Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles
This paper provides simulated datasets for different versions of small-scale physical sinkhole models that are essential to understand the sinkhole formation rate. These physical models were used in experiments to monitor ground settlement or collapse due to leakage from an underground pipeline. The...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340921000263 |
| _version_ | 1818670011130052608 |
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| author | Haibat Ali Jae-ho Choi |
| author_facet | Haibat Ali Jae-ho Choi |
| author_sort | Haibat Ali |
| collection | DOAJ |
| description | This paper provides simulated datasets for different versions of small-scale physical sinkhole models that are essential to understand the sinkhole formation rate. These physical models were used in experiments to monitor ground settlement or collapse due to leakage from an underground pipeline. The factors under consideration were the subsurface soil profile, pattern of water flow, and leakage position in the pipeline. The experimental results and statistical analysis showed that the subsurface soil strata conditions dominated the sinkhole occurrence mechanism, although other factors also contributed to the settlement. The results also showed that the subsurface soil comprising strata sandy clay, limestone, and bedrock (SC-LS-BR) dominates the sinkhole mechanism. The data are organized and formated in a useful structure. Specifically, the dataset is presented in terms of tables to illustrate the settlements in different soil profiles under various conditions. This analysis was then used to predict the sinkhole risk level under different conditions. The formulated dataset and the results can be considered in developing a sinkhole risk index (SRI) and identifying sinkhole risk areas. |
| first_indexed | 2024-12-17T07:01:19Z |
| format | Article |
| id | doaj.art-b4171ee4407641218ebc7ae3146ad5d9 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-17T07:01:19Z |
| publishDate | 2021-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-b4171ee4407641218ebc7ae3146ad5d92022-12-21T21:59:16ZengElsevierData in Brief2352-34092021-02-0134106740Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profilesHaibat Ali0Jae-ho Choi1Department of Civil Engineering, Dong-A University, 550 Bungil 37, Nakdong-Daero, Saha-Gu, Busan 49315, South KoreaCorresponding author.; Department of Civil Engineering, Dong-A University, 550 Bungil 37, Nakdong-Daero, Saha-Gu, Busan 49315, South KoreaThis paper provides simulated datasets for different versions of small-scale physical sinkhole models that are essential to understand the sinkhole formation rate. These physical models were used in experiments to monitor ground settlement or collapse due to leakage from an underground pipeline. The factors under consideration were the subsurface soil profile, pattern of water flow, and leakage position in the pipeline. The experimental results and statistical analysis showed that the subsurface soil strata conditions dominated the sinkhole occurrence mechanism, although other factors also contributed to the settlement. The results also showed that the subsurface soil comprising strata sandy clay, limestone, and bedrock (SC-LS-BR) dominates the sinkhole mechanism. The data are organized and formated in a useful structure. Specifically, the dataset is presented in terms of tables to illustrate the settlements in different soil profiles under various conditions. This analysis was then used to predict the sinkhole risk level under different conditions. The formulated dataset and the results can be considered in developing a sinkhole risk index (SRI) and identifying sinkhole risk areas.http://www.sciencedirect.com/science/article/pii/S2352340921000263SinkholeRisk predictionSewer pipelinePipeline leakageSoil profile |
| spellingShingle | Haibat Ali Jae-ho Choi Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles Data in Brief Sinkhole Risk prediction Sewer pipeline Pipeline leakage Soil profile |
| title | Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| title_full | Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| title_fullStr | Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| title_full_unstemmed | Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| title_short | Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| title_sort | data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles |
| topic | Sinkhole Risk prediction Sewer pipeline Pipeline leakage Soil profile |
| url | http://www.sciencedirect.com/science/article/pii/S2352340921000263 |
| work_keys_str_mv | AT haibatali dataonmanmadesinkholesduetoleakageinundergroundpipelinesindifferentsubsurfacesoilprofiles AT jaehochoi dataonmanmadesinkholesduetoleakageinundergroundpipelinesindifferentsubsurfacesoilprofiles |