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The fine particles around a mining area are easy to transport under the climatic and hydrological actions such as rainfall, that causes the change in the permeability of accumulated slag and increases the hazard probability of slag debris flow. In this study, eight experiments were designed to discuss the influence of fine particle migration on the permeability characteristics and clogging of slag accumulation in different graded particles and different dry densities. The results of experiments with coarse slags of five different particle sizes show that the ratio ranging from four to six in the coarse slag size and fine size caused a significant clogging phenomenon. It is confirmed that the shape of the particles is one of the factors affecting the clogging of coarse soil besides the coarse and fine particle size, and the clogging assessment criterion for slag and the corresponding clogging patterns based on the slag shape characteristics are given. And through three kinds of different dry density-graded slag, three clogging experiments were completed to verify the clogging standard and clogging particle size. The experimental results show that the clogging particle size obtained by the clogging criteria can effectively reduce the permeability of slag accumulation, and it is considered that the equivalent particle size and particle shape characteristics are the main factors affecting the clogging performance of accumulation, while the dry density of deposits has no significant influence on it. [ABSTRACT FROM AUTHOR]

To explore the influence of sample preparation methods on the permeability and microstructure of remolded loess, remolded loess collected from Heifangtai was taken as the research object. A total of 40 sets of falling-head permeability tests were conducted using two commonly used sample preparation methods, and five different dry density and four initial water content conditions. Additionally, the electrical conductivity of the leachate and the microscopic structure of the samples were analyzed. The results demonstrate that compared to the transfer wetting method, the homogeneity of samples prepared using the pre-wetting method is inferior. This difference is particularly evident when the initial water content is high. Due to the long duration of the permeability test, the pore structure is prone to change, resulting in relatively higher permeability coefficients. Moreover, the total dissolved solids (TDS) of the leachate exhibit a significant decrease with increasing seepage time, indicating the loss of soluble salts. Microscopic structural analysis reveals that samples prepared using the pre-wetting method exhibit a greater number of large pores and aggregates, which are intrinsic factors contributing to the observed differences in permeability between the two sample preparation methods. Furthermore, it should be noted that the impact of the sample preparation method on the permeability of remolded loess is more significant when the dry density is relatively low (specifically, less than 1.45 g/cm3). Conversely, when the dry density is higher, the influence becomes less pronounced. [ABSTRACT FROM AUTHOR]

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas. [ABSTRACT FROM AUTHOR]