Sensitivity of High Fill Slope Stability Factors under Seismic Conditions
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SOIL MECHANICS SENSITIVITY OF HIGH FILL SLOPE STABILITY FACTORS UNDER SEISMIC CONDITIONS
UDC 624.131.537:624.131.55 Huang Anping and Ye Shuaihua* School of Civil Engineering; Key Laboratory of Disaster Mitigation in Civil Engineering of Gansu Province; Testing Center of Geotechnical Engineering and Foundation, Lanzhou University of Technology, Lanzhou, China, *Corresponding author E-mail: [email protected].
An evaluation method based on vector similarity is introduced to determine the sensitivity of stability factors of high fill slope under seismic action in this paper. The Euclidean similarity and cosine similarity of slope stability factors are calculated. In addition, grey relational analysis method is used to calculate the case, and the three calculation results are compared. The results show that the sensitivity ranking results of high fill slope stability factors obtained by cosine similarity and grey correlation analysis are consistent, and slightly different from those obtained by Euclidean similarity. This indicates that cosine similarity method is more suitable for sensitivity evaluation of high fill slope stability factors than Euclidean similarity method. 1. Introduction High fill slope is a main form of slope engineering. Seismic stability analysis and evaluation of permanent high fill slope is an important part of slope engineering [1]. The stability of high fill slope determines whether the project itself is reliable and is also closely related to human life and property safety. High fill slope instability and failure are caused by many factors [2]. According to Peng et al. [3]'s statistics on 14544 loess landslides from the Loess Plateau of China, the main factors affecting slope stability include soil properties, regional tectonics, geomorphological structure, rainfall, seismic activity and human activity. Due to the variety of slope types, there are many factors affecting the stability of slopes [4], and the specific impact degree of each factor on slope stability is also different. Therefore, it is of great significance to evaluate the sensitivity of slope stability factors [5]. At present, there are many methods for sensitivity analysis of slope stability factors, such as neural network analysis method [6], fuzzy math integrated evaluation method [7], hierarchical analysis method [8], grey correlation analysis method [9], and orthogonal test method [10]. However, due to the complexity of the actual slope, the slope stability has great uncertainty and randomness [11]. These evaluation methods have been widely used in the field of slope stability evaluation and have achieved good results. However, these algorithms consist many steps and complicated processing processes, which are not easy for decision makers to quickly understand and master. In addition, the sensitivity evaluation results of slope stability factors obtained by different algorithms may not be completely consistent. Therefore, it is necessary to use multiple ways to compare and analyze the sensitivity of slope stability factors
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