PREDICTION OF PIPE WRINKLING USING ARTIFICIAL NEURAL NETWORK

As the demand for oil and gas resources increases pipeline construction pushes further into the geologically unstable Arctic and sub-Arctic regions. Consequently, these buried pipelines suffer much harsh environmental and complex loading conditions. In addition, higher strength and larger size pipes with higher operation pressure are used gradually. These severe and unknown conditions increase the risk of pipeline failure, especially, local buckling (wrinkling) failure. The wrinkling failure and sequential pipe fracture can cause enormous cost loss as well as high risk in safety and environmental impact. In the past, to prevent the buried pipelines from buckling failure, the pipeline maintenance was processed by periodical inspections and excavations in the field. The whole procedure is expansive and time consuming, and has no active warning system for possible failures between the inspection periods. Therefore, to overcome these problems, an automatic warning system for monitoring pipeline buckling is developed. A damage detection model (DDM) with artificial neural network (ANN) is a kern of the warning system and discussed in this paper. The proposed DDM will allow engineers to diagnose the pipe condition reliably and continuously without interrupt the normal operation of buried pipelines. The proposed DDM successfully identifies the distributed strain patterns in local characteristics as well as global trend. Some significant findings in the ANN model working with distributed strain patterns of the pipes are discussed, and a guideline of applying the DDM to the field pipe is also presented in this paper.