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Your search keyword '"Huang, Guanwen"' showing total 11 results
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11 results on '"Huang, Guanwen"'

8. Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR.

Author

Chen, Zi, Huang, Guanwen, and Zhang, Yongzhi

Subjects
*SWELLING soils, *SOIL mechanics, *LANDSLIDES, *MASS-wasting (Geology), *HILBERT-Huang transform, *STANDARD deviations
Abstract

A non-periodic "step-like" variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. [ABSTRACT FROM AUTHOR]

Published
2024
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11. RTK/Accelerometer Tightly Coupled Deformation Monitoring Algorithm Based on Baseline Shift Virtual Observation Constraint

Author

Jing, Ce, Huang, Guanwen, Li, Xin, Zhang, Qin, Huang, Xiaoyu, Zhang, Kai, and Yang, Huan

Abstract

The precision of deformation monitoring with Global Navigation Satellite System (GNSS) technology will degrade in complex environments, such as landslides. Furthermore, the loosely coupled (LC) model based on GNSS and accelerometer offers only limited improvement when dealing with continuous unreliable outliers. In this study, our work concentrates on the multisensor data process, and we introduce a tightly coupled (TC) algorithm that combines real-time kinematic (RTK) and accelerometer, in which the state and observation equations are described in detail. To address the issue of the accelerometer baseline shift (BS) potentially absorbing the errors from float coordinates when the fix ambiguities cannot be resolved, we further propose a virtual observation constraint model based on BS. This model is named as the novel tightly coupled (NTC) algorithm in this article, aiming to slow down the error accumulation in the acceleration integral process. Two experiments were conducted: a simulated deformation experiment and a real-world landslide monitoring task, to validate the effectiveness of the proposed algorithm. The TC algorithm notably enhances the fixed rate of integer ambiguities, achieving 74.37% and 99.97% fix rate in the two experiments, respectively, which represents a 25.37% and 51.67% improvement over the RTK solution. In the case of the NTC algorithm, it achieves more precise BS correction, which leads to a further positive effect on enhancing the fixed ambiguity resolution in the simulated experiment. In the real-world landslide experiment, while the improvement of the fix rate is nonsignificant, however, the ratio value results still demonstrate a noticeable contribution.

Published
2024
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