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第 3 期                何 卫,等: 实测数据驱动的地铁振动源强 MLP 神经网络预测方法                                     791

                  [D]. 北京: 北京交通大学, 2020.                             way vibration forecast analysis based on RBF neural net⁃
                   QU  Yang.  Study  on  effect  of  vibration  isolation  barrier   work[J].  Journal  of  Tianjin  Chengjian  University,
                   on mitigation of low frequency vibration induced by met⁃  2016, 22(6): 465-470.
                   ro  traffic[D].  Beijing:  Beijing  Jiaotong  University,   [16] 张中帅 . 基于机器学习方法的地铁列车振动源强取值
                   2020.                                             研究[D]. 北京: 北京交通大学, 2022.
             [8] 马蒙, 刘维宁 . 我国文物建筑受列车微振动影响研究                          ZHANG  Zhongshuai.  Determination  of  metro  train-in⁃
                   现况及关键问题分析[J]. 噪声与振动控制, 2019, 39                   duced vibration source intensity based on machine learn⁃
                  (4): 1-6.                                          ing  method[D].  Beijing:  Beijing  Jiaotong  University,
                   MA  Meng,  LIU  Weining.  Overview  and  key  problem   2022.
                   analysis of the vibration influences on historic buildings   [17] 中华人民共和国生态环境部 . 环境影响评价技术导则
                   induced by moving trains in China[J]. Noise and Vibra⁃  城市轨道交通: HJ 453—2018[S]. 北京: 中国环境科
                   tion Control, 2019, 39(4): 1-6.                   学出版社, 2018.
             [9] 巴振宁, 符瞻远, 付继赛, 等 . 地铁列车振动对颐和园                       Ministry  of  Ecology  and  Environment  of  the  People’s
                   北宫门古建筑木结构影响的实测与分析[J]. 振动工程                        Republic of China. Technical guidelines for environmen⁃
                   学报, 2023, 36(6): 1602-1612.                       tal impact assessment-urban rail transit: HJ 453—2018
                   BA Zhenning, FU Zhanyuan, FU Jisai, et al. Measure⁃  [S].  Beijing:  China  Environmental  Science  Press,
                   ment and analysis of the influence of metro train vibra⁃  2018.
                   tion  on  the  ancient  wooden  structures  of  North  Palace   [18] 国家环境保护局 . 城市区域环境振动测量方法 : GB
                   Gate of the Summer Palace[J]. Journal of Vibration En⁃  10071—88[S]. 北京: 中国标准出版社, 1989.
                   gineering, 2023, 36(6): 1602-1612.                State  Bureau  of  Environmental  Protection  of  the  Peo⁃
             [10] 辜小安 . 城市轨道交通环境影响评价中地下线路振动                          ple’s Republic of China. Measurement method of envi⁃
                   源强取值存在的问题与建议[J]. 铁路节能环保与安全                        ronmental  vibration  of  urban  area:  GB  10071—88[S].
                   卫生, 2013, 3(5): 211-216.                          Beijing: Standards Press of China, 1989.
                   GU Xiaoan. The suggestions and the problems of the vi⁃  [19] 北京市市场监督管理局 . 地铁噪声与振动控制规范:
                   bration  source  intensity  data  of  the  underground  line  in   DB 11/T 838―2019[S]. 北京: 北京市市场监督管理
                   the  environmental  impact  assessment  of  the  urban  rail   局, 2019.
                   transit[J].  Railway  Energy  Saving  &  Environmental   Beijing  Municipal  Bureau  of  Market  Supervision  and
                   Protection  &  Occupational  Safety  and  Health,  2013,   Administration. Code for metro noise and vibration con⁃
                   3(5): 211-216.                                    trol: DB 11/T 838―2019[S]. Beijing: Beijing Munici⁃
             [11] CONNOLLY D P, KOUROUSSIS G, GIANNOPOU⁃             pal  Bureau  of  Market  Supervision  and  Administration,
                   LOS  A,  et  al.  Assessment  of  railway  vibrations  using   2019.
                   an efficient scoping model[J]. Soil Dynamics and Earth⁃  [20] 李明航, 马蒙, 刘维宁, 等 . 地铁列车振动源强离散机理
                   quake Engineering, 2014, 58: 37-47.               测试分析[J]. 振动、测试与诊断, 2020, 40(4): 738-744.
             [12] PANEIRO G, DURÃO F O, COSTA E SILVA M,             LI Minghang, MA Meng, LIU Weining, et al. Analy⁃
                   et  al.  Prediction  of  ground  vibration  amplitudes  due  to   sis mechanism of vibration source dispersion induced by
                   urban  railway  traffic  using  quantitative  and  qualitative   metro  trains  through  in  situ  test[J].  Journal  of  Vibra⁃
                   field  data[J].  Transportation  Research  Part  D:  Trans⁃  tion,  Measurement  &  Diagnosis,  2020,  40(4):  738-
                   port and Environment, 2015, 40: 1-13.             744.
             [13] 邱瑞辰 . 基于机器学习算法的地铁交通环境振动数据                     [21] MA M, LI M H, QU X Y, et al. Effect of passing met⁃
                   库预测模型研究[D]. 北京: 北京交通大学, 2021.                     ro  trains  on  uncertainty  of  vibration  source  intensity:
                   QIU Ruichen. Research on prediction model of train-in⁃  monitoring tests[J]. Measurement, 2022, 193: 110992.
                   duced  vibration  database  based  on  machine  learning  al⁃  [22] 温士明, 李伟, 朱强强, 等 . 地铁车轮多边形磨损对浮
                   gorithm[D].  Beijing:  Beijing  Jiaotong  University,   置 板 轨 道 振 动 特 性 的 影 响[J].  噪 声 与 振 动 控 制 ,
                   2021.                                             2018, 38(4): 116-122.
             [14] YAO  J  B,  XIA  H,  ZHANG  N,  et  al.  Prediction  on   WEN Shiming, LI Wei, ZHU Qiangqiang, et al. Influ⁃
                   building vibration induced by moving train based on sup⁃  ence  of  polygonal  wear  of  metro  wheels  on  vibration
                   port vector machine and wavelet analysis[J]. Journal of   characteristics of floating slab tracks[J]. Noise and Vi⁃
                   Mechanical  Science  and  Technology,  2014,  28(6):   bration Control, 2018, 38(4): 116-122.
                   2065-2074.
             [15] 王秀丽, 潘雷, 彭桂力, 等 . 基于 RBF 神经网络的地               第一作者: 何 卫(1987―),男,博士,副教授。
                   铁 振 动 预 测 分 析[J]. 天 津 城 建 大 学 学 报 , 2016, 22          E-mail: hewei2018@cug.edu.cn
                  (6): 465-470.                                 通信作者: 宋世琦(2001―),男,硕士研究生。
                   WANG Xiuli, PAN Lei, PENG Guili, et al. The sub⁃      E-mail: songshiqi0724@cug.edu.cn
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