深度学习在隧道与地下工程中的应用现状及展望  

Application Status and Prospects of Deep Learning in Tunnels and Underground Engineering

作　　者：宋战平[1,2,3] 杨子凡 张玉伟 霍润科 SONG Zhanping;YANG Zifan;ZHANG Yuwei;HUO Runke(School of Civil Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,Shaanxi,China;Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering,Xi′an 710055,Shaanxi,China;Institute of Tunnel and Underground Structure Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,China)

机构地区：[1]西安建筑科技大学土木工程学院,陕西西安710055 [2]陕西省岩土与地下空间工程重点实验室,陕西西安710055 [3]西安建筑科技大学隧道与地下结构工程研究所,陕西西安710055

出　　处：《隧道建设(中英文)》2025年第2期221-255,共35页Tunnel Construction

基　　金：国家自然科学基金资助项目(52178393,52308374);陕西省创新能力支撑计划-创新团队(2020TD-005);陕西省自然科学基金(2023-JC-YB-297)。

摘　　要：为系统分析深度学习在隧道及地下工程中的应用研究进展,分别从参数反演分析、施工机械参数预测与优化、施工及运营过程控制与风险评估、隧道安全监测与缺陷检测、隧道结构健康预测、围岩分级、掌子面图像识别与分类等7个方向对深度学习在隧道及地下工程问题中的应用现状进行研究。结果表明:1)参数反演理论体系的建立已基本完善,结合新型监测技术、计算机技术及仿真技术,建立多源化智能反演模型是今后隧道及地下工程反演方法的发展方向;2)掘进参数的准确预测对于优化施工机械性能及智能掘进过程具有至关重要的作用,且考虑掘进参数之间的相关性与差异性可进一步提高预测模型的泛化能力;3)以多源监测数据在时间与空间上的高度融合为决策基础,基于数据驱动技术的风险控制分析方法为隧道施工及运营阶段的动态设计与信息化施工提供智能化管理;4)特征融合深度神经网络与自适应像素级分割算法相结合的计算机视觉技术不仅降低了缺陷检测成本,更进一步保障了智慧防灾及安全监测系统与工程现场之间的适用性;5)以结构健康监测方法为核心技术,通过融合物理机制和深度学习算法构建的优化系统保障了隧道稳定性与变形预测的准确性与可靠性;6)基于多源信息获取技术的深度学习框架可提取岩体结构面特征参数并转化为定量指标,实现对不同地质环境及施工方法的隧道围岩智能分级;7)利用图像处理技术和深度学习算法,能够从复杂的掌子面图像中自动提取轮廓的有效信息,并进行精确的裂隙特征识别和量化分析。基于深度学习在隧道及地下工程中7类应用方向的总结分析,指出现有研究中存在数据处理实时共享难度大、缺乏模型预测准确度评价标准等问题,并结合隧道及地下空间智能化、绿色化与可持续化建设趋势,针对深度�The authors systematically examine the application progress of deep learning in tunnels and underground engineering across seven key areas:parameter inversion analysis,construction machinery parameter prediction and optimization,construction and operation process control and risk assessment,tunnel safety monitoring and defect detection,tunnel structural health prediction,surrounding rock classification,and tunnel face image recognition and classification.The analytical results reveal the following:(1)The theoretical framework for parameter inversion has reached a relatively mature stage.The future of inversion methods in tunnels and underground engineering lies in the development of multivariate intelligent inversion models that integrate advance monitoring technologies,computer algorithms,and simulation techniques to achieve high accuracy and efficiency.(2)The accurate prediction of tunneling parameters is crucial for optimizing the performance of construction machinery and facilitating intelligent tunneling processes.Considering the correlations and differences among tunneling parameters further improves the generalization capabilities of prediction models.(3)The high integration of temporal and spatial multivariate monitoring data with data-driven risk control methods offers intelligent management solutions,facilitating dynamic design and information-based construction during the tunnel construction and operation phases.(4)Computer vision technology,combining feature fusion-based deep neural networks with adaptive pixel level segmentation algorithms,reduces the cost of defect detection,ensuring the applicability of intelligent disaster prevention and safety monitoring systems at engineering sites.(5)By integrating structural health monitoring methods with deep learning algorithms and physical mechanisms,optimized systems improve the accuracy and reliability of tunnel stability and deformation predictions.(6)A deep learning framework based on multivariate information acquisition technologies extracts key parame

关 键 词：隧道与地下工程 深度学习 多源监测数据 衬砌病害识别 智慧防灾系统 

分 类 号：U45[建筑科学—桥梁与隧道工程]