Risk Prediction of Sinkhole Occurrence for Different Subsurface Soil Profiles due to Leakage from Underground Sewer and Water Pipelines

Author:

Ali HaibatORCID,Choi Jae-hoORCID

Abstract

A sinkhole is a ground surface depression that may occur with or without any indications on the surface and often pose danger to both properties and people. Leakage from underground pipe mains in urban areas may cause sudden ground subsidence or sinkholes. For a long time, researchers have been working on the hazard and risk assessment of sinkhole formation, especially natural sinkholes. However, much less work has been done on risk prediction and the mechanism of manmade sinkholes. In this study, different versions of small-scale sinkhole physical models were used in experiments to monitor ground surface settlement or collapse due to leakage from an underground pipeline. The factors under consideration were the type of subsurface soil profile, type of water flow, and leakage position in the pipeline. The ultimate goal was to use this information to predict the risk of sinkhole occurrence due to leakage from sewer or water pipelines under different subsurface soil conditions. The experimental results and statistical analysis showed that the subsurface soil strata conditions dominated the mechanism of sinkhole occurrence, although other factors also have contributed to the settlement. Then, this analysis was used to predict the sinkhole risk level under different conditions. The development of a reliable sinkhole risk prediction system can potentially minimize the risk to human lives and infrastructure. These findings can be applied to the development of a sinkhole risk index (SRI) that considers various other factors influencing sinkhole occurrence.

Funder

National Research Foundation of Korea

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development

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1. Model experimental study on the mechanism of collapse induced by leakage of underground pipeline;Scientific Reports;2024-07-31

2. Radar Interferometry for Sustainable Groundwater Use: Detecting Subsidence and Sinkholes in Kabodarahang Plain;Water;2024-07-12

3. Pipeline Leak Detection System Using Machine Learning;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

4. Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach;Sustainability;2024-06-20

5. Evaluation of sand subgrade seepage erosion caused by buried pipeline leakage;Proceedings of the Institution of Civil Engineers - Geotechnical Engineering;2024-06-19

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