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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (8): 1691-1703    DOI: 10.3785/j.issn.1008-973X.2024.08.016
    
Land subsidence risk zoning for high speed railway based on hesitant fuzzy linguistic model
Chuxin WANG2(),Yingchao WANG1,2,*(),Jiguang YANG3,Xiamin FAN3,Zheng ZHANG3,4
1. State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground, China University of Mining and Technology, Xuzhou 221116, China
2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
3. Construction Headquarters of Xuzhou Railway Hub Project, China Railway Shanghai Bureau Group Limited Company, Xuzhou 221000, China
4. Hefei Railway Hub Project Construction Headquarters, China Railway Shanghai Bureau Group Limited Company, Hefei 230011, China
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Abstract  

A new hesitant fuzzy 2-tuple-data envelopment analysis (DEA) model was established in order to assess the risk of land subsidence along high-speed railway. Assessment opinions were handled with the computation theory of hesitant fuzzy 2-tuple linguistic model. Experts’ weights were adjusted based on the principle of maximum group consensus, and the factors’ weights were determined by using the DEA model, which made up for the defects of the previous assessment methods, such as difficulty of passing consistency test, difficulty of assessment process, and low degree of consensus of the evaluators. The hesitant fuzzy 2-tuple-DEA analysis model was applied to a high-speed railway project under construction in Huaibei, Anhui Province. The risk assessment index system of hazard factors, vulnerability factors and sensitivity factors was established. The fuzzy assessment set of individual hesitation and matrix of group hesitation were established, and weight of each factor was obtained. The visual display of risk distribution in the disaster, vulnerability and sensitivity index assessment area was realized based on GIS system. Land subsidence risk distribution along the high-speed railway was obtained. Results show that DK66-DK67 is the highest risk section, and the subsidence monitoring in this section should be strengthened.

Key words： hesitant fuzzy theory      land subsidence      risk assessment      data envelopment analysis (DEA)      high-speed railway     
Received: 24 July 2023      Published: 23 July 2024
CLC:  TU 997  
Fund:  国家自然科学基金资助项目（42272313）；国家重点研发计划资助项目（2022YFC3003304）；中国铁路上海局集团有限公司科研资助项目（2022178）.
Corresponding Authors: Yingchao WANG     E-mail: 1287670901@qq.com;wych12345678@126.com
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Chuxin WANG
Yingchao WANG
Jiguang YANG
Xiamin FAN
Zheng ZHANG
Cite this article:

Chuxin WANG,Yingchao WANG,Jiguang YANG,Xiamin FAN,Zheng ZHANG. Land subsidence risk zoning for high speed railway based on hesitant fuzzy linguistic model. Journal of ZheJiang University (Engineering Science), 2024, 58(8): 1691-1703.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.08.016     OR     https://www.zjujournals.com/eng/Y2024/V58/I8/1691


基于犹豫模糊语言模型的高铁地面沉降风险区划

为了评估高速铁路的沿线地面沉降风险，建立新的犹豫模糊二元语言-数据包络分析(DEA)模型. 该方法根据犹豫模糊二元语言计算理论处理评估意见，基于最大群体共识度原则调整专家权重，采用DEA模型确定因子权重，弥补以往评估方法一致性检验难通过、评估过程难度高及评估人员共识度低的缺陷. 将犹豫模糊二元语言-DEA分析模型应用于安徽省淮北正在建设的某高铁工程，构建灾害因子、易损因子和敏感因子的风险评估指标体系，建立个体犹豫模糊评价集合和群体犹豫模糊评价矩阵，求得各因子权重. 基于GIS系统实现灾害性、易损性和敏感性指标区域风险分布的可视化展示，得到高铁沿线地面沉降风险分布. 结果表明，DK66-DK67为风险最高区段，须加强该区段的沉降监测.


