A Data-Intensive FLAC3D Computation Model: Application of Geospatial Big Data to Predict Mining Induced Subsidence
| Autor: | Ya-Qiang Gong, Guang-li Guo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Big data 0211 other engineering and technologies Borehole Underground mining (hard rock) Context (language use) Dimensional modeling Subsidence 02 engineering and technology 021001 nanoscience & nanotechnology computer.software_genre Computer Science Applications Software Modeling and Simulation Groundwater-related subsidence Data mining 0210 nano-technology business computer 021101 geological & geomatics engineering |
| Zdroj: | Computer Modeling in Engineering & Sciences. 119:395-408 |
| ISSN: | 1526-1506 |
| DOI: | 10.32604/cmes.2019.03686 |
| Popis: | Big data are widely used in various fields. However, the application of big data is rare in the study of predicting surface subsidence caused by underground mining. Traditional research has the problem of oversimplifying geological mining conditions. In the context of geospatial big data, a data-intensive FLAC3D (Fast Lagrangian Analysis of a Continua in 3 Dimensions) model is proposed in this paper based on CAD software (Rhinoceros) and borehole logs. The data-intensive FLAC3D model was developed using Rhinoceros software to visualize borehole logs in three dimensions. In the three dimensional modeling process, based on the characteristics of the FLAC3D model and borehole logs, we developed a method to handle geospatial big data and were able to make full use of borehole logs. The effectiveness of the proposed method was verified by comparing the results of the traditional method, proposed method and 70 observation points on the surface. This study shows that the proposed method has obvious advantages over the traditional prediction results. Compared with the traditional prediction results, the relative error of the surface maximum subsidence predicted by the proposed method decreased by 93.7% and the standard deviation of the prediction results (which was 70 points) decreased by 39.4%, on average. The proposed method is the first method to geospatial big data into the mining subsidence prediction research, which is of great significance for improving the accuracy of mining subsidence predictions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|