Data Compression and Damage Evaluation of Underground Pipeline With Musicalized Sonar GMM

Abstract

The underground cement pipeline network is critical in urban infrastructure, such as sewage drainage and fluid transportation. Pipelines are deeply buried, so the damage is difficult to be detected. It is of great significant to monitor the crack of pipeline in real time. In this research, a data compression and damage evaluation method of underground pipeline using musicalized sonar Gaussian mixed model (GMM) was proposed. First, the sonar echo signal was sampled and discretized into four musicalized indicators by musicalized model. Then, the distribution of multiparameters was analyzed by GMM. The Frechet similarity was constructed to compare the difference between each working condition and baseline condition, so that the damage state could be identified. The engineering experiment shown that this method could greatly compress the monitoring data and evaluate four crack levels.