Application of Artificial Neural Networks for Seismic Analysis and Design of Buried Pipelines in Heterogeneous Soils

Document Type : Research Paper


Civil and Structural Engineering Department, South Oil Company



Every year, the phenomenon of earthquake causes a lot of human, financial and environmental losses. Transmission pipelines are one of the vital arteries that are very important, however, in the event of an earthquake can cause devastating damages. Safeguarding urban and interurban facilities, including electricity, water supply, oil and gas transmission lines, against these loads requires careful studies and engineering designs. Given that traditional methods for seismic design of pipelines such as FEM modeling and experimental methods are so expensive, a new combined method for predicting the strain of pipes based on the Artificial Neural Network (ANN) is proposed. For this purpose, the parameters of the pipeline including pipe and soil type, length, discharge, path slope, depth, etc. and earthquake-induced characteristics including earthquake acceleration, earthquake occurrence time, Peak ground acceleration (PGA), etc. were included in the model. Earthquake input parameters were considered as input parameters and pipe strains and stresses were considered as output parameter. ANSYS finite element software has also been used to simulate the pipeline and produce training data. The results of finite element software were used as input and output parameters for training and validating artificial neural network. 753 models created using ANSYS and its input/output data divided into three parts to creat ANN model. 70% of the total data were used for training, 15% for validating and 15% for testing the ANN model. Results show that the proposed Method provides a very good agreement with the computational results of the ANSYS with accuracy of 96 percent.


Barghi Kh, Asoudeh, H. (1391). Fragility analysis of buried pipelines against the effects of earthquake waves. 9th International Congress of Civil Engineering (p. 8). Isfahan: Isfahan University of Technology.
Bavanpour, H., Khazaei, J., & Sharafi, H. (2011). Numerical study of hazards resulting from the occurrence of earthquake-induced liquefaction on the site and stability of transmission pipes (vital arteries). Second National Conference on Crisis Management (p. 7). Tehran: Tehran.
Hosseinpour, S. (1389). Investigation of earthquake safety measures for pipelines in Tehran. Third Conference on Resilience and Urban Management (p. 8). Khomeini: Islamic Azad University, Khomeini Branch.
Fruits, M., Ghafouripour, A., & Karimi, A. (2010). Impact of earthquake on buried pipelines. The First National Conference on Crisis Management, Earthquake and Vulnerability of Vital Places and Arteries (p. 8). Tehran: Ministry of Interior, Crisis Management Organization.
Mohammed S. El-Abbasy, Ahmed Senouci, Tarek Zayed, Farid Mirahadi, Laya Parvizsedghy, Artificial neural network models for predicting condition of offshore oil and gas pipelines, Automation in Construction, Volume 45, September 2014,
Alireza Farhidzadeh, Ehsan Dehghan-Niri, Zilan Zhong, Salvatore Salamone, Amjad Aref, Andre Filiatrault, Post-earthquake evaluation of pipelines rehabilitated with cured in place lining technology using acoustic emission, Construction and Building Materials, Volume 54, 15 March 2014,
Meimei Liu, Min Yang, Modeling the behavior of natural gas pipeline impacted by falling objects, Engineering Failure Analysis, Volume 42, July 2014,
Chunlei Zeng, Changchun Wu, Lili Zuo, Bin Zhang, Xingqiao Hu, Predicting energy consumption of multiproduct pipeline using artificial neural networks, Energy, Volume 66, 1 March 2014,
Asskar Janalizadeh Choobbasti, Hamidreza Tavakoli, Saman Soleimani Kutanaei, Modeling and optimization of a trench layer location around a pipeline using artificial neural networks and particle swarm optimization algorithm, Tunnelling and Underground Space Technology, Volume 40, February 2014,
Do Hyung Lee, Byeong Hwa Kim, Hacksoo Lee, Jung Sik Kong, Seismic behavior of a buried gas pipeline under earthquake excitations, Engineering Structures, Volume 31, Issue 5, May 2009,
XiaobenLiuaQianZhengabKaiWuaYueYangaZiqiZhaoaHongZhan, Development of a novel approach for strain demand prediction of pipes at fault crossings on the basis of multi-layer neural network driven by strain data, Engineering Structures,Volume 214, 1 July 2020, 110685
Mehdi Nikoo, & Panam Zarfam . (2012). Determining Confidence for Evaluation of Vulnerability In Reinforced Concrete Frames with Shear Wall. Journal of Basic and Applied Scientific Research, 7(2), 6605-6614.
K. Kimishima1 , Y. Maruyama2 and F. Yamazaki3,SPATIAL DISTRIBUTION OF DAMAGES TO BURIED PIPES FOLLOWING THE 2007 NIIGATA-KEN CHUETSU-OKI, JAPAN, EARTHQUAKE, Proceedings of the 9th U.S. National and 10th Canadian Conference on Earthquake Engineering, Paper No 857, July 25-29, 2010
Numerical analysis of factors affecting the liquefaction caused by earthquake on the construction of buried pipes, Hassan Sharafi, Payam Parsafar
Guidelines for the Seismic Design of Oil and Gas Pipeline Systems, Committee on Gas and Liquid Fuel Lifelines of the ASCE Technical Council on Lifeline Earthquake Engineering, ASCE, American Society of Civil Engineers, New York, NY, 978-0-87262-428-3 (ISBN-13).
Y. Zhang, W.G. Weng, Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference, Reliability Engineering & System Safety, Volume 203, 2020.
Thikra Dawood, Emad Elwakil, Hector Mayol Novoa, José Fernando Gárate Delgado,Artificial intelligence for the modeling of water pipes deterioration mechanisms, Automation in Construction, Volume 120, 2020.
Ayati, A., Ranginkaman, M., Bakhshipour, A., Haghighi, A. Transient Measurement Site Design in pipe networks using the Decision Table Method (DTM). Journal of Hydraulic Structures, 2019; 5(2): 32-48. doi: 10.22055/jhs.2019.29402.1107.
Dariane, A., Ashrafi Gol, M., Karami, F. Forecasting of rainfall using different input selection methods on climate signals for neural network inputs. Journal of Hydraulic Structures, 2019; 5(1): 42-59. doi: 10.22055/jhs.2019.29625.1113.
Alahdin, S., Ghafouri, H. Developing optimal operating reservoir rule-curves in drought periods. Journal of Hydraulic Structures, 2017; 3(2): 47-61. doi: 10.22055/jhs.2017.13490