Abstract
Monitoring surface deformation for highways built on soft clay subgrades is fundamental for understanding the dynamics of the settlement process and preventing the occurrence of safety accidents. Most of the traditional interferometric synthetic aperture radar (InSAR) deformation models for highway monitoring are based on a combination of one or several empirical functions, which ignores the underground mechanism of highway deformation. To overcome this limitation, we proposed an advanced InSAR approach that improved the InSAR deformation model and parameter estimation algorithm for soft clay road deformation monitoring. The improved InSAR deformation model is based on the Poisson curve, which considers the characteristics of the temporal physical deformation evolution of the soft clay. The improved algorithm for the unknown parameter estimation is based on a GARN algorithm, which can solve the parameters with better accuracy by combining the genetic algorithm and the regularized Newton iterative algorithm. To evaluate the better performance for the proposed approach and its feasibility for soft clay highway monitoring, both the simulation and the real data experiments on a soft clay highway in Foshan, China, are performed. The time-series deformation from January 1, 2015 to January 18, 2017, is retrieved, and the temporal deformational characteristics over this area are analyzed, which facilitates a greater understanding of the deformational evolution process of the soft clay highway. The results are verified in terms of the residual high-pass deformation and the final obtained vertical deformation compared with the external leveling measurements, which indicate the greater reliability of our method.
Highlights
WITH the geotechnical characteristics of high natural water content, high compressibility, low intensity, and poor structure, the soft clay subgrade is highly prone to cumulative deformation and instability settlement [1]-[3]
The basic steps are as follows: (1) Generating unwrapped differential interferograms over the study area; (2) Selecting the high-coherence points based on the indexes of the average correlation coefficient, intensity, and amplitude dispersion; (3) Interferometric Synthetic Aperture Radar (InSAR) deformation modeling based on Poisson curve, which builds the temporal functional relationship between the InSAR phases and the unknown parameters; (4) GA and the Regularized Newton Iterative Algorithm (GARN) parameter estimation, which includes generating the initial values of the unknown parameters based on Genetic Algorithm (GA) global searching and secondary optimization by the Regularized Newton iterative algorithm (RN) iterative algorithm
The improved InSAR deformation model was based on Poisson Curve, which considers the characteristics of the temporal physical deformation evolution of the soft clay
Summary
WITH the geotechnical characteristics of high natural water content, high compressibility, low intensity, and poor structure, the soft clay subgrade is highly prone to cumulative deformation and instability settlement [1]-[3]. Most of the InSAR deformation models for highway monitoring are based on the combination of single or several empirical models (such as the linear model [21], seasonal model [22], and polynomial model [23], etc.) lacking the specific deformation mechanism of the highway subgrade Those pure mathematical empirical models may not accurately describe the real time-varying disciplines of the settlement of highway soft soil foundation, which would affect the accuracy of the derived time-series. To describe the temporal evolution process of soft soil deformation with better reality, the Poisson curve is introduced here into the traditional MT-InSAR deformation modeling and, an advanced time-series InSAR deformation model based on Poisson curve is proposed for the soft clay highway deformation monitoring. In this paper, we propose an advanced InSAR approach for soft clay highway deformation monitoring In the approach, both the InSAR deformation model and the parameter estimation algorithm are improved. The residual deformation and comparison with external leveling measurements are discussed to verify the better performance and feasibility for soft clay highways
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