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

Haibat Ali and Jae-ho Choi
Additional contact information
Haibat Ali: Civil Engineering, Dong-A University, Busan, P4401-1, 550 Bungil 37, Nakdong-Dero, Saha-Gu 49315, Korea
Jae-ho Choi: Civil Engineering, Dong-A University, Busan, P4401-1, 550 Bungil 37, Nakdong-Dero, Saha-Gu 49315, Korea

Sustainability, 2019, vol. 12, issue 1, 1-16

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.

Keywords: sinkhole; risk prediction; sewer pipeline; pipeline leakage; soil profile (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/1/310/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/1/310/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2019:i:1:p:310-:d:303556

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().