Pit Collapse Risk Fusion Early-Warning Method Based on Machine Learning and Improved Cloud Dempster–Shafer
Source abstract
Abstract
Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seismology and Earthquake Studies · Evaluation and Optimization Models
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang 330013, China
School of Civil and Architecture Engineering, East China University of Technology, Nanchang 330013, China
Funding
Natural Science Foundation of China · Natural Science Foundation of Jiangxi Province · China Postdoctoral Science Foundation · Jiangxi Province Graduate Innovation Special Fund Project · Innovation Leading Talent Project of Jiangxi Province
References (40)
Show source-provided references
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