Comprehensive safety factor of roof in goaf underdeep high stress

The safety factor of roof under deep high stress is a quantitative index for evaluating roof stability.Based on the failure mode of surrounding rock of stope roof,the mechanics model of goaf roof is constructed,and the internal force of roof is deduced by the theory of hingeless arch.The calculation method of roof safety factor(K)under the environment of deep mining is proposed in view of compression failure and shear failure of roof.The calculation formulas of shear safety factor(K1),compression safety factor(K2)and comprehensive safety factor(K)of roof are given.The influence of stope span and roof thickness on roof stability is considered in this paper.The results show that when the roof thickness remains constant,the roof safety factor decreases with the increasing of the stope span;when the stope span remains constant,the roof safety factor increases with the increasing of the roof thickness.The deep mining example shows that when the stope span is 30 m and the roof thickness is 10 m,the roof comprehensive safety factor is 1.12,which indicates the roof is in a stable state.

Dividing the Expropriated Areas Based on Multi-Factor Comprehensive Evaluation—A Case Study of Gongcheng Yao Autonomous County

In recent years, with the rapid development of urbanization in China, land acquisition work is particularly important. The division of expropriated area is the primary factor to be considered when evaluating the comprehensive land price of expropriated area. Reasonable division can help to improve the scientific and applicability of the comprehensive land price of the region. How to maximize the protection of farmers’ rights and interests by the division of land requisition is an urgent problem to be solved. Taking Gongcheng Yao Autonomous County as an example, this study adopted the multi-factor comprehensive evaluation method to evaluate the division of land requisition in this area, and carried out the corresponding spatial analysis and data processing based on GIS technology. Gongcheng Yao Autonomous County was divided into levy areas, and the Delphi method was used to screen the impact factors and determine the weight of the levy areas. Reasonable division of land requisition area can provide references for land requisition area, make it more scientific and reasonable, and protect the rights and interests of farmers.

An Exploratory Study of E-Business Success Factors

E-business success factors are Important for traditional enterprises to implement e-business. This topic is attracting more and more researchers to study. This paper makes an exploratory study on the factors influencing e-business success. Firstly, based on the literature review, 52 factors are suggested. Secondly, two rounds of survey with Delphi method are conducted. Qualitative and quantitative analysis are used to identify 57 factors. This is the foundation of empirical study.

Impact of environmental factors on water quality at multiple spatial scales and its spatial variation in Huai River Basin, China

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators,namely, ammonia nitrogen(NH_4^+-N), permanganate index(COD_(Mn)), total phosphorus(TP) and total nitrogen(TN) at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors(land use pattern) and natural factors(river density, elevation and precipitation) to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with COD_(Mn), NH_4^+-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed "small-scale" effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.

A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation

Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.

Deep learning of DEM image texture for landform classification in the Shandong area,China

Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient.