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桥梁裂缝智能识别与监测方法研究

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当前,裂缝识别与监测一直是桥梁结构健康监测的重要研究内容.在桥梁结构现场检测与监测中,传统的裂缝识别与监测技术尚不足以满足实际工程的时效性和精确性需求,尤其是裂缝监测技术.基于深度学习的裂缝图像识别极大提升了裂缝检测的效率和精度,但目前仅能获得特定时刻的裂缝信息,缺乏对裂缝产生和演化过程的监测能力,而这些信息对混凝土结构服役安全量化和科学评价具有重要意义.鉴于此,对基于深度学习的裂缝识别与监测方法进行了系统研究,分析和讨论了裂缝数据集构建基准,改进优化了裂缝目标检测和语义分割算法,提出一种多任务集成一体化实时识别算法,并建立了该模型推理效果评价方法,优化了裂缝参数计算方法,最终形成了裂缝识别及动态扩展自动化实时监测方法.结果表明:所提出的裂缝智能识别与监测方法可以对新裂缝的产生和既有裂缝的全局演化实现良好追踪,监测数据可以为桥梁结构当前服役性能的科学量化评估提供支撑.
Crack Intelligent Recognition and Bridge Monitoring Methods
Recognition and monitoring of cracks is an important part of the current research on the structural health monitoring of bridges.In the field of inspection and monitoring of bridge structures,traditional crack recognition and monitoring techniques,particularly crack monitoring techniques,hardly meet the timeliness and accuracy requirements of practical projects.Crack recognition based on deep learning has greatly improved the efficiency and accuracy of crack detection;however,it can only obtain crack information at a specific moment,and the ability to monitor the process of crack generation and evolution,which is crucial for a more reasonable evaluation and safety quantification of concrete structures,is lacking.In view of this,a systematic study of crack recognition and monitoring methods based on deep learning was performed.In this study,we analyze and discuss the construction benchmark of a crack dataset,improve and optimize the crack detection and semantic segmentation algorithms,propose a real-time recognition algorithm for multitask integration,establish an evaluation method for the model inference effect,and optimize the calculation method of crack parameters,ultimately forming crack recognition and automatic real-time monitoring algorithms for crack dynamic expansion.The results show that the proposed method for intelligent recognition and monitoring of cracks can effectively track the generation of new cracks and the global evolution of existing cracks,and the monitoring data can provide support for a reasonable and quantitative assessment of the current service performance of bridge structures.

bridge engineeringbridge structureintelligent recognition and monitoringconcrete crackdeep learningcrack parametersinference effect

岳清瑞、徐刚、刘晓刚

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北京科技大学土木与资源工程学院,北京 100083

西安建筑科技大学土木工程学院,陕西西安 710055

北京科技大学城镇化与城市安全研究院,北京 100083

桥梁工程 桥梁结构 智能识别与监测 混凝土裂缝 深度学习 裂缝参数 推理效果

国家自然科学基金重大项目国家自然科学基金重大项目北京科技新星计划

5219266351890903Z201100006820044

2024

10.19721/j.cnki.1001-7372.2024.02.002

中国公路学报
中国公路学会

中国公路学报

CSTPCD北大核心
影响因子：1.607
ISSN：1001-7372
年,卷(期)：2024.37(2)
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