Mapping surface deformation and thermal dilation of arch bridges by structure-driven multi-temporal DInSAR analysis

Journal Article (2018)
Author(s)

Xiaoqiong Qin (The Hong Kong Polytechnic University, Wuhan University)

Lu Zhang (Wuhan University)

Mengshi Yang (TU Delft - Mathematical Geodesy and Positioning, Wuhan University)

Heng Luo (Wuhan University)

Mingsheng Liao (Wuhan University, Ministry of Land and Resources of China)

Xiaoli Ding (The Hong Kong Polytechnic University)

DOI related publication
https://doi.org/10.1016/j.rse.2018.06.032 Final published version
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Publication Year
2018
Language
English
Volume number
216
Pages (from-to)
71-90
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305
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Abstract

Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or collapse on public safety. An essential method for SHM, the modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, can detect subtle deformation of bridges at relatively low costs. Nevertheless, identifying dense point-like targets (PTs) on such partially coherent arch bridges in SAR image is more difficult than that for other man-made objects, owing to their complex structures and backscattering behaviors. Furthermore, the complex mechanical properties of arch bridges, due to the varying arch-beam interactions, make it hard to separate the surface deformation and thermal dilation accurately, and the lack of specific structural knowledge, that can help to understand the deformation evolution process, limits the global structural risk assessment. Aiming at these problems, we developed a structure-driven multi-temporal DInSAR approach for arch bridge-specific SHM. By introducing three structure-driven steps, i.e. backscattering geometrical interpretation, linear thermal dilation estimation and validation, and Deformation Feature Points (DFPs) based risk assessment, into the traditional DInSAR method, the reliability of PTs identification, thermal dilation separation, and structural risk assessment for arch bridges are significantly improved. The effectiveness of our approach was fairly presented by two case studies of the Rainbow and Lupu bridges, and the experimental results were verified by leveling benchmark validation, cross-sensor comparison, as well as structural-reliability assessment. Our results revealed that arch bridges exhibit a similar pattern of PTs distribution that is dense around piers and sparse on the spans, as well as a symmetrical progressive pattern of surface deformation with the subsidence increasing from piers and reaching a peak at the central spans. In contrast, magnitudes and mechanisms of thermal dilation are different, and highly dependent on the materials and structural characteristics of specific bridges.

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