Experimental research on coal seam similar material proportion and its application

In this paper, the optimization design of the low strength mechanical test and orthogonal test have been analyzed in order to simulate the mechanical properties of thick and extra-thick coal seam accurately in a similar material simulation test. The results show that the specimen can reach a wider range of strength when cement has been used compared to that of gypsum, suggesting that cement is more suitable for making coal seam in similar material simulation tests. The uniaxial compressive strength is more sensitive to cement than coal or sand. The proportion of coal and sand do not play a decisive role in uniaxial compressive strength. The uniaxial compressive strength and specimen density decrease as the mass percent of coal and aggregate–binder ratio rise. There is a positive correlation between uniaxial compressive strength and density. The No. 5 proportion(cement: sand: water: activated carbon: coal = 6:6:7:1.1:79.9)was chosen to be used in the similar material simulation test of steeply dipping and extra-thick coal seam with a density of 0.913 g/cm^3 and an uniaxial compressive strength of 0.076 MPa which are in accordance with the similarity theory. The phenomenon of overburden stratum movement, fracture development and floor pressure relief were obtained during the similar material simulation test by using the proportion.

Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.