基于分布式无滑移应变增敏光纤光栅传感器的地铁隧道运营期沉降监测和损伤识别技术

项目名称： 基于分布式无滑移应变增敏光纤光栅传感器的地铁隧道运营期沉降监测和损伤识别技术

项目编号： No.51208113

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 建筑环境与结构工程学科

项目作者： 沈圣

作者单位： 福州大学

项目金额： 25万元

中文摘要： 由于地铁隧道所处环境特殊，采用传统人工观测方法进行全面准确的沉降监测和结构损伤识别较为困难。本项目拟引入分布式光纤光栅(FBG)传感技术，以实现地铁隧道运营期不均匀沉降的全面自动分布式高精度监测和隧道结构损伤准确识别。本项目首先提出一种可根据现场要求定制测量精度与标距长度的分布式无滑移应变增敏FBG传感器，以对隧道结构日常微小应变变化进行长期稳定准确测量。其次，研究基于分布式高精度应变测量的地铁隧道结构应变-沉降高精度计算方法，提出控制海量应变数据测量误差累积的措施，并建立相应的传感器布设设计方法，以及所需传感器测量精度与标距长度的确定方法。最后，结合海量数据特征提取技术，提出基于分布式高精度应变测量的隧道结构损伤静力识别方法。本项目不仅可以实现地铁隧道任意位置不均匀沉降的准确监测和隧道结构由于腐蚀产生损伤的准确识别，还可为后续管养维修的提供线索与指南。

中文关键词： 地铁盾构隧道；沉降监测；损伤识别；光纤光栅；分布式监测

英文摘要： Due to the special work environment of underground tunnels, it is difficult to achieve the accurate distribution of uneven settlement and structural damage by traditional monitoring methods. Based on the distributed Fiber Bragg grating sensing technique, the distributed uneven settlement monitoring and structural damage auto-identification for underground tunnels in their operation periods are shown in this project. First of all, a distributed non-slippage and strain sensitive Fiber Bragg grating sensor is given with good designability of measuring accuracy and gage length according to the detailed requirements from practical monitoring. It can be used for tiny strain variation measurements in practical long-time monitoring of underground tunnels. Then, this project proposes an accurate method to calculate the distribution of uneven settlement along underground tunnels based on the distributed sensitive strain measurements. This method not only restrains the influence of strain measurement error accumulation from mass strain measurement data along the tunnel, but also gives the design procedure for proposed sensor placement. The requirements for the monitoring accuracy and gage length of proposed sensors are also given. At last, combining the feature detection technique for mass measuring data, this project pro

英文关键词： Underground shield tunnel；Settlement monitoring；Damage identification；Fiber Bragg Grating；Distributed monitoring