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基于改进卷积神经网络的边坡稳定性及工程地质综合评价模型设计。

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network.

机构信息

No. 3 Geological Brigade of Hebei Geology and Minernal Exploration Bureau, Zhangjiakou, Hebei, China.

出版信息

Comput Intell Neurosci. 2022 May 9;2022:1639311. doi: 10.1155/2022/1639311. eCollection 2022.

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.

摘要

边坡的地质力学、岩土工程特性和水文地质条件复杂多变,因此其稳定性评估是一个复杂的系统;其传统的工程地质评估不考虑系统的对立性、性能指标的不确定性和指标分类的模糊性,容易因模糊性而扭曲结果。改进的卷积神经网络(CNN)在分析具有随机性和模糊性的问题方面具有突出的优势。它可以对具有精确值、区间值和定性判断值的边坡评估指标进行统一的数值处理,使传统的定性描述转化为定量计算。因此,本文在总结和分析前人研究工作的基础上,阐述了边坡稳定性和工程地质综合评价模型的研究现状和意义;详细阐述了改进的 CNN 的发展背景、现状和未来挑战;介绍了模型结构、卷积层设计和数据流向优化的方法和原理;进行了评价指标体系的建立和指标权重的确定;建立了边坡稳定性的数学评价模型;基于改进的 CNN 进行了边坡稳定性的评价模块设计;分析了各因素对综合工程地质特征的重要性;讨论了综合单元工程地质特征的评价值的确定;基于改进的 CNN 进行了边坡工程地质的评价模块设计;最后进行了工程应用及结果分析。研究结果表明,改进的 CNN 可以根据边坡的实际情况选择一些普遍和客观的因素,包括地形、地层岩性、地质构造、大气降水、地下水、工程活动等,设置因素集和判断集,进行模糊推理。综合评价模型可以根据一定的原则和标准,运用适当的数学方法判断边坡稳定性和工程地质的利弊,并对结果进行分级,识别出最重要的地质问题。本文的研究结果为进一步研究基于改进的 CNN 的边坡稳定性和工程地质综合评价模型的设计提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e573/9110133/9ee0f6db4c7d/CIN2022-1639311.001.jpg

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