列车引起环境振动预测方法与不确定性研究进展

马蒙; 刘维宁; 刘卫丰

doi:10.19818/j.cnki.1671-1637.2020.03.001

列车引起环境振动预测方法与不确定性研究进展

doi: 10.19818/j.cnki.1671-1637.2020.03.001

北京交通大学土木建筑工程学院, 北京 100044

基金项目:

国家自然科学基金 51978043

国家自然科学基金 51778049

详细信息

作者简介:
马蒙(1983-), 男, 四川成都人, 北京交通大学副教授, 工学博士, 从事轨道交通环境振动研究

中图分类号: U231
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出版历程
- 收稿日期: 2020-03-29
- 刊出日期: 2020-06-25

Research progresses of prediction method and uncertainty of train-induced environmental vibration

School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Natural Science Foundation of China 51978043

National Natural Science Foundation of China 51778049

More Information

Author Bio:
MA Meng(1983-), male, associate professor, PhD, mameng@bjtu.edu.cn

摘要

摘要: 系统总结了列车运行引起环境振动的各类预测方法及其不确定性问题, 梳理了初步预测、确认预测和精准预测3个预测等级内各种方法和模型近10年来的发展状况; 讨论了模型输入参数的随机不确定性, 包括车辆之间差异、轮轨磨耗以及预测模型中输入地层参数等带来的不确定性; 根据新的测试结果分析了车轮和钢轨磨耗状态对地铁振动源强不确定性的影响。研究结果表明: 机器学习方法和地层传递函数解析法可用于初步预测阶段; 用于确认预测的各类数值和解析模型日益完善, 预测效率日益提高, 但考虑车轮和钢轨磨耗发展的轮轨激励输入方法仍有待进一步完善, 仍需进一步发展振动传递路径清晰且可用于工程预测的建筑结构动力学模型; 精准预测需要发展混合预测方法并研究其在地下线振动预测中的应用; 目前对预测结果精准性和预测方法可靠性的研究十分欠缺, 绝大多数预测只能给出定值结果, 无法考虑轮轨磨耗、养护管理水平和振动在地层中传播的不确定性; 建议进一步开发具有远程智能离线采样功能, 并可在建筑结构上长期便捷安装的小型振动采集装置, 以便与机器学习预测方法相结合, 从而适应未来智能化预测的发展要求; 建议发展能够描述钢轨短波磨耗状态等级和车轮不圆顺等级的粗糙度谱, 构建完整养护维修周期内环境振动动态预测模型; 应发展具有可靠性及精准度要求的智能化预测方法, 并在未来实现由定值预测向概率预测发展的根本性转变。
- 地铁 /
- 环境振动 /
- 振动预测 /
- 数值模型 /
- 不确定性
Abstract: Various prediction methods and their uncertainty problems for the train-induced environmental vibration were summarized systematically. Various methods and models developed in the recent decade were summarized in three prediction classes such as the scoping prediction, determined prediction and detail prediction. The stochastic uncertainty of model input parameters was discussed, including the uncertainties caused by the difference between trains, the wheel and rail wear growth, and the input soil parameters in the prediction model. Based on the new measurement results, the influence of wheel and rail wear state on the uncertainty of vibration source intensity of metro was analyzed. Research result indicates that the machine learning method and analytical transfer function method of soil layers can be employed in the scoping prediction stage. In the determined prediction stage, various numerical and analytical models improve gradually, and their prediction efficiencies increase gradually. However, the wheel-rail excitation input method considering the developments of wheel and rail wear still needs to be further improved. The dynamics model of building structure with clear vibration propagation path and for the engineering prediction should still be further developed. In the detail prediction, the hybrid prediction method needs to be developed, and the application on the vibration prediction for underground metro lines should be investigated. Up to now, there is lack of researches on the accuracy and precision of prediction result and the reliability of prediction method. Most predictions can only provide results with determined values. The uncertainties of wheel and rail wear, operation maintenance level and vibration propagation in the soil layers cannot be considered. It is suggested to further develop the micro vibration acquisition device with remote intelligent offline sampling function, and it can be permanently installed on the building structures, so as to combine this technique with the machine learning method to adapt to the development requirements of intelligent prediction in future. It is suggested to develop the roughness spectrum that can describe the rail short-wave wear state grade and wheel out-of-round grade, and establish a dynamic environmental vibration prediction model in a whole maintenance and repair cycle. The intelligent prediction method with reliability and accuracy requirements should be developed, and the prediction should be fundamentally changed from the fixed value prediction to the probability prediction in future.
- metro /
- environmental vibration /
- vibration prediction /
- numerical model /
- uncertainty

HTML全文

图 1 列车引起环境振动影响问题涉及的子系统和研究内容

Figure 1. Subsystems and research contents of train-induced environmental vibration impact problem

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图 2 预测列车引起环境振动的神经网络法结构示意

Figure 2. Structural schematic of neural network method for predicting train-induced environmental vibration

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图 3 采用混合方法预测新建地铁线路附近建筑振动

Figure 3. Predicting building vibration near newly-built railway line by hybrid method

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图 4 概率预测结果准确度、精确度的含义及其与射击打靶结果类比

Figure 4. Concepts of accuracy and precision in probabilistic prediction results and analogy with shooting results

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图 5 预测模型输入参数随机不确定性

Figure 5. Stochastic uncertainties of input parameters for prediction model

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图 6 北京地铁某区间隧道内实测200趟列车引起隧道壁振动响应统计

Figure 6. Statistics of tunnel wall vibration responses induced by 200 trains measured in a running tunnel in Beijing metro

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图 7 列车车轮镟修前后引起隧道壁垂向加速度比较

Figure 7. Comparison of tunnel wall vertical accelerations induced by one train before and after its wheel turning repair

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图 8 不同钢轨磨耗状态下隧道壁振动响应比较

Figure 8. Comparison of tunnel wall vibration responses under different conditions of rail wear

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表 1 预测方法划分

Table 1. Classification of prediction methods

预测等级	适用阶段	预测方法	预测成本
初步预测	可行性研究	经验法、解析法、人工智能	低
确认预测	方案设计阶段	数值法、半解析法、测试校准模型	中
精准预测	施工设计阶段	实测传递函数法、混合法	高

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表 2 代表性数值模型在轨道交通环境振动预测中的应用

Table 2. Typical numerical models employed in prediction of train-induced environmental vibration

边界处理方法	三维	2.5维	周期性
施加人工边界	Xu等^[25-26]	谢伟平等^[27-28]
有限元-边界元耦合法	Galvín等^[29]	Sheng等^[30-33]	Gupta等^[34-36]
有限元-无限元耦合法	曹艳梅等^[37-38]	Alves Costa等^[39-40]	马龙祥等^[41-42]

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