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基于自适应代理模型的曲线低刚度高架线轨道动态几何状态可靠性评估(英文)
Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state
【摘要】 轨道动态几何状态(TDGS)是影响列车运行状态,结构使用寿命的关键因素之一,而现有的传统仿真方法存在计算成本高、步骤繁琐等问题。开展基于TDGS的轨道可靠性评估具有重要意义。本文首先将TDGS的仿真计算结果作为数据集,提出了基于网格搜索-粒子群优化-遗传算法-多输出最小二乘支持向量机的代理模型,通过测试函数验证了超参数优化算法的有效性。然后,为了开展随机轨道几何不平顺(TGI)条件下的可靠性评估,提出了基于自适应代理模型的概率密度演化方法(PDEM)。最后,以曲线段列车-钢弹簧浮置板轨道-U梁为工程案例,验证了在时域上,代理模型可准确预测轨道动态几何状态,峰值最大相差0.6524 mm,均值相差0.1946 mm。在频域上,代理模型预测结果不仅可以准确反映位于1/n桥梁跨度、板长等处的特征波长,功率谱峰值大小也与仿真计算结果吻合较好。在相同的预测精度条件下,基于自适应代理模型的PDEM相比蒙特卡罗方法计算效率提高1个数量级,相比基于仿真的PDEM计算效率提高4个数量级。可靠性评估结果表明,TDGS部分峰值管理指标(左/右轨垂向动态不平顺、右轨轨向动态不平顺和三角坑)的可靠性值分别为0.9648、0.9918、0.9978和0.9901,TDGS平均管理指标(轨道质量指数)的可靠性值为0.9950。基于以上结果,本文提出的方法可以准确、高效地基于轨道动态几何状态评估曲线低刚度高架线的可靠性,为轨道养护维修提供理论依据。
【Abstract】 Traditional track dynamic geometric state(TDGS) simulation incurs substantial computational burdens, posing challenges for developing reliability assessment approach that accounts for TDGS. To overcome these, firstly, a simulation-based TDGS model is established, and a surrogate-based model, grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression, is established. Among them, hyperparameter optimization algorithm’ s effectiveness is confirmed through test functions. Subsequently, an adaptive surrogate-based probability density evolution method(PDEM) considering random track geometry irregularity(TGI) is developed. Finally, taking curved train-steel spring floating slab track-U beam as case study, the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains, but also surpasses existing models. Additionally, the adaptive surrogate-based PDEM shows high accuracy and efficiency, outperforming Monte Carlo simulation and simulation-based PDEM. The reliability assessment shows that the TDGS part peak management indexes, left/right vertical dynamic irregularity, right alignment dynamic irregularity, and track twist, have reliability values of 0.9648, 0.9918, 0.9978, and 0.9901, respectively. The TDGS mean management index, i.e., track quality index, has reliability value of 0.9950. These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts, providing a theoretical basis for the TGI maintenance.
【Key words】 reliability assessment; track dynamic geometric state; hybrid machine learning algorithm; adaptive learning strategy; probability density evolution method;
- 【文献出处】 Journal of Central South University ,中南大学学报(英文版) , 编辑部邮箱 ,2024年11期
- 【分类号】TB114.3;U213.21
- 【下载频次】10