工程科学学报，第40卷，第4期：427-437,2018年4月 Chinese Journal of Engineering,Vol.40,No.4:427-437,April 2018 DOI:10.13374/j.issn2095-9389.2018.04.005:http://journals.ustb.edu.cn 基于云理论的油气管道滑坡危险性综合评价 张满银”，王生新)区，孙志忠”，徐震》，王沪生》 1)甘肃省科学院地质自然灾害防治研究所，兰州7300002)中石油管道联合有限公司西部分公司，乌鲁木齐830000 3)安徽省交通规划设计研究总院股份有限公司，合肥230088 ☒通信作者，E-mail:42062509@qq.com 摘要管道滑坡危险性评价是长输油气管道沿线滑坡灾害预防和治理中规划决策的重要依据.该评价组织由定量和定性 两类指标构成，评价系统具有随机性和模糊性的特点.针对常用的定性和半定量评价法在处理系统的随机性和模糊性上存在 顾此失彼和人为主观性强的问题，引入能同时有效反映事物随机性和模糊性的云理论，运用黄金分割率法构建5级标度的管 道滑坡危险性状态标尺云和指标重要性权重云，提出定量指标的不确定性推理过程和定性指标专家群语言云转化的浮动云 偏好集结算法，构建了油气管道滑坡危险性的综合评价模型并进行了工程例证分析.4处待评样本的综合评价结果与半定量 法结果基本一致，并与实际相符.该模型软化了指标边界的硬划分，简化了指标数据的预处理；实现了评价的定量与定性融合 和集成决策：提高了结果的精确性、合理性和可视化. 关键词油气管道；滑坡危险性；云理论：评价体系：不确定性推理；专家群语言：浮动云算法 分类号TE832:X937 Comprehensive evaluation of landslide risks of oil and gas pipelines based on cloud theory ZHANG Man-yin,WANG Sheng-xin,SUN Zhi-zhong",XU Zhen2,WANG Hu-sheng 1)Geological Hazards Research and Prevention Institute,Gansu Academy of Sciences,Lanzhou 730000,China 2)Oil&Gas Transmission Sub-company,Petro China West Pipeline Company,Urumqi 830000,China 3)Anhui Transport Consulting and Design Institute Co.Ltd,Hefei 230088,China Corresponding author,E-mail:42062509@qq.com ABSTRACT Landslides are serious geological hazards along long-distance oil and gas pipelines.Especially common are discontinu- ous-developing single landslides.A single landslide hazard can cause anything from pipeline rupture and fracture to complete failure and shutdown,thus triggering serious secondary disasters.Risk assessments of oil-and-gas-pipeline landslides are an effective method for ascertaining the degree of landslide risk and can provide an important scientific basis for planning and decision-making regarding landslide prevention and control along long-distance oil and gas pipelines.In addition,risk assessments represent an important step in the pipeline-integrity management process.The evaluation system consists of both quantitative and qualitative indexes,which are char- acterized by randomness and fuzziness.To address the subjectivity and incompleteness of qualitative and semi-quantitative evaluation methods in the processing of randomness and fuzziness,the cloud theory was introduced,which can simultaneously reflect randomness and fuzziness.The golden section method was used to establish a five-level standard cloud metric for pipeline landslide risk and index weighting.In the cloud transformation process,this paper proposes uncertainty reasoning for the quantitative index and a floating cloud preference algorithm for expert group language as a qualitative index,which comprises the assessment model for landslide risk of oil and gas pipelines.The comprehensive evaluation results indicate that the floating cloud preference algorithm for the qualitative index is more suitable for the language of expert group decision-making than the synthetic cloud algorithm commonly used.In addition,the results of 收稿日期：2017-10-31 基金项目：国家自然科学基金资助项目(51469001)：中石油管道联合有限公司西部分公司科技开发项目(XG11-2015001):甘肃省科学院软 科学专项项目(2016KP01):甘肃省科学院青年科技创新基金资助项目(2014QN-10)工程科学学报，第 40 卷，第 4 期: 427--437，2018 年 4 月 Chinese Journal of Engineering，Vol． 