光纤传感信息驱动的结构特征参数反演、损伤识别及状态评估方法研究进展

Optical fiber sensing information driven structural feature parameter reflection，damage identification and state assessment:a review

摘要

分布式和准分布式光纤传感器因其在长距离、大跨度和超高层结构全尺度测试方面的低成本、高精度和长期稳定等优势，在工程结构测量及智能化健康监测和预警等系统领域受到高度重视。由于直径纤细光纤的二氧化硅材质特征，在长期连续测量实际工程结构应变和温度等参数时，需要对其进行封装保护处理从而提升传感器的有效服役寿命。在静动态形变测量中，封装光纤测量结构应变可以等效为多层介质结构的应变传递模型，通过应变传递理论指导光纤传感器的封装工艺设计，提升光纤传感器测量的准确性、长期稳定性和耐久性，从而实现结构特征参数的准确反演。在高质量海量监测信息积累基础上，需要融合结构理论和数学分析方法对结构整体和局部损伤进行识别和诊断，开发服役状态安全与预警评估方法，从而为结构健康监测系统的智能化和自动化提供科学指导。因此，本研究将主要介绍工程化光纤传感器件设计和应用历程，并根据课题组在应变传递理论方面的十余年研究成果，系统、深入和全面地探讨点式、准分布式和分布式光纤传感器件耐久型设计核心思想及其测量结构参数的反演分析方法，从而实现工程结构特征参数的准确解读和结构健康或安全状态诊断。进一步，综述基于海量监测数据处理和模态参数估计的结构损伤识别方法、基于多源监测信息的结构状态评估研究现状，并给出了人工智能算法在结构损伤识别和状态评估方向的应用现状和趋势。本研究从硬件和软件层面，为结构健康监测系统的传感器件和传感网络架构优化设计、核心损伤识别和状态评估模块功能升级及智能化和轻量化运行提供可行、可靠、有效的科学支撑。

Abstract

Distributed and quasi-distributed optical fiber sensors have attracted great attention in the field of structural measurement，smart health monitoring and warning system due to their outstanding advantages such as low cost，high precision，and long-term stability in full-scale testing of long-distance，large-span，and super high-rise structures. Owing to the silica material and slender diameter of optical fibers，packaging and protection measures are required during long-term continuous measurement of strain and temperature parameters of actual engineering structures，so as to improve the effective service life of the sensors. The strain measurement of the structure using the packaged optical fiber can be equivalent to a strain transfer model of a multi-layer medium structure. Therefore，strain transfer theory plays a critical role in guiding the packaging process design，thereby realizing accurate inversion of structural characteristic parameters，preventing interface damage between the packaged optical fiber sensor and the measured structure，and improving the accuracy，long-term stability，and durability of distributed and quasi-distributed optical fiber sensors in engineering structure measurement. Based on the accumulation of high-quality and massive monitoring information，it is necessary to integrate structural theory and mathematical analysis methods to identify and diagnose damage in both the overall and local structures. This approach aims to develop safety and early-warning assessment methods for the service condition，thereby providing scientific guidance for the intelligent and automated operation of structural health monitoring systems. Therefore，this review aims to mainly introduce the design and application course of engineering optical fiber sensors. Based on over a decade of research by our group on strain transfer theory，we will systematically and comprehensively discuss the core ideas related to the durable design of point-type，quasi-distributed，and distributed optical fiber sensors as well as the inversion analysis methods for measuring structural parameters，so as to realize an accurate interpretation of engineering structural characteristic parameters and the diagnosis of structural health or safety status. Furthermore，this review summarizes the structural damage identification methods based on massive monitoring data processing and modal parameter estimation，the research status of structural state assessment based on multi-source monitoring information，and the application status and trends of artificial intelligence algorithms in structural damage identification and state assessment. This study provides feasible，reliable，and effective scientific support for the optimization design of sensors and sensor network architecture，functional upgrade of core damage identification and state assessment modules，and intelligent and lightweight operation of structural health monitoring systems from both hardware and software levels.

关键词

光纤传感器 / 应变传递理论 / 测量机理 / 特征参数估计 / 损伤识别 / 状态评估

Key words

optical fiber sensor / strain transfer theory / measurement principle / feature parameter recognition / damage identification / state assessment

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基金资助

中央高校基本科研业务费-青稞计划资助项目(lzujbky-2024-05)

甘肃省教育厅资助项目(2024B-005)

甘肃省教育厅资助项目(2025CYZC-003)