Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities
暂无分享,去创建一个
Rouzbeh Abbassi | Renren Zhang | Xinhong Li | Yi Zhang | Guoming Chen | Ming Yang | Guoming Chen | Xinhong Li | R. Abbassi | Ming Yang | Renren Zhang | Yi Zhang
[1] Faisal Khan,et al. Failure probability analysis of the urban buried gas pipelines using Bayesian networks , 2017 .
[2] Michalis Christou,et al. Identification of reference accident scenarios in SEVESO establishments , 2005, Reliab. Eng. Syst. Saf..
[3] Curtis Smith,et al. Bayesian inference in probabilistic risk assessment - The current state of the art , 2009, Reliab. Eng. Syst. Saf..
[4] Yunhua Gong,et al. STAMP-based causal analysis of China-Donghuang oil transportation pipeline leakage and explosion accident , 2018, Journal of Loss Prevention in the Process Industries.
[5] Brane Širok,et al. Towards more detailed determination of third party impact on risk on natural gas pipelines: Influence of population density , 2015 .
[6] Wei Liang,et al. Risk identification of third-party damage on oil and gas pipelines through the Bayesian network , 2018, Journal of Loss Prevention in the Process Industries.
[7] Nima Khakzad,et al. Dynamic risk analysis using bow-tie approach , 2012, Reliab. Eng. Syst. Saf..
[8] Jinduo Xing,et al. An urban pipeline accident model based on system engineering and game theory , 2020 .
[9] Nicola Paltrinieri,et al. On the application of near accident data to risk analysis of major accidents , 2014, Reliab. Eng. Syst. Saf..
[10] Sohag Kabir,et al. A fuzzy Bayesian network approach for risk analysis in process industries , 2017 .
[11] Min Xie,et al. A Real-Time Fault Diagnosis Methodology of Complex Systems Using Object-Oriented Bayesian Networks , 2016, Bayesian Networks in Fault Diagnosis.
[12] Guoming Chen,et al. Developing a dynamic model for risk analysis under uncertainty: Case of third-party damage on subsea pipelines , 2018, Journal of Loss Prevention in the Process Industries.
[13] Brian Veitch,et al. A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents , 2017, Risk analysis : an official publication of the Society for Risk Analysis.
[14] Hong Zhang,et al. Study on Failure of Third-Party Damage for Urban Gas Pipeline Based on Fuzzy Comprehensive Evaluation , 2016, PloS one.
[15] Noor Quddus,et al. Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines , 2020 .
[16] F. Khan,et al. Marine transportation risk assessment using Bayesian Network: Application to Arctic waters , 2018, Ocean Engineering.
[17] Christian Delvosalle,et al. ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries. , 2006, Journal of hazardous materials.
[18] Toly Chen,et al. A fuzzy set approach for event tree analysis , 2001, Fuzzy Sets Syst..
[19] Manchun Li,et al. Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems , 2013 .
[20] Rouzbeh Abbassi,et al. Major accident modelling using spare data , 2017 .
[21] Faisal Khan,et al. Risk assessment of rare events , 2015 .
[22] Wei Liang,et al. Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM , 2012, Eng. Appl. Artif. Intell..
[23] Noor Quddus,et al. Towards a causal model from pipeline incident data analysis , 2020 .
[24] Xiaoli Meng,et al. A novel method of risk assessment based on cloud inference for natural gas pipelines , 2016 .
[25] Pavanaditya Badida,et al. Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis , 2019, Journal of Natural Gas Science and Engineering.
[26] Xinhong Li,et al. An integrated methodology to manage risk factors of aging urban oil and gas pipelines , 2020 .
[27] Z Y Han,et al. Comparison study on qualitative and quantitative risk assessment methods for urban natural gas pipeline network. , 2011, Journal of hazardous materials.
[28] Jiang Xu,et al. Analysis on accident-causing factors of urban buried gas pipeline network by combining DEMATEL, ISM and BN methods , 2019, Journal of Loss Prevention in the Process Industries.
[29] T. Onisawa. An application of fuzzy concepts to modelling of reliability analysis , 1990 .
[30] Brian Veitch,et al. Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations , 2011, Risk analysis : an official publication of the Society for Risk Analysis.
[31] Brian Veitch,et al. Arctic shipping accident scenario analysis using Bayesian Network approach , 2017 .
[32] Vikram Garaniya,et al. Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring , 2017 .
[33] Rui He,et al. A quantitative risk analysis model considering uncertain information , 2018, Process Safety and Environmental Protection.