40，No． 4: 427--437，April 2018 DOI: 10． 13374 /j． issn2095--9389． 2018． 04． 005; http: / /journals． ustb． edu． cn 基于云理论的油气管道滑坡危险性综合评价 张满银1) ，王生新1) ，孙志忠1) ，徐 震2) ，王沪生3) 1) 甘肃省科学院地质自然灾害防治研究所，兰州 730000 2) 中石油管道联合有限公司西部分公司，乌鲁木齐 830000 3) 安徽省交通规划设计研究总院股份有限公司，合肥 230088  通信作者，E-mail: 42062509@ qq． com 摘 要 管道滑坡危险性评价是长输油气管道沿线滑坡灾害预防和治理中规划决策的重要依据． 该评价组织由定量和定性 两类指标构成，评价系统具有随机性和模糊性的特点． 针对常用的定性和半定量评价法在处理系统的随机性和模糊性上存在 顾此失彼和人为主观性强的问题，引入能同时有效反映事物随机性和模糊性的云理论，运用黄金分割率法构建 5 级标度的管 道滑坡危险性状态标尺云和指标重要性权重云，提出定量指标的不确定性推理过程和定性指标专家群语言云转化的浮动云 偏好集结算法，构建了油气管道滑坡危险性的综合评价模型并进行了工程例证分析． 4 处待评样本的综合评价结果与半定量 法结果基本一致，并与实际相符． 该模型软化了指标边界的硬划分，简化了指标数据的预处理; 实现了评价的定量与定性融合 和集成决策; 提高了结果的精确性、合理性和可视化． 关键词 油气管道; 滑坡危险性; 云理论; 评价体系; 不确定性推理; 专家群语言; 浮动云算法 分类号 TE832; X937 收稿日期: 2017--10--31 基金项目: 国家自然科学基金资助项目( 51469001) ; 中石油管道联合有限公司西部分公司科技开发项目( XG11--2015--001) ; 甘肃省科学院软 科学专项项目( 2016KP--01) ; 甘肃省科学院青年科技创新基金资助项目( 2014 QN--10) Comprehensive evaluation of landslide risks of oil and gas pipelines based on cloud theory ZHANG Man-yin1) ，WANG Sheng-xin1)  ，SUN Zhi-zhong1) ，XU Zhen2) ，WANG Hu-sheng3) 1) Geological Hazards Ｒesearch and Prevention Institute，Gansu Academy of Sciences，Lanzhou 730000，China 2) Oil ＆ Gas Transmission Sub-company，Petro China West Pipeline Company，Urumqi 830000，China 3) Anhui Transport Consulting and Design Institute Co． Ltd，Hefei 230088，China  Corresponding author，E-mail: 42062509@ qq． com ABSTＲACT Landslides are serious geological hazards along long-distance oil and gas pipelines． Especially common are discontinu￾ous-developing single landslides． A single landslide hazard can cause anything from pipeline rupture and fracture to complete failure and shutdown，thus triggering serious secondary disasters． Ｒisk assessments of oil-and-gas-pipeline landslides are an effective method for ascertaining the degree of landslide risk and can provide an important scientific basis for planning and decision-making regarding landslide prevention and control along long-distance oil and gas pipelines． In addition，risk assessments represent an important step in the pipeline-integrity management process． The evaluation system consists of both quantitative and qualitative indexes，which are char￾acterized by randomness and fuzziness． To address the subjectivity and incompleteness of qualitative and semi-quantitative evaluation methods in the processing of randomness and fuzziness，the cloud theory was introduced，which can simultaneously reflect randomness and fuzziness． The golden section method was used to establish a five-level standard cloud metric for pipeline landslide risk and index weighting． In the cloud transformation process，this paper proposes uncertainty reasoning for the quantitative index and a floating cloud preference algorithm for expert group language as a qualitative index，which comprises the assessment model for landslide risk of oil and gas pipelines． The comprehensive evaluation results indicate that the floating cloud preference algorithm for the qualitative index is more suitable for the language of expert group decision-making than the synthetic cloud algorithm commonly used． In addition，the results of