Article(id=1273953312899633501, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, articleNumber=null, orderNo=null, doi=10.20174/j.JUSE.2026.02.27, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750176000000, receivedDateStr=2025-06-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1781663738703, onlineDateStr=2026-06-17, pubDate=1776614400000, pubDateStr=2026-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1781663738703, onlineIssueDateStr=2026-06-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1781663738703, creator=13701087609, updateTime=1781663738703, updator=13701087609, issue=Issue{id=1273953231114887613, tenantId=1146029695717560320, journalId=1272209045839646724, year='2026', volume='22', issue='2', pageStart='377', pageEnd='752', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1781663719203, creator=13701087609, updateTime=1781663760928, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1273953406235467954, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1273953406235467955, tenantId=1146029695717560320, journalId=1272209045839646724, issueId=1273953231114887613, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=653, endPage=663, ext={EN=ArticleExt(id=1273953313151291743, articleId=1273953312899633501, tenantId=1146029695717560320, journalId=1272209045839646724, language=EN, title=The Realization and Application of AI Reconstruction Method of Stratum Information Based on FCN, columnId=null, journalTitle=Chinese Journal of Underground Space and Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

It is quite important for the refined design and construction of geotechnical engineering to obtain a certain amount of borehole data and then determine the stratum profile information through geotechnical investigation. However, due to the particularity of the area where some engineering sites are located (existing old urban areas, cultural Relic Protection Building, etc.), there is often a problem that the borehole data is difficult to obtain, which makes the corresponding stratum information determination challenging. To this end, an artificial intelligence (AI) method for stratum information reconstruction is developed based on the fully convolutional network (FCN). The core idea of this method is to use the existing borehole data in the region as a learning sample, analyze and extract the multi-dimensional information features of the sample (vertical stratification, horizontal extension), and then use this information feature as a template to perform probability-based stratum profile information interpolation reconstruction for engineering sites with only a small amount of borehole data. Through the study and reconstruction of the geological survey data of a tunnel project and foundation pit project in the ancient city of Suzhou, it is found that the accuracy of stratum prediction gradually tends to be stable after the number of simulations increases to more than 30 times, and can reach about 90%. This verifies the applicability of the developed AI reconstruction method of stratum information, which will provide an effective choice for the prediction of complex stratum information in related projects.

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通过岩土工程勘察获取一定量的钻孔数据继而确定地层剖面信息，这对于岩土工程精细化设计、施工至关重要。然而，部分工程场地因所处区域的特殊性（既有老城区、文保建筑区等）往往存在钻孔数据难以获取的问题，使得相应的地层信息确定存在挑战。为此，基于全卷积网络（FCN）开发了用于地层信息重构的人工智能（AI）方法，该方法的核心思路是将区域既有钻孔数据作为学习样本，分析并提取样本的多维信息特征（竖向成层、水平延展），继而将该信息特征作为模板针对仅有少量钻孔数据的工程场地进行基于概率的地层剖面信息插值重构。通过对苏州古城区某隧道工程与基坑工程的地勘数据进行学习与重构，发现地层预测的准确度在模拟次数增加至30次以上后逐渐趋于稳定，并可达90%左右。这验证了所开发的地层信息AI重构方法的适用性，将能为相关工程的复杂地层信息预测提供有效选择。

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冯伟健（2000—），男，江苏扬州人，主要从事人工智能方法在岩土工程中的应用研究工作。E-mail: wjfeng0422@163.com

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冯伟健（2000—），男，江苏扬州人，主要从事人工智能方法在岩土工程中的应用研究工作。E-mail: wjfeng0422@163.com

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