关键词： 犹豫模糊理论,  地面沉降,  风险评估,  数据包络分析(DEA),  高铁 
Fig.1 Questionnair for collection of risk assessment opinion
Fig.2 System of subsidence risk assessment
分级H1H2/mH3/(mm·a?1)H4/mH5/mm
1无明显塌陷?20 ~ ?160~10150~175< 200
2—?16 ~ ?1211~20175~200200~400
3有明显塌陷趋势?12 ~ ?821~30200~225400~600
4—?8 ~ ?431~40225~250600~800
5已发生明显塌陷?4 ~ 0>40250~275>800
Tab.1 Grading standard of hazard factor[38-43]
Fig.3 Distribution of ground collapse disaster
分级V6/mV5V2V4
110~15<10工业用地四级公路
215~20—居住用地三级公路
320~2510~15公共服务二级公路
425~30—农业用地一级公路
5>30>15生态绿地高速公路
Tab.2 Grading standards of vulnerability factor[44-45]
分级S4/mS5/(km·h?1)S2S1/m
1<5250纵连板式5 000~6 500
25~20——6 500~8 000
320~40300单元板式8 000~9 500
440~150——9 500~11 000
5>150350双块式>11 000
Tab.3 Grading standard of sensitivity factor[40, 46-48]
Fig.4 Flow chart of risk assessment
Fig.5 Schematic diagram of study area[49]
Fig.6 Spatial distribution of hazard risk level and hazard factor in study area
Fig.7 Spatial distribution of vulnerability risk level and vulnerability factor in study area
DMH1H2H3H4H5H6
DM1{l3, l4}{l6, l7}{l5, l6}{l7, l8}{l4, l5}{l8}
DM2{l3, l4, l5}{l5, l6, l7}{l6, l7}{l8}{l3, l4}{l7, l8}
DM3{l3, l4, l5}{l6, l7}{l6, l7}{l7, l8}{l3, l4}{l7, l8}
Tab.4 Set of individual expert hesitant fuzzy assessment for hazard factor
DMH1H2H3H4H5H6
DM1{l3, l3, l3, l4, l4, l4}{l6, l6, l6, l7, l7, l7}{l5, l5, l5, l6, l6, l6}{l7, l7, l7, l8, l8, l8}{l4, l4, l4, l5, l5, l5}{l8, l8, l8, l8, l8, l8}
DM2{l3, l3, l4, l4, l5, l5}{l5, l5, l6, l6, l7, l7}{l6, l6, l6, l7, l7, l7}{l8, l8, l8, l8, l8, l8}{l3, l3, l3, l4, l4, l4}{l7, l7, l7, l8, l8, l8}
DM3{l3, l3, l4, l4, l5, l5}{l6, l6, l6, l7, l7, l7}{l6, l6, l6, l7, l7, l7}{l7, l7, l7, l8, l8, l8}{l3, l3, l3, l4, l4, l4}{l6, l6, l7, l7, l8, l8}
Tab.5 Matrix of normalized individual expert hesitant fuzzy assessment for hazard factor
因子评价结果
H1{(l3, +0.000 3), (l3, +0.000 3), (l4, ?0.393 8), (l4, +0.000 4), (l5, ?0.393 8), (l5, ?0.393 8)}
H2{(l6, ?0.287 8), (l6, ?0.287 8), (l6, ?0.287 8), (l7, ?0.287 7), (l7, ?0.287 7), (l7, ?0.287 7)}
H3{(l6, ?0.393 6), (l6, ?0.393 6), (l6, ?0.393 6), (l7, ?0.393 5), (l7, ?0.393 5), (l7, ?0.393 5)}
H4{(l7, +0.289 1), (l7, +0.289 1), (l7, +0.289 1), (l8, +0.000 8), (l8, +0.000 8)(l8, +0.000 8)}
H5{(l3, +0.394 5), (l3, +0.394 5), (l3, +0.394 5), (l4, +0.394 6), (l4, +0.394 6), (l4, +0.394 6)}
H6{(l7, +0.394 9), (l7, +0.394 9), (l7, +0.394 9), (l8, +0.000 8), (l8, +0.000 8), (l8, +0.000 8)}
Tab.6 Matrix of group hesitant fuzzy assessment for hazard factor
因子权重因子权重
H10.107 6H40.216 2
H20.175 7H50.110 1
H30.172 7H60.217 7
Tab.7 Weights of hazard factors
Fig.8 Spatial distribution of sensitivity risk level and sensitivity factor along high-speed railway
