Abstract

Building deformation models consistent with reality is a crucial step for time-series deformation monitoring. Most deformation models are empirical mathematical models, lacking consideration of the physical mechanisms of observed objects. In this study, we propose an improved time-series deformation model considering rheological parameters (viscosity and elasticity) based on the Kelvin model. The functional relationships between the rheological parameters and deformation along the Synthetic Aperture Radar ( SAR) line of sight are constructed, and a method for rheological parameter estimation is provided. To assess the feasibility and accuracy of the presented model, both simulated and real deformation data over a stretch of the Lungui highway (built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. With the proposed deformation model, the unknown rheological parameters over all the high coherence points are obtained and the deformation time-series are generated. The high-pass (HP) deformation component and external leveling ground measurements are utilized to assess the modeling accuracy. The results show that the root mean square of the residual deformation is ±1.6 mm, whereas that of the ground leveling measurements is ±5.0 mm, indicating an improvement in the proposed model by 53%, and 34% compared to the pure linear velocity model. The results indicate the reliability of the presented model for the application of deformation monitoring of soft clay highways. The estimated rheological parameters can be provided as a reference index for the interpretation of long-term highway deformation and the stability control of subgrade construction engineering.

Highlights

  • Stability control of highways built on a soft clay subgrade is one of the key technical problems for highway subgrade engineering

  • According to our in situ investigation and the design materials collected from the highway construction company, Fuoshan, China, we found a section of the test highway which was still under road surfacing from November 2014 to December 2014, and the whole route was opened to traffic in January 2015

  • A time-series deformation model considering rheological parameters was proposed, and the rheological parameters of elastic modulus and viscosity were introduced into a traditional empirical functional model

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Summary

Introduction

Stability control of highways built on a soft clay subgrade is one of the key technical problems for highway subgrade engineering. For highways built on soft clay, a single pure empirical mathematical function may not describe the actual dynamic evolution, due to its temporally complicated non-linear characteristics, and a negative impact could be imposed on the accuracy of the obtained measurements and the subsequent displacement prediction This would be adverse to the corresponding long-term analysis and deformation interpretation following highway construction. A qualitative analysis of the material is initially carried out, the corresponding rheological state function is constructed, which quantitatively represents the functional relationship between the strain of soft soil material and physical variables (i.e., viscosity, elastic modulus, and time) These are mainly based on the series-parallel connection of basic mechanical components (i.e., Burgers model, Kelvin model, and Maxwell model, among others). In the real data scenario, the rheological parameters of a stretch of highway (namely, the Lungui Highway in Foshan, China) are obtained, and the time-series subsidence over the period of June 2014 to December 2015 is investigated using TerraSAR X imagery

Time-Series Deformation Model
Rheological Model Based on the Kelvin Model
Improved Deformation Model Considering Rheological Parameters
Unknown Parameter Estimation
Simulated Experiment
Geological Background of Study Area
SAR Acquisition and Data Processing
Experimental Results
Potential Reasons for the Deformation
Temporal Deformation Characteristics over Feature Points
Comparative Analysis with other Non-Linear Time-Series Models
Accuracy Evaluation
Conclusions

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