is conducive to getting rid of the excessive dependence on the conventional safety factor with conspicuous conservativeness during risk management, enhancing the robustness of critical infrastructure. ? 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.