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An Improved Time-Series Model Considering Rheological Parameters for Surface Deformation Monitoring of Soft Clay Subgrade

Lookup NU author(s): Dr Lifu Chen

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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.

Publication metadata

Author(s): Xing X, Chen L, Yuan Z, Shi Z

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2019

Volume: 19

Issue: 14

Print publication date: 02/07/2019

Online publication date: 11/07/2019

Acceptance date: 09/07/2019

Date deposited: 28/08/2019

ISSN (print): 1424-8239

ISSN (electronic): 1424-8220

Publisher: M D P I AG

URL: https://doi.org/10.3390/s19143073

DOI: 10.3390/s19143073

PubMed id: 31336806

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