Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network. (9th May 2022)

• Record Type: Journal Article
• Authors: Liu, Yuedong; Yan, Dongdong; Zheng, Kexiong
• Other Names: Sun Gengxin Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-05-09
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/1639311
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The geological mechanics, geotechnical characteristics, and hydrogeological conditions of slopes are complex and changeable, so their stability assessment is a complicated system; their traditional engineering geological assessment does not consider the opposition of the system, the uncertainty of performance indicators, and the ambiguity of index classification, being easy to distort results due to the ambiguity. Improved convolutional neural network (CNN) has outstanding advantages in analyzing problems with randomness and fuzziness. It can perform unified numerical processing on slope assessment indicators with precise values, interval values, and qualitative judgment values, making the traditional qualitative description is transformed into quantitative calculation. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the comprehensive assessment model for slope stability and engineering geology; elaborated the development background, current status, and future challenges of the improved CNN; introduced the methods and principles of the model structure, convolutional layer design, and data flow optimization of the improved CNN; performed the assessment index system establishment and index weight determination; established the mathematical assessment model for slope stability; conducted the assessment module design for slope stability based on the improved CNN; analysed the importance of individual factors to the comprehensive engineering geological characteristics; discussed the determination of assessment value of comprehensive unit engineering geological characteristics; explored the assessment module design for slope engineering geology based on the improved CNN; and finally carried out an engineering application and its result analysis. The study results show that the improved CNN can select some universal and objective factors according to the actual conditions of slopes, including topography, stratum lithology, geological structure, atmospheric rainfall, groundwater, engineering activities, setting up factor sets and judgment sets, and making fuzzy inferences. The comprehensive assessment model can use appropriate mathematical methods to judge the pros and cons of slope's stability and engineering geology according to certain principles and standards, and grade the results and identify the most important geological problems. The results of this paper provide a reference for further researches on the design of a comprehensive assessment model for slope stability and engineering geology based on the improved CNN.