设计、施工、监测

基于多方法组合的南京长江漫滩相土层智能划分

  • 蔡智 ,
  • 夏培凯 ,
  • 张廷忠 ,
  • 张锐 ,
  • 申志福
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  • 1.中国水利水电第七工程局有限公司,成都 610213;
    2.中电建铁路建设投资集团有限公司,北京 100060;
    3.南京工业大学 城市地下空间研究中心,南京 211816
蔡智(1987—),男,成都人,工程师,主要从事岩土工程勘察技术与管理工作。E-mail:306752321@qq.com
申志福(1988—),男,四川绵阳人,博士,副教授,主要从事城市地下空间工程的教学与科研工作。E-mail:zhifu.shen@njtech.edu.cn

收稿日期: 2025-06-15

  网络出版日期: 2026-06-23

基金资助

国家自然科学基金青年项目(51908284)

Intelligent Stratigraphic Division of the Nanjing Yangtze River Floodplain Soil Layers Based on Combined Multiple Methods

  • Cai Zhi ,
  • Xia Peikai ,
  • Zhang Tingzhong ,
  • Zhang Rui ,
  • Shen Zhifu
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  • 1. Powerchina Sinohydro Bureau 7 Co., Ltd., Chengdu 610213, P. R. China;
    2. Power China Railway Construction Investment Group Co., Ltd., Beijing 100060, P. R. China;
    3. Research Center of Urban Underground Space, Nanjing Tech University, Nanjing 211816, P. R. China

Received date: 2025-06-15

  Online published: 2026-06-23

摘要

在岩土工程勘察中,基于有限的原位测试和取样试验进行土层划分时,受钻孔间距限制,孔间土层往往依靠人为经验推断,如何智能、可靠地进行土层划分是当前岩土工程信息化发展的重要研究方向。本文提出贝叶斯压缩感知、支持向量机分类、高斯混合模型和隐马尔可夫随机场模型4种方法组合的土层智能划分方法,并以南京长江某漫滩相土层为例,展示了所提方法的应用流程和土层划分结果。结果表明:贝叶斯压缩感知能对原位测试的标贯击数进行可靠扩展,用于后续土层划分;支持向量机分类能在(标贯击数、深度)二维空间中智能学习各土类的边界,实现对土层的初步划分;在此基础上,借助高斯混合模型,引入土体特征参数的概率分布,可实现土层划分的初步优化;最后借助隐马尔可夫随机场模型,引入空间相关性约束(相邻点更倾向于同一种土类),可实现土层划分的二次优化;4种方法的组合可智能、自动化地进行土层划分,提高了土层整体划分的准确性和土层边界识别的准确性。

本文引用格式

蔡智 , 夏培凯 , 张廷忠 , 张锐 , 申志福 . 基于多方法组合的南京长江漫滩相土层智能划分[J]. 地下空间与工程学报, 2026 , 22(3) : 928 -936 . DOI: 10.20174/j.JUSE.2026.03.18

Abstract

In geotechnical site investigation, the distance between boreholes often makes the inter-borehole soil layer inferences rely on human experience under conditions of limited in situ tests and sampling. How to conduct intelligent and reliable soil stratigraphic division is a crucial research direction in the current information-oriented development of geotechnical engineering. This paper presents an intelligent soil stratigraphic layer division method by combining Bayesian Compressed Sensing (BCS), Support Vector Machine (SVM) classification, Gaussian Mixture Model (GMM), and Hidden Markov Random Field (HMRF) model. The application flowchart and soil layer division results are presented by taking the Nanjing Yangtze River floodplain ground as an example. The study shows that: BCS can reliably extend the blow count data from standard penetration test (SPT) for subsequent soil layer division; SVM classification can intelligently learn soil boundaries in the two-dimensional space of SPT blow count versus test depth, achieving an initial soil stratigraphic division; based on this, the preliminary optimization of soil layer division can be realized by using the GMM by considering the probability distribution of soil characteristic parameters; finally, the secondary optimization of soil layer division can be realized by using the HMRF model by incorporating spatial correlation constraints (i.e., adjacent points tending to be the same soil type). Combining the four methods can intelligently and automatically divide soil layers, and can significantly improve the accuracy of overall soil layer division and soil layer boundary identification.

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