精准掌握软弱地基的地质信息资料是确定振冲碎石桩施工工艺和控制成桩质量的重要依据。现有地质勘探技术确定地层地质信息的方法存在较大的随机性和离散性,不能获取所有加固区域的地质条件。为了克服传统方法存在的缺陷,依托拉哇水电站振冲碎石桩施工过程实时监控系统采集到的大量桩成孔过程中与地层分类属性相关的数据,通过对大数据进行清洗,选取与地层分类属性相关的进尺深度、速度和电流为特征属性,采用模糊C均值聚类算法对软弱地基进行地层识别研究。结果表明,与传统的K-means算法相比,本文方法对地层分类识别具有更高的准确性和优越性,可实现对地层地质条件的实时研判。研究成果对后续进行振冲碎石桩施工质量合理评价以及振冲碎石桩桩成过程智能化施工等都有重要的指导意义。
Abstract
Accurately obtaining the geological information of soft foundation is an essential basis for determining the construction technique and controlling the pile quality of vibro-replacement stone columns. The existing geological exploration technology used to determine stratum information is considerably random and discrete, which makes it impossible to comprehensively understand the geological conditions of the reinforced areas. To overcome these limitations, this study relies on a large amount of data related to stratum classification attributes collected by the real-time monitoring system during the construction process of vibro-replacement stone columns at Lawa Hydropower Station. By cleaning big data, features such as penetration depth, speed, and current related to stratum classification attributes were selected for fuzzy C-means clustering algorithm-based study of stratum identification of the soft foundation. The results indicate that compared to the traditional K-means algorithm, the method proposed in this paper exhibits higher accuracy and superiority in identifying strata and enables real-time research and judgment of geological conditions. The research findings presented in this paper are of great significance in the rational evaluation of vibro-replacement stone column construction quality and the intelligent construction of the pile formation process.
关键词
振冲碎石桩 /
地层识别 /
模糊C均值聚类 /
实时监控系统 /
施工过程参数
Key words
vibro-replacement stone column /
stratum identification /
fuzzy C-means clustering algorithm /
real-time monitoring system /
construction process parameters
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基金
中国水利水电科学研究院三型人才专项项目(GE0145B022021)
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