DOI: 10.5937/jaes16-17019
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
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Volume 16 article 533 pages: 307 - 312
Bravo, E. (2007). Los impactos de la explotación petrolera en ecosistemas tropicales y la biodiversidad. Acción ecológica, Vol. 24 (1), P. 35-42.
Cheng, Y., & Akkar, S. (2017). Probabilistic permanent fault displacement hazard via Monte Carlo simulation and its consideration for the probabilistic risk assessment of buried continuous steel pipelines. Earthquake Engineering & Structural Dynamics, 46(4), 605-620.
El-Abbasy, M. S., Senouci, A., Zayed, T., & Mosleh, F. (2015). A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process. Structure and Infrastructure Engineering, 11(3), 263-281.
Gharabagh, M. J., Asilian, H., Mortasavi, S. B., Mogaddam,
A. Z., Hajizadeh, E., & Khavanin, A. (2009). Comprehensive risk assessment
and management of petrochemical feed and product transportation pipelines.
Journal of Loss Prevention in the Process Industries, 22(4), 533-539.
Guo Y., Meng X., Wang D., Meng T., Liu S., He R. (2016).
Comprehensive risk evaluation of long-distance oil and gas transportation
pipelines using a fuzzy Petri net model. Journal of Natural Gas Science &
Engineering, Vol. 33, P. 18 – 29, doi: 10.1016/j.jngse.2016.04.052.
Hu, Y., Liu, K., Xu, D., Zhai, Z., & Liu, H. (2017,
August). Risk Assessment of Long Distance Oil and Gas Pipeline Based on Grey
Clustering. In Big Knowledge (ICBK), 2017 IEEE International Conference on (pp.
198-201). IEEE.
Kabir, G., Sadiq, R., & Tesfamariam, S. (2015). A fuzzy
Bayesian belief network for safety assessment of oil and gas pipelines.
Structure and Infrastructure Engineering, 12(8), 874-889.
Kovshov, S. (2013). Biological ground recultivation and
increase of soil fertility. International Journal of Ecology & Development,
25(2), 105-113.
Kovshov, S. V., Garkushev, A. U., & Sazykin, A. M.
(2015). Biogenic technology for recultivation of lands contaminated due to
rocket propellant spillage. Acta Astronautica, 109, 203-207.
Kovshov, S. V., & Kovshov, V. P. (2014). Biogenic
fixation of dusting surfaces. Life Science Journal, 11 (9), 401 – 404.
Muhlbauer, W. K. (2004). Pipeline risk management manual:
ideas, techniques, and resources. Elsevier.
Pashkevich M.A., Antciferova T.A. (2013). Risk assessment of
anthropogenic impact of the fuel and energy complex. Journal of Mining
Institute, Vol. 203, P. 225 – 228.
Peng, X. Y., Yao, D. C., Liang, G. C., Yu, J. S., & He,
S. (2016). Overall reliability analysis on oil/gas pipeline under typical
third-party actions based on fragility theory. Journal of Natural Gas Science
and Engineering, 34, 993-1003.
Performance of European cross-country oil pipelines. Statistical
summary of reported spillages in 2015 and since 1971. Concawe, Brussels, 2017.
Podavalov, I.Yu. (2008). Analysis of methods of technogenic
risk calculation in the operation of main gas pipelines. Journal of Mining
Institute, Vol. 178, P. 82 – 85.
Proskuryakov, R.M., Dementev A.S (2016). The building a
system of diagnosing the technical condition of the pipeline on the basis of
continuous pulsed magnetic field. Journal of Mining Institute, Vol. 218, P. 215
– 219.
Shabarchin, O., & Tesfamariam, S. (2016). Internal
corrosion hazard assessment of oil & gas pipelines using Bayesian belief
network model. Journal of Loss Prevention in the Process Industries, 40,
479-495.
Urquidi-Macdonald, M., Tewari, A., & Ayala H, L. F.
(2014). A neuro-fuzzy knowledge-based model for the risk assessment of
microbiologically influenced corrosion in crude oil pipelines. Corrosion,
70(11), 1157-1166.
Vtorushina, A.N., Anishchenko, Y.V., Nikonova, E.D. (2017).
Risk assessment of oil pipeline accidents in special climatic conditions. IOP
Conf. Ser.: Earth Environ. Sci. 66 012006.
