NS013-0002
Detection of an Underground Non-metallic Pipeline Based on a Strong Magnetic Tracer

Wednesday, 16 December 2020
Poster
Wenfeng Guo, Yonghui Zhao and Wenda Bi, Tongji University, School of Ocean & Earth Science, Shanghai, China
Abstract:
In recent years, the utilization of urban underground space has become a popular trend; however, inaccurate information gathered from underground pipelines has led to frequent engineering accidents. Therefore, these pipelines are becoming a major source of risk in urban societies. With the development of trenchless construction technology, the pipelines tend to be deeply buried, small in diameter and non-metallic, factors which pose a more serious challenge for detection. Traditional detection methods such as electromagnetic imaging, resistivity and acoustic vibration are not applicable for this type of pipeline. Thus, we devised a technique for accurately detecting underground pipelines. Using inertial positioning technology and the borehole magnetic gradient method, we proposed an approach for detecting deeply buried non-metallic pipeline based on strong magnetic tracing. Using an auxiliary push device, we placed a strong magnetic probe into the underground pipeline in order to locate it via the distribution characteristics of the magnetic anomalies observed on the surface or near the boreholes. Taking the bar magnet as an example, we created a forward simulation of the magnetic anomaly. Then, we were able to analyze the anomalous distribution characteristics of magnets by studying different magnetic moment, lengths, depths and dip angles. In the field, we were able to choose the appropriate magnetic strength at the appropriate magnetic moment through forward calculations. Pipelines can be accurately located by observing the magnetic field intensity or gradient within the boreholes. The proposed method provides a new solution for detecting the deeply buried non-metallic underground pipelines. Moreover, it is not restricted by the material and depth of the pipeline and has been found to perform effectively in a broad range of circumstances, giving it many applications for future study.