Fig.9 Spatial distribution of risk level along high-speed railway
因子分类因子权重
灾害性因子地面塌陷区（H1）0.107 6
地下水位（H2）0.175 7
差异沉降速率（H3）0.172 7
松散层厚度（H4）0.216 2
降雨量（H5）0.110 1
抽水井的地理位置（H6）0.217 7
易损性因子人口密度（V1）0.165 0
用地类型（V2）0.161 7
轨道交通（V3）0.190 1
道路类型（V4）0.198 4
城镇密度（V5）0.136 6
地面高程（V7）0.148 2
敏感性因子平曲线半径（S1）0.203 2
轨道类型（S2）0.310 3
砟道类型（S3）0.199 4
跨度（S4）0.075 7
车速（S5）0.211 3
Tab.8 Weights of factors
[1]   BAGHERI-GAVKOSH M, HOSSEINI S, ATAIE-ASHTIANI B, et al Land subsidence: a global challenge[J]. The Science of the Total Environment, 2021, 778: 146493
[2]   YE S J, XUE Y Q, WU J C, et al. Progression and mitigation of land subsidence in China[J]. Hydrogeology Journal, 2016, 24: 685- 693
doi: 10.1007/s10040-015-1356-9
[3]   翟婉明, 赵春发 现代轨道交通工程科技前沿与挑战[J]. 西南交通大学学报, 2016, 51 (2): 209
ZHAI Wanming, ZHAO Chunfa Frontiers and challenges of sciences and technologies in modern railway engineering[J]. Journal of Southwest Jiaotong University, 2016, 51 (2): 209
[4]   岳建刚 鲁南高铁沿线地面沉降现状及原因分析[J]. 铁道勘察, 2020, 46 (2): 60
YUE Jiangang Analysis on the current situation and causes of land subsidence along the Lunan high speed railway[J]. Railway Investigation and Surveying, 2020, 46 (2): 60
[5]   尚金光, 张献州, 官超伟 高速铁路沉降评估中工程沉降和区域沉降的可区分性研究[J]. 测绘科学, 2013, 38 (1): 84- 86
SHANG Jinguang, ZHANG Xianzhou, GONG Chaowei Distinguishability of engineering settlement and land subsidence in the settlement evaluation of high-speed railway[J]. Science of Surveying and Mapping, 2013, 38 (1): 84- 86
[6]   卢颖, 郭良杰, 侯云玥, 等 多灾种耦合综合风险评估方法在城市用地规划中的应用[J]. 浙江大学学报: 工学版, 2015, 49 (3): 538- 546
LU Ying, GUO Liangjie, HOU Yunyue, et al Comprehensive multi-hazard risk assessment method applicated in urban land-use planning[J]. Journal of Zhejiang university: Engineering Science, 2015, 49 (3): 538- 546
[7]   RAJMATIi O, GOLKARIAN A, BIGGS T, et al Land subsidence hazard modeling: machine learning to identify predictors and the role of human activities[J]. Journal of Environmental Management, 2019, 236: 466- 480
doi: 10.1016/j.jenvman.2019.02.020
[8]   LYU H M, SHEN S L, ZHOU A, et al Assessment of safety status of shield tunnelling using operational parameters with enhanced SPA[J]. Tunnelling and Underground Space Technology, 2022, 123: 104428
doi: 10.1016/j.tust.2022.104428
[9]   SU S l, LI D, YU X, et al Assessing land ecological security in Shanghai (China) based on catastrophe theory[J]. Stochastic Environmental Research and Risk Assessment, 2011, 25: 737- 746
doi: 10.1007/s00477-011-0457-9
[10]   LYU H M, SUN W J, SHEN S L, et al. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach [J]. Science of The Total Environment . 2018, 626: 1012-1025.
