Article(id=1259474419915866752, tenantId=1146029695717560320, journalId=1259198796492718097, issueId=1259474414219964747, articleNumber=null, orderNo=null, doi=10.15951/j.tmgcxb.2026.01.0918, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758124800000, receivedDateStr=2025-09-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778211701611, onlineDateStr=2026-05-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778211701611, onlineIssueDateStr=2026-05-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778211701611, creator=13701087609, updateTime=1778211701611, updator=13701087609, issue=Issue{id=1259474414219964747, tenantId=1146029695717560320, journalId=1259198796492718097, year='2026', volume='59', issue='1', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778211700252, creator=13701087609, updateTime=1778211964781, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1259475523919594262, tenantId=1146029695717560320, journalId=1259198796492718097, issueId=1259474414219964747, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1259475523919594263, tenantId=1146029695717560320, journalId=1259198796492718097, issueId=1259474414219964747, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=12, ext={EN=ArticleExt(id=1259474420960248464, articleId=1259474419915866752, tenantId=1146029695717560320, journalId=1259198796492718097, language=EN, title=Artificial intelligence of engineering, columnId=null, journalTitle=China Civil Engineering Journal, columnName=null, runingTitle=null, highlight=null, articleAbstract=The analysis methods for mechanical behavior and safety in engineering still fail to meet real demands. Simply applying available artificial intelligence (AI) methods cannot fundamentally address the strict requirements on the stability and reliability of output in engineering. To tackle this issue, by simulating the thinking and decision-making process of human experts, the ‘mechanism’, represented by mechanical analysis methods, and the ‘data’, obtained after multi-source information assimilation, are integrated in real time. Centering around the mechanical models of engineering, three main methods for AI of Engineering are established, namely the multi-source data assimilation and data quality evaluation method, the mechanism-data coupling-driven AI method, and the cross-engineering synergistic analysis method. These methods are progressively implemented into the framework of AI of Engineering, forming a new generation engineering intelligent agent, and achieving a qualitative change from ‘one-way AI for engineering’ to ‘integrated AI of engineering’. AI systems developed therefrom are applied to landslide dams, slopes, and wind turbine generators to predict the performance. The applications indicate that AI of engineering is not constrained by the limited quantity, unstable quality and weak correlation of multi-source data in practice. It also addresses the limitations of mechanical methods under complex conditions and the difficulties in accurately obtaining computation parameters. AI of engineering integrates multiple functions, such as deformation source tracing, mechanical behavior prediction, risk early-warning and risk regulation, providing solid supports for engineering projects., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=null), CN=ArticleExt(id=1259474420633092748, articleId=1259474419915866752, tenantId=1146029695717560320, journalId=1259198796492718097, language=CN, title=工程域人工智能, columnId=1259474420570178185, journalTitle=土木工程学报, columnName=论坛, runingTitle=null, highlight=null, articleAbstract=目前工程力学行为和安全分析方法仍不能满足实际需求, 照搬已有人工智能方法不能从根本上满足工程对输出结果稳定性和可靠性的严格要求。针对这一问题, 通过模拟专家思维和决策过程, 将以力学分析方法为代表的“机理”和多源信息同化后的“数据”实时耦合, 以工程的力学分析模型为核心建立了多源数据同化-质量评价方法、机理-数据耦合驱动的人工智能方法和跨工程协同分析方法等3个主要方法, 逐层递进提出工程域人工智能理论, 构建新一代的工程智能体, 实现从“人工智能单向赋能工程”到“人工智能与工程一体化构建”的质变。针对堰塞坝、边坡和风力发电机群等重要工程建立人工智能系统, 开展工程服役性能演化预测分析。实际应用表明, 工程域人工智能理论突破工程领域数据数量偏少、质量参差不齐、多源且关联弱等问题的制约, 克服力学分析方法难以考虑工程及环境复杂条件、工程材料结构参数难以精准测定等困难, 实现工程变形溯源、行为预报、风险预警、调控预演等功能, 为工程安全及其高效运行提供科学技术支撑。, correspAuthors=张嘎, authorNote=张嘎(1976—)，男，博士，教授。主要从事岩土破坏力学与工程域人工智能的教学和科研工作。
罗方悦: 罗方悦(1995—)，女，博士，助理教授。主要从事岩土工程域人工智能的教学和科研工作。, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=ByFwONznXCXnyLJIUF/BpQ==, pdfFileSize=2897018, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1.清华大学，北京 100084;
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