Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.
Key Laboratory for Geo-Hazards in Loess Area, MLR, Xi'an, 710054, Shaanxi, China.
Sci Rep. 2021 Jan 12;11(1):816. doi: 10.1038/s41598-020-78702-7.
Collapsibility determination in loess area is expensive, and it also requires a large amount of experimentation. This paper aims to find the association rules between physical parameters and collapsibility of the loess in Xining through the method of data mining, so to help researchers predict the collapsibility of loess. Related physical parameters of loess collapsibility, collected from 1039 samples, involve 13 potential influence factors. According to Grey Relational Analysis, the key influence factors that lead to collapsing are identified from these potential influence factors. Subsequently, take the key influence factors, δs (coefficient of collapsibility) and δzs (coefficient of collapsibility under overburden pressure) as input items, and use the Apriori algorithm to find multiple association rules between them. Then, through analysing the results of association rules between these key influence factors and collapsibility, the evaluation criteria for collapsibility in this area is proposed, which can be used to simplify the workload of determining collapsibility. Finally, based on these research results, recommendations for projects construction were made to ensure the safety of construction in the area.
黄土地区的湿陷性确定既昂贵又需要大量的实验。本文旨在通过数据挖掘的方法，找出西宁黄土物理参数与湿陷性之间的关联规则，以帮助研究人员预测黄土的湿陷性。从 1039 个样本中收集了与黄土湿陷性相关的物理参数，涉及 13 个潜在影响因素。根据灰色关联分析，从这些潜在影响因素中确定导致湿陷的关键影响因素。随后，以关键影响因素δs（湿陷系数）和δzs（上覆压力下湿陷系数）为输入项，采用 Apriori 算法找出它们之间的多个关联规则。然后，通过分析这些关键影响因素与湿陷性之间的关联规则的结果，提出了该地区湿陷性的评价标准，可用于简化湿陷性确定的工作量。最后，基于这些研究结果，为项目建设提出了建议，以确保该地区施工的安全。