[11]   任娟娟, 刘宽, 王伟华, 等 基于区间层次分析的CRTS Ⅲ型板式无砟轨道开裂状况评估[J]. 浙江大学学报: 工学版, 2021, 55 (12): 2267- 2274
REN Juanjuan, LIU Kuan, WANG Weihua, et al Evaluation of cracking condition for CRTS Ⅲ prefabricated slab track based on interval analytic hierarchy process[J]. Journal of Zhejiang University: Engineering Science, 2021, 55 (12): 2267- 2274
[12]   SIERRA L, YEPES V, PELLICER E A review of multi-criteria assessment of the social sustainability of infrastructures[J]. Journal of Cleaner Production, 2018, 187: 496- 513
doi: 10.1016/j.jclepro.2018.03.022
[13]   DONG Y C, CHEN X, HERRERA F Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making[J]. Information Sciences, 2015, 297: 95- 117
doi: 10.1016/j.ins.2014.11.011
[14]   TORRA V Hesitant fuzzy sets[J]. International Journal of Intelligent Systems, 2010, 25 (6): 529- 539
[15]   XIA M M, XU Z Hesitant fuzzy information aggregation in decision making[J]. International Journal of Approximate Reasoning, 2011, 52 (3): 395- 407
doi: 10.1016/j.ijar.2010.09.002
[16]   ZADEH L The concepts of a linguistic variable and its application to approximate reasoning[J]. Information Sciences, 1975, 8 (3): 199- 249
doi: 10.1016/0020-0255(75)90036-5
[17]   HERRERA F, ALONSO S, CHICLANA F, et al Computing with words in decision making: foundations, trends and prospects[J]. Fuzzy Optimization and Decision Making, 2009, 8 (4): 337- 364
doi: 10.1007/s10700-009-9065-2
[18]   WANG J, HAO J A new version of 2-tuple fuzzy linguistic representation model for computing with words[J]. IEEE Transactions on Fuzzy Systems, 2006, 14 (3): 435- 445
doi: 10.1109/TFUZZ.2006.876337
[19]   RODRIGUEZ R, LABELLA Á, MARTINEZ L An overview on fuzzy modelling of complex linguistic preferences in decision making[J]. International Journal of Computational Intelligence Systems, 2016, 9: 81- 94
doi: 10.1080/18756891.2016.1180821
[20]   LIAO H C, XU Z S, HERRERA-VIEDMA E, et al Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey[J]. International Journal of Fuzzy Systems, 2018, 20: 2084- 2110
doi: 10.1007/s40815-017-0432-9
[21]   廖虎昌, 缑迅杰, 徐泽水 基于犹豫模糊语言集的决策理论与方法综述[J]. 系统工程理论与实践, 2017, 37 (1): 35- 48
LIAO Huchang, GOU Xunjie, XU Zeshui A survey of decision making theory and methodologies of hesitant fuzzy linguistic term set[J]. System Engineering Theory and Practice, 2017, 37 (1): 35- 48
[22]   MENG F Y, TANG J, LI C L Uncertain linguistic hesitant fuzzy sets and their application in multi-attribute decision making[J]. International Journal of Intelligent Systems, 2018, 33 (3): 586- 614
doi: 10.1002/int.21957
[23]   MENG F Y, CHEN X H A hesitant fuzzy linguistic multi-granularity decision making model based on distance measures[J]. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, 2015, 28 (4): 1519- 1531
[24]   HUANG J, YOU X Y, LIU H C, et al New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory[J]. International Journal of Production Research, 2019, 57: 1283- 1299
doi: 10.1080/00207543.2018.1470343
[25]   NAZ S, AKRAM M, DAVVAZ B, et al A new decision-making framework for selecting the river crossing project under dual hesitant q-rung orthopair fuzzy 2-tuple linguistic environment[J]. Soft Computing, 2023, 27: 12021- 12047
doi: 10.1007/s00500-023-08739-z
[26]   王磊, 赵臣啸 , 薛惠锋, 等. 基于犹豫模糊语言的专家综合集成研讨方法[J]. 系统工程理论与实践, 2021, 41(8): 2157-2168.
WANG Lei, ZHAO Chenxiao, XUE Huifeng, et al. The expert synthesis and integration research method based on hesitant fuzzy language [J]. System Engineering Theory and Practice , 2021, 41(8): 2157-2168.
[27]   PRAKASH T, KUMAR A, DURAI C, et al Enhanced Elman spike neural network optimized with flamingo search optimization algorithm espoused lung cancer classification from CT images[J]. Biomedical Signal Processing and Control, 2023, 84: 104948
doi: 10.1016/j.bspc.2023.104948
[28]   YING X J, NI T, LU M X, et al. Sub-catchment-based urban flood risk assessment with a multi-index fuzzy evaluation approach: a case study of Jinjiang district, China [J]. Geomatics Natural Hazards and Risk , 2023, 14(1): 1–23.
