Mining subsidence monitoring model based on BPM-EKTF and TLS and its application in building mining damage assessment

• Published in: Environmental earth sciences Vol. 80; no. 11
• Main Authors: Li, JingYu, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Biogeosciences; Building damage; Coal mines; Coal mining; Damage assessment; Data acquisition; Deformation; Deformation effects; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geology; Hydrology/Water Resources; Lasers; Mining; Monitoring; Monitoring methods; Original Article; Prediction models; Subsidence; Terrestrial Pollution; Time functions; Mining damage assessment; Boltzmann function; Exponent Knothe time function; Wolf pack algorithm; Terrestrial laser scanner; Mining subsidence monitoring
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-021-09704-5

In mining subsidence monitoring, “discrete point deformation monitoring and mining subsidence prediction model” is often used. The key to mining subsidence monitoring is to choose a convenient, economical, accurate, and reliable deformation monitoring method. In this study, the terrestrial laser scanner (TLS) with convenient, high efficiency, and high precision was used as the data acquisition method. And the Boltzmann function prediction method-exponent Knothe time function mining subsidence prediction model with high simulation degree for the deformation of rock strata above the mining affected area was constructed to calculate the surface deformation. Taking the surrounding area of South 1312 (1) working face of Gubei Coal Mine in Huainan, China as the application area, first, the model parameters are obtained by the wolf pack algorithm according to the TLS scanning point cloud data, followed by predicting the subsidence and horizontal displacement of the surrounding area. Finally, the building mining damage assessment is conducted according to the deformation of the surrounding area of the working face. The analysis results show that the mining subsidence monitoring method proposed in this paper can obtain the surface deformation in a large area affected by mining after observing a small area. The surface deformation obtained by this method is consistent with the surface deformation obtained by leveling observation. Moreover, the predicted effect of this method is better than that of the mining subsidence monitoring method using PIM-KTF model combined with TLS, and it has certain robustness to the geological and mining condition errors. The results of this work can provide a reference for predicting mining subsidence influence scope, deformation size, and mining damage assessment of mining buildings.