The leak detection of water supply pipelines is significant for the protection of water resources. Acoustic detection is a common method used to investigate water supply pipeline leaks. Although some acoustic methods for the leak detection of the water supply pipeline have been developed experimentally, the theoretical investigation of said acoustic methods is still limited. Compared with aeroacoustics, the development of quantitative jet acoustic theory towards flow field calculations of liquid pipeline leak has not been reported, and the characteristics of the liquid flow field cannot be quantitatively transformed into the acoustic model. In this paper, the liquid pipeline leak is combined with piston acoustics, and the acoustic model for the leak detection of the water supply pipeline is quantitatively studied for the first time. The acoustic pressure value can be directly calculated using pipe and liquid parameters, and the validity of the model can be verified experimentally. Based on theoretical and experimental investigations, it is found that the leak sound pressure increases significantly with increasing pipeline pressure and decreases with increasing detection distance. The material composition of the pipes has little influence on the leak sound pressure. The theoretical values based on the proposed acoustic model and the experimental values agree well, where the maximum difference between them is 8.5% and the average difference is 2.6%. This study presents a foundation for the development of acoustic leak detection technology of the liquid pipeline.

The degradation and failure of the urban water supply network may lead to serious safety hazards, including pipe breaks, water supply interruptions, water resource losses, and contaminant intrusions. The risk evaluation of water supply pipeline failure in a distribution network is a challenging task, because most of the available data cannot fully reflect pipeline failure events and many of the mechanisms still cannot be fully understood. Therefore, a predictive model is urgently needed to assess pipeline failure risk based on available data. In this paper, based on the traditional risk assessment theory, seven main factors affecting pipeline failure are selected and scored, and then a pipeline failure model is established by using the particle swarm optimization (PSO) neural network. The model uses the neural network training of historical data to evaluate the failure of the water supply pipeline, and the PSO is used to optimize the neural network to effectively improve the training time and accuracy. The model error and correlation coefficient are 0.003 and 0.987, respectively. The proposed model can be used as a powerful support tool to assist infrastructure managers and pipeline maintainers in their plans and decision-making.