Dataset Information

Combining subsidence theory and slope stability analysis method for building damage assessment in mountainous mining subsidence regions.

ABSTRACT: Ground subsidence and surface cracks caused by coal mining are typical man-made geological hazards that can severely damage the ecological environment and buildings. In China, within the theme of sustained and stable development, accurate assessment of mining-related building damage is paramount in order to address the contradiction between coal mining enterprises and building owners. Previous research in China focused mainly on the mining areas of plains, and only a few studies have considered building damage caused by intensive mining in mountainous areas. First, based on field investigation, this study located ground surface cracks and assessed the damage to buildings in the village of Nanyetou in Shanxi Province (China) attributable to the exploitation of the 15110 working face of the Baiyangling coal mine. Second, based on the mining subsidence law and boundary angle, the surface influenced boundary caused by underground mining was determined. However, as the existing subsidence theory cannot adequately explain the phenomenon of building damage, the damage was investigated from the perspective of slope stability analysis, and the slope safety factor before and after working face mining were calculated using the Janbu method. The analytical results showed that slope instability due to a decrease of the safety factor because of the coal mining activity was the principal reason for damage to the village buildings, a finding that was confirmed by field survey and InSAR monitoring displacement. The results of this study could provide guidance and reference for the assessment of building damage caused by underground mining in mountain areas.

SUBMITTER: Diao X

PROVIDER: S-EPMC6364875 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Combining subsidence theory and slope stability analysis method for building damage assessment in mountainous mining subsidence regions.

Diao Xinpeng X Wu Kan K Zhou Dawei D Wang Jinyun J Duan Zhixin Z Yu Zixiang Z

PloS one 20190206 2

Ground subsidence and surface cracks caused by coal mining are typical man-made geological hazards that can severely damage the ecological environment and buildings. In China, within the theme of sustained and stable development, accurate assessment of mining-related building damage is paramount in order to address the contradiction between coal mining enterprises and building owners. Previous research in China focused mainly on the mining areas of plains, and only a few studies have considered ...[more]

PMID: 30726213

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Project description:Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Dataset Information

Combining subsidence theory and slope stability analysis method for building damage assessment in mountainous mining subsidence regions.

Publications

Combining subsidence theory and slope stability analysis method for building damage assessment in mountainous mining subsidence regions.

Similar Datasets

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