[29]   ROUYENDEGH B, OZTEKIN A, EKONG J, et al Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach[J]. Annals of Operations Research, 2019, 278: 361- 378
doi: 10.1007/s10479-016-2330-1
[30]   MOUSAVI-NASAB S H, SOTOUDEH-ANVARI A A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems[J]. Materials and Design, 2017, 121: 237- 253
doi: 10.1016/j.matdes.2017.02.041
[31]   王伟武, 黎菲楠, 王頔, 等 基于通风潜力及风特征量化分析的城市风道构建[J]. 浙江大学学报: 工学版, 2019, 53 (3): 470- 481
WANG Weiwu, LI Feinan, WANG Di, et al Urban ventilation corridor construction based on ventilation potential and quantitative analysis of wind characteristics[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (3): 470- 481
[32]   黄赠, 王锐, 赵宇, 等 隐伏断层地震诱发滑坡易发性评价[J]. 浙江大学学报: 工学版, 2017, 51 (11): 2136- 2143
HUANG Zeng, WANG Rui, ZHAO Yu, et al Susceptibility assessment of landslides triggered by buried fault earthquake[J]. Journal of Zhejiang University: Engineering Science, 2017, 51 (11): 2136- 2143
[33]   RAHMAWATY, SIAHAAN J, NURYAWAN A, et al. Mangrove cover change (2005-2019) in the Northern of Medan City, North Sumatra, Indonesia [J]. Geocarto International , 2023, 38(1): 1–28.
[34]   WU P, ZHOU L G, MARTINEZ L An integrated hesitant fuzzy linguistic model for multiple attribute group decision-making for health management center selection[J]. Computers and Industrial Engineering, 2022, 171: 108404
doi: 10.1016/j.cie.2022.108404
[35]   WU P, ZHOU L G, CHEN H Y, et al Additive consistency of hesitant fuzzy linguistic preference relation with a new expansion principle for hesitant fuzzy linguistic term sets[J]. IEEE Transactions on Fuzzy Systems, 2019, 27 (4): 716- 730
doi: 10.1109/TFUZZ.2018.2868492
[36]   COOK W, SEIFORD L Data envelopment analysis (DEA): thirty years on[J]. European Journal of Operational Research, 2019, 192 (1): 1- 17
[37]   LYU H M, SUN W J, SHEN S L, et al Risk assessment using a new consulting process in fuzzy AHP[J]. Journal of Construction Engineering and Management-ASCE, 2020, 146: 04019112
doi: 10.1061/(ASCE)CO.1943-7862.0001757
[38]   鲁明星. 开采沉陷区残余变形时空演化规律及其对地面建筑影响[D]. 北京: 北京科技大学, 2019.
LU Mingxing. Research on spatial and temporal evolution of residual deformation in mining subsidence area and the influence of ground buildings [D]. Beijing: University of Science and Technology Beijing, 2019.
[39]   曹伟伟. 采灌作用下地层变形与含水层水位变化的相关性分析及沉降预测[D]. 上海: 上海交通大学, 2020.
CAO Weiwei. Correlation analysis of stratum deformation and water level variation of aquifers and land subsidence prediction concerning groundwater exploitation and recharge [D]. Shanghai: Shanghai Jiao Tong University, 2020.
[40]   国家铁路局. 高速铁路设计规范: TB 10621—2014 [S]. 北京: 中国铁道出版社, 2014.
[41]   狄胜同. 地下水开采导致地面沉降全过程宏细观演化机理及趋势预测研究[D]. 济南: 山东大学, 2020.
DI Shengtong. Research on macro-mesoscopic evolution mechanism of whole process and trend prediction of land subsidence caused by groundwater exploitation [D]. Jinan: Shandong University, 2020.
[42]   郑景云, 尹云鹤, 李炳元 中国气候区划新方案[J]. 地理学报, 2010, 65 (1): 3
ZHENG Jingyun, YIN Yunhe, LI Bingyuan A new scheme for climate regionalization in China[J]. Acta Geographica Sinica, 2010, 65 (1): 3
doi: 10.11821/xb201001002
[43]   边超. 地下水开采引发地面沉降对鲁南高铁沿线的影响性分析及防治[D]. 济南: 山东大学, 2021.
BIAN Chao. Analysis and prevention of influence of ground subsidence caused by groundwater exploitation on south Shandong high-speed railway [D]. Jinan: Shandong University, 2021.
