沉管隧道结构性裂缝分级治理关键技术研究

Research on Key Technologies for Graded Treatment of Structural Cracks in Immersed Tube Tunnels

摘要: 本研究针对特大型沉管隧道在高负荷交通与复杂水文地质条件下的结构性裂缝治理难题，依托国内首座沉管法越江隧道工程，首创结构性裂缝分级治理技术体系。通过系统性整合裂缝精准检测、渗漏分级评估与差异化治理三大模块，创新建立“渗漏类型–治理措施–材料组合”定量匹配机制，突破传统经验式维修的精度与长效性瓶颈。核心方法包括：开发基于国家规范的裂缝三维表征与渗漏分级技术，聚焦顶板主导性裂缝成因解析；构建注浆堵漏与表面封堵的差异化工艺体系，优化材料配比与工艺参数实现精准治理；融合沉降协同控制技术，显著抑制裂缝复发风险。该体系实现治理有效率大幅提升、复发率显著降低，形成“检测–分析–治理–验证”全流程标准化技术链。核心创新在于建立国内首个沉管隧道裂缝量化分级治理范式，突破定性维修局限；技术框架已在国家级重大工程成功应用，推动行业标准升级，为高负荷越江隧道全寿命智能维养提供科学支撑。未来将深化人工智能预警系统与数字孪生平台研究，构建智慧管养生态体系。

Abstract: This research addresses the structural crack management challenges of extra-large immersed tube tunnels under high traffic loads and complex hydrogeological conditions. Relying on the first immersed tube tunnel project across a river in China, it pioneers a hierarchical management technology system for structural cracks. By systematically integrating three modules: precise crack detection, leakage classification assessment, and differentiated treatment, it innovatively establishes a quantitative matching mechanism of “leakage type-treatment measures-material combination”, breaking through the accuracy and long-term effectiveness limitations of traditional empirical maintenance. The core methods include: developing a three-dimensional characterization and leakage classification technology based on national standards, focusing on the analysis of the causes of dominant cracks in the top slab; constructing a differentiated treatment system of grouting and surface sealing, optimizing material ratios and process parameters for precise treatment; and integrating settlement control technology to significantly reduce the risk of crack recurrence. This system has significantly improved the effectiveness of treatment and reduced the recurrence rate, forming a standardized technical chain covering “detection-analysis-treatment-verification”. The core innovation lies in establishing the first quantitative hierarchical management model for cracks in immersed tube tunnels in China, breaking through the limitations of qualitative maintenance. The technical framework has been successfully applied in national-level major projects, promoting the upgrading of industry standards and providing scientific support for the intelligent maintenance of high-load river-crossing tunnels throughout their life cycle. In the future, research will be deepened on artificial intelligence early warning systems and digital twin platforms to build a smart management and maintenance ecosystem.