摘要
在列车荷载和环境影响的双重作用下,有砟轨道道砟层支承轨枕的刚度会逐渐退化,严重威胁着行车安全。本文建立包含钢轨-轨枕-道砟层的二维有砟轨道模型,以有砟轨道系统中轨枕结构的频率和振型作为输入数据,采用稀疏贝叶斯学习方法,对枕下道砟层的单损伤和多损伤工况进行了损伤识别研究。本文首先采用室内两跨三层异型框架结构损伤试验验证了所提稀疏贝叶斯学习方法的有效性;然后采用该方法对有砟轨道室内试验的损伤工况进行了损伤识别和不确定性评估。实验结果表明,该方法能够准确地识别出道砟层在单损伤和多损伤工况下的损伤位置和损伤程度,同时计算了道砟层损伤参数的后验概率密度分布,有效评估识别结果的不确定度。本文的研究有望为有砟轨道道砟层的损伤检测提供技术支撑。
Under the dual action of the cyclic dynamic train loads and environmental effects,the stiffness of the railway ballast layer to support the sleepers has been degraded gradually,which will seriously affect the running safety of the train.A rail-sleeper-ballast system was established and utilized in the process of identifying the railway ballast damage in the single and multiple-damage cases based on the natural frequencies and mode shapes of the ballasted track system by using sparse Bayesian learning method.Firstly,the feasibility of the proposed sparse Bayesian learning method is verified by using the experimental data of the three-story irregular shape frame structure in the laboratory.And then the proposed method is adopted for damage identification and uncertainty evaluation of a full-scale ballasted track under different damaged cases.The experimental result indicates that the location and extent of the ballast damage are both accurately identified in the single and multiple-damage cases by using sparse Bayesian learning method,and the posterior probability distributions of the identified damage parameters can be calculated to estimate the uncertainties of the identification results.The research of this paper is expected to provide technical support for railway ballast damage detection.
作者
胡琴
陈晗
HU Qin;CHEN Han(School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《土木工程与管理学报》
2022年第2期92-97,共6页
Journal of Civil Engineering and Management
基金
国家自然科学基金(52178287,51708242,51838006)。
关键词
稀疏贝叶斯学习
损伤识别
有砟轨道
道砟层
sparse Bayesian learning
damage identification
ballasted track
railway ballast
