Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems
提出一种结合极值导向灵敏度方法与多项式混沌展开的新方法，聚焦结构极限状态附近的灵敏度分析，帮助工程师识别影响不同设计准则的关键输入因素，并在列车-轨道-桥梁系统中验证了其准确性和效率。
The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit states, which combines an extreme-oriented sensitivity method with polynomial chaos expansion. This enables engineers to perform sensitivity analysis near given limit states and visualize the relevance of input factors to different design criteria and corresponding thresholds. The polynomial chaos expansion is used to approximate the model output and alleviate the computational cost in sensitivity analysis, which features sparsity and adaptivity to enhance efficiency. The accuracy and efficiency of the method are verified in a truss structure, which is then illustrated on a dynamic train-track-bridge system. The role of the input factors in response variability is clarified, which differs in terms of the design criteria chosen for sensitivity analysis. The method incorporates multi-scenarios and can thus be useful to support decision-making in design and management of engineering structures.