Third-party threats, such as construction activities and man-made sabotage, have become the main cause of pipeline accidents in recent years. This article proposes a surveillance system for protecting the buried municipal pipelines from third-party damage based on distributed fiber optic sensing and convolutional neural network (CNN). Due to the ability of detecting very small perturbation, the phase-sensitive optical time-domain reflectometry (φ-OTDR) is employed for distributed vibration measurements along the pipelines. A two-layer classifier based on CNN is developed: one layer is used to discriminate the third-party activities from the environmental disturbance; the other is to determine the specific type of the third-party events. Meanwhile, a time-space matrix is introduced to reduce the false alarm and correct possible errors by taking into account the continuity of the signals in time and space. Field tests are carried out to validate the effectiveness of the proposed surveillance system. The recognition results show that the CNN-based classifiers achieve the accuracy of over 97%, which is 14.8% higher than that of the traditional feature-based machine learning method using random forest (RF) algorithm. It also indicates that the time-space matrix can dramatically reduce the false alarm and enhance the recognition accuracy.
Traffic loads have an important impact on long-term performance of buried urban pipelines due to generation of cyclic stress and fatigue damage. As a key task for pipeline fatigue assessment, the investigation of traffic-induced pipe stress and fatigue load spectrums is always a big challenge. Focusing on the metal pipelines buried under urban intersection, an integrated framework is proposed for fatigue assessment of the pipelines under traffic loads using video monitoring data acquired by the widespread surveillance cameras in cities. The computer vision algorithms and a quarter vehicle model are first employed to identify traffic loads. Analytical pipe-soil models are then developed to calculate pipeline stress and establish the traffic-induced fatigue load spectrums of buried pipelines, based on which the Monte-Carlo simulations are finally conducted to evaluate cumulative fatigue damage and fatigue reliability. A case study on a DN500 steel pipeline buried under an urban intersection demonstrates and validates the fatigue assessment procedure. The results can provide a basis for decision support regarding maintenance and replacement of urban pipelines.
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