Ali Haibat, Choi Jae-Ho
Department of Civil Engineering, Dong-A University, 550 Bungil 37, Nakdong-Daero, Saha-Gu, Busan 49315, South Korea.
Data Brief. 2021 Jan 9;34:106740. doi: 10.1016/j.dib.2021.106740. eCollection 2021 Feb.
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.
本文提供了不同版本的小规模物理塌陷坑模型的模拟数据集,这些数据集对于理解塌陷坑形成速率至关重要。这些物理模型用于实验中,以监测由于地下管道泄漏导致的地面沉降或塌陷。所考虑的因素包括地下土壤剖面、水流模式以及管道中的泄漏位置。实验结果和统计分析表明,尽管其他因素也会导致沉降,但地下土壤地层条件主导着塌陷坑的发生机制。结果还表明,由砂质粘土、石灰岩和基岩(SC-LS-BR)地层组成的地下土壤主导着塌陷坑机制。数据以有用的结构进行了组织和格式化。具体而言,数据集以表格形式呈现,以说明在各种条件下不同土壤剖面中的沉降情况。然后,该分析用于预测不同条件下的塌陷坑风险水平。在制定塌陷坑风险指数(SRI)和识别塌陷坑风险区域时,可以考虑所制定的数据集和结果。