收稿：2018-03-08，
纸质出版：2020-07-07
移动端阅览
本文以青岛市胶州湾跨海大桥为研究对象
针对2014-01—2016-03 PS-InSAR技术测得的形变数据进行信息深度挖掘。在数据处理过程中，首先引入形变随季节变化及形变随温度变化的两种新模型，然后分别采用传统线性模型和这两种模型对PS点形变数据进行了分解，通过对比3种模型与实测形变数据的匹配程度，评估不同模型在桥梁分析过程中的性能。数据分析结果证实，随季节变化的周期型形变是桥梁主要形变，进而线性—周期模型的分析效果最好。同时，基于桥梁某区间的数据，重点分析了该区间内形变信息沿桥梁纵向的剖面图，并结合桥梁结构信息，对伸缩缝两侧PS点的形变特征及成因进行重点讨论。实测数据分析证实了InSAR技术具备监测桥梁微小形变信息的能力。在未来应用过程中，它能对桥梁形变风险进行早期识别，提前对风险桥梁及其风险区域进行预报，并为风险成因分析提供测量数据，最终为城市桥梁风险管理提供技术支持。
The study selected Qingdao Jiaozhou Bay Bridge as the study object and gathered information on deep mining based on InSAR deformation data acquired from Jan 2014 to May 2016.
Two new deformation models with seasonal variation and temperature variation were first introduced in data analysis. These two models and the traditional linear model were utilized to decompose the deformation evolution data. Thereafter
the performance of the different models was evaluated by calculating the deviation component among the three models and the measured data. After analyzing the data of a section of the bridge
the deformation information along the longitudinal direction was acquired. The deformation characteristics of the PS points on both sides of the expansion joint and bridge structure were discussed.
Results were used to confirm that the periodic deformation component was the main deformation component on the bridge
thus validating the performance of the Linear–Periodical model as the best among the models. Meanwhile
the thermal effect of the bridge panels caused the difference in the deformation between the two sides of the expansion joint.
The analysis of measured data confirms that InSAR technology has the capacity to monitor the microdeformation information of the bridge. In the future
it can identify the bridge with deformation risk early
and search the bridge with risk and its corresponding area in advance. At the same time
the measurement data could also be used for risk cause analysis. Finally
it can provide technical support for urban bridge risk management.
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