[44]   吕海敏. 城市地铁系统沉涝灾害风险评估方法与防灾对策[D]. 上海: 上海交通大学, 2019.
LV Haimin. Risk assessment methods and countermeasures for floods of metro system in subsidence environment [D]. Shanghai: Shanghai Jiao Tong University, 2019.
[45]   中华人民共和国交通运输部. 公路工程技术标准: JTG B01—2014 [S]. 北京: 人民交通出版社, 2014.
[46]   邱颖新, 张献州, 张拯, 等 基于物联网模式的高速铁路工后变形监测预警体系研究[J]. 铁道科学与工程学报, 2016, 13 (4): 606- 612
QIU Yingxin, ZHANG Xianzhou, ZHANG Zheng, et al Research on high-speed rail post-construction deformation monitoring and warning system based on internet of things[J]. Journal of Railway Science and Engineering, 2016, 13 (4): 606- 612
doi: 10.3969/j.issn.1672-7029.2016.04.003
[47]   陈兆玮. 高速铁路桥墩沉降对行车性能影响的研究[D]. 成都: 西南交通大学, 2017.
CHEN Zhaowei. Influence of pier settlement on dynamic performance of running trains in high-speed railways [D]. Chengdu: Southwest Jiaotong University, 2017.
[48]   邵旭东. 桥梁工程[M]. 北京: 人民交通出版社, 2019: 5.
[49]   王其合, 张鹏, 李程, 等 控制地面沉降的地下水限采方案研究[J]. 城市轨道交通研究, 2023, (Suppl.2): 92- 99
WANG Qihe, ZHANG Peng, LI Cheng, et al Research on groundwater extraction control scheme for controlling land subsidence[J]. Urban Mass Transit, 2023, (Suppl.2): 92- 99
[50]   邓雪, 李家铭, 曾浩健, 等 层次分析法权重计算方法分析及其应用研究[J]. 数学的实践与认识, 2012, 42 (7): 93- 100
DENG Xue, LI Jiaming, ZENG Haojian, et al Research on computation methods of AHP weight vector and its applications[J]. Journal of Mathematics in Practice and Theory, 2012, 42 (7): 93- 100
doi: 10.3969/j.issn.1000-0984.2012.07.012
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[3] LUO Yue, YE Shu-jun, WU Ji-chun, ZHANG Yan-hong, JIAO Xun, WANG Han-mei. Global sensitivity analysis of parameters in land subsidence model[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(10): 2007-2013.
[4] HE Bin, XIAO Xin-biao, ZHOU Xin, JIN Xue-song. Influence factors of insertion loss and their noise reduction mechanism for noise barrier[J]. Journal of ZheJiang University (Engineering Science), 2017, 51(4): 761-770.
[5] OU Zu-min, SUN Lu, CHENG Qun-qun. Simplified calculation of temperature field in high-speed railway ballastless track structure[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(3): 482-487.
[6] LU Ying, GUO Liang-jie, HOU Yun-yue, ZHAO Yun-sheng, CHEN Lian-jin. Comprehensive multi-hazard risk assessment method applicated in urban land-use planning[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(3): 538-546.
[7] MAO Jie, HAO Zhi-yong, SUN Qiang, ZHENG Xu, MA Xiao-long, ZHANG Qiang. High-speed train medium-frequency interior noise calculation under multi-physical-field coupling excitations[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(2): 315-321.
[8] ZHANG Xing-you, WANG Shou-xiang. Risk assessment of distribution power systems considering communication systems[J]. Journal of ZheJiang University (Engineering Science), 2014, 48(4): 568-574.
[9] CHEN Ren-peng, WANG Zuo-zhou, JIANG Hong-guang, BIAN Xue-cheng. Control standard of differential settlement in high-speed railway slab based on bending fatigue strength [J]. Journal of ZheJiang University (Engineering Science), 2013, 47(5): 796-802.
[10] DAI Gong-lian, ZHENG Peng-fei, YAN Bin, XIAO Xiang-nan. Longitudinal force of CWR on box girder under solar radiation[J]. Journal of ZheJiang University (Engineering Science), 2013, 47(4): 609-614.
[11] HU Ye-Shuang, CHEN Shui-Long, MA Lei. Cutoff effect of groundwater seepage due to existence of underground
structure[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(10): 1902-1906.