As the demand for oil and gas resources increases pipeline construction pushes further into the Arctic and sub-Arctic regions. Consequently, these buried pipelines suffer much harsh environmental and complex loading conditions. Moreover, to increase the transporting efficiency, larger size pipes and higher operation pressure are used more frequently. Therefore, these conditions increase the risk of pipeline failure, especially local buckling (wrinkling) failure. To prevent the buried pipes from buckling failure, an automatic warning system for continuously monitoring pipeline buckling is needed. A method to achieve this purpose was studied and presented here.;In the second phase, finite element (FE) models were developed and calibrated by the results of full-scale pipe buckling tests and then used to obtain the patterns (or signatures) of the strain distributions along pipes under combined loading. Based on the results of the parametric study, a SHM system is proposed. The system integrated the distributed strain sensing system (such as Brillouin scattering fiber-optic sensory system), numerical models (FE models), and damage detection models (artificial neural network (ANN)) into a reliable, real-time monitoring system. Thereby, a methodology of health monitoring of the buried pipe buckling was carried out.;The last phase of the research focuses on the development of the damage detection models (DDM) in the SHM system. The effects of different parameters on the strain distribution patterns were studied by using a total of 74 FE models. The framework of the damage detection models was achieved mainly by four trained ANN protocols. The proposed damage detection model provides an accuracy of 90% in evaluating the health of the buried pipes during buckling and can reliably detect the onset of pipe wrinkling before the maximum strains accumulated on the monitored pipe reach 65% of the critical strain.;The research program is divided into three phases. In the first phase, a literature review has concluded that it is feasible to detect pipe wrinkling by monitoring the signatures of distributed strains and curvatures along a buried pipe and by using the distributed strain sensory systems in a structural health monitoring (SHM) system. Subsequently, the test results and the field strain distribution data were used to verify the viability of using distributed strain sensors for early detecting wrinkles in buried pipes.
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