Bravo, E. (2007). Los impactos de la explotación petrolera
en ecosistemas tropicales y la biodiversidad. Acción ecológica, Vol. 24 (1), P.
35-42.
Cheng, Y., & Akkar, S. (2017). Probabilistic permanent
fault displacement hazard via Monte Carlo simulation and its consideration for
the probabilistic risk assessment of buried continuous steel pipelines.
Earthquake Engineering & Structural Dynamics, 46(4), 605-620.
El-Abbasy, M. S., Senouci, A., Zayed, T., & Mosleh, F.
(2015). A condition assessment model for oil and gas pipelines using integrated
simulation and analytic network process. Structure and Infrastructure
Engineering, 11(3), 263-281.
Gharabagh, M. J., Asilian, H., Mortasavi, S. B., Mogaddam,
A. Z., Hajizadeh, E., & Khavanin, A. (2009). Comprehensive risk assessment
and management of petrochemical feed and product transportation pipelines.
Journal of Loss Prevention in the Process Industries, 22(4), 533-539.
Guo Y., Meng X., Wang D., Meng T., Liu S., He R. (2016).
Comprehensive risk evaluation of long-distance oil and gas transportation
pipelines using a fuzzy Petri net model. Journal of Natural Gas Science &
Engineering, Vol. 33, P. 18 – 29, doi: 10.1016/j.jngse.2016.04.052.
Hu, Y., Liu, K., Xu, D., Zhai, Z., & Liu, H. (2017,
August). Risk Assessment of Long Distance Oil and Gas Pipeline Based on Grey
Clustering. In Big Knowledge (ICBK), 2017 IEEE International Conference on (pp.
198-201). IEEE.
Kabir, G., Sadiq, R., & Tesfamariam, S. (2015). A fuzzy
Bayesian belief network for safety assessment of oil and gas pipelines.
Structure and Infrastructure Engineering, 12(8), 874-889.
Kovshov, S. (2013). Biological ground recultivation and
increase of soil fertility. International Journal of Ecology & Development,
25(2), 105-113.
Kovshov, S. V., Garkushev, A. U., & Sazykin, A. M.
(2015). Biogenic technology for recultivation of lands contaminated due to
rocket propellant spillage. Acta Astronautica, 109, 203-207.
Kovshov, S. V., & Kovshov, V. P. (2014). Biogenic
fixation of dusting surfaces. Life Science Journal, 11 (9), 401 – 404.
Muhlbauer, W. K. (2004). Pipeline risk management manual:
ideas, techniques, and resources. Elsevier.
Pashkevich M.A., Antciferova T.A. (2013). Risk assessment of
anthropogenic impact of the fuel and energy complex. Journal of Mining
Institute, Vol. 203, P. 225 – 228.
Peng, X. Y., Yao, D. C., Liang, G. C., Yu, J. S., & He,
S. (2016). Overall reliability analysis on oil/gas pipeline under typical third-party
actions based on fragility theory. Journal of Natural Gas Science and
Engineering, 34, 993-1003.
Performance of European cross-country oil pipelines.
Statistical summary of reported spillages in 2015 and since 1971. Concawe,
Brussels, 2017.
Podavalov, I.Yu. (2008). Analysis of methods of technogenic
risk calculation in the operation of main gas pipelines. Journal of Mining
Institute, Vol. 178, P. 82 – 85.
Proskuryakov, R.M., Dementev A.S (2016). The building a
system of diagnosing the technical condition of the pipeline on the basis of
continuous pulsed magnetic field. Journal of Mining Institute, Vol. 218, P. 215
– 219.
Shabarchin, O., & Tesfamariam, S. (2016). Internal
corrosion hazard assessment of oil & gas pipelines using Bayesian belief
network model. Journal of Loss Prevention in the Process Industries, 40,
479-495.
Urquidi-Macdonald, M., Tewari, A., & Ayala H, L. F.
(2014). A neuro-fuzzy knowledge-based model for the risk assessment of
microbiologically influenced corrosion in crude oil pipelines. Corrosion,
70(11), 1157-1166.
Vtorushina, A.N., Anishchenko, Y.V., Nikonova, E.D. (2017). Risk assessment of oil pipeline accidents in special climatic conditions. IOP Conf. Ser.: Earth Environ. Sci. 66 012006.