Songxue Zhao

China University of Geosciences (Beijing)

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Runjie Wang

Beijing University of Civil Engineering and Architecture

Xianglei Liu

Beijing University of Civil Engineering and Architecture

Honglei Yang

China University of Geosciences (Beijing)

Di Cai

Academy of State Administration of Grain

Zeping Wang

Xinjiang University

Ao Yan

China University of Geosciences (Beijing)

Xue-Qian Li

Shanghai Sixth People's Hospital

Tengjun Zhang

Dongbei University of Finance and Economics

Xianglei Liu

Beijing University of Civil Engineering and Architecture

Jianfeng Han

Inner Mongolia University of Technology

Cooperative Institutions

Beijing University of Civil Engineering and Architecture

China University of Geosciences (Beijing)

Chinese Academy of Sciences

National Administration of Surveying, Mapping and Geoinformation of China

Nankai University

Institut National de la Recherche Scientifique

Inner Mongolia University of Technology

Mongolian University of Science and Technology

Tianjin University

Ministry of Natural Resources

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Improved Landslide Deformation Prediction Using Convolutional Neural Network–Gated Recurrent Unit and Spatial–Temporal Data

Remote Sensing (2025)

Honglei Yang Youfeng Liu Qing‐Long Han Linlin Xu Tengjun Zhang

As one of the major forms of geological disaster, landslides cause huge casualties and economic losses in China every year. Given the importance of landslide prediction, it is a challenging task due to difficulties in efficiently leveraging the spatial–temporal information for enhanced prediction. This paper presents a novel spatial–temporal enhanced CNN-GRU model to improve landslide predictions with the following contributions. First, this paper explicitly models the spatial correlation in the dataset and constructs a spatial–temporal time-sequence deformation prediction model that greatly improves landslide predictions. This model integrates the spatial correlation of monitoring points into time-series deformation prediction to improve the prediction of landslide deformation trends. Second, we develop a complete data processing pipeline involving SBAS-InSAR, time-series data preprocessing, spatial–temporal homogeneous point selection and weighting, as well as CNN-GRU model training. The pipeline is tailor-designed to leverage the spatial–temporal correlation in the data to enhance the prediction performance. Third, we apply the proposed model to monitor landslide deformation around Woda Village, Chamdo City, Tibet. The results show that the root mean square error (RMSE) of the monitoring points in the landslide area is reduced by about 20.9% and the number of points with an RMSE of less than 3 mm is increased by 12.9%, leading to a significant improvement in prediction accuracy.

10.3390/rs17040727

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ESMD-WSST High-Frequency De-Noising Method for Bridge Dynamic Deflection Using GB-SAR

Electronics (2022)

Xianglei Liu Songxue Zhao Runjie Wang

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in the obtained dynamic deflection with the low signal-to-noise ratio. To solve this problem, this paper proposes an innovative high-frequency de-noising method combining the wavelet synchro-squeezing transform (WSST) method with the extreme point symmetric mode decomposition (ESMD) method. First, the ESMD method is applied to decompose the observed dynamic deflection signal into a series of intrinsic mode functions (IMFs), and the frequency boundary of the original signal autocorrelation is filtered by the mutual information entropy (MIE) for each IMF pair. Second, the high-frequency IMF components are fused into a high-frequency sub-signal. WSST is performed to remove the influence of noise to reconstruct a new sub-signal. Finally, the de-noised bridge dynamic deflection is reconstructed by the new sub-signal, the remaining IMF components, and the residual curve R. For the simulated signal with 5 dB noise, the signal-to-noise ratio (SNR) after noise reduction is increased to 11.13 dB, and the root-mean-square error (RMSE) is reduced to 0.30 mm. For the on-site experiment for the Wanning Bridge, the noise rejection ratio (NRR) is 5.48 dB, and ratio of the variance root (RVR) is 0.05 mm. The results indicate that the proposed ESMD-WSST method can retain more valid information and has a better noise reduction ability than the ESMD, WSST, and EMD-WSST methods.

Root mean square

10.3390/electronics12010054

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Improved Data-Driven Stochastic Subspace Identification with Autocorrelation Matrix Modal Order Estimation for Bridge Modal Parameter Extraction Using GB-SAR Data

Buildings (2022)

Xianglei Liu Songxue Zhao Peipei Wang Runjie Wang Ming Huang

With the advantage of non-contact measurement, ground-based synthetic aperture radar (GB-SAR) has been widely used to obtain the dynamic deflection of various bridges. Data-driven stochastic subspace recognition (Data-SSI), a popularized time-domain technique, is commonly used for modal parameter identification of bridges. To improve the computational efficiency and accuracy of the Data-SSI method for bridge modal parameter estimation using GB-SAR, this paper proposes an improved Data-SSI method. First, boxplot data filtering is applied to screen out the error points to generate a Hankel matrix. Second, the Hankel matrix compression method is presented to reduce the ill-conditioned vectors in the column vectors of the Hankel matrix to improve calculation efficiency. Finally, the exact modal order (EMO) modal estimation algorithm based on the autocorrelation matrix is adopted to reduce the generation of false modes and improve the calculation efficiency. The results of simulation and field experiments show that the natural frequency values for the improved Data-SSI method are 2.3208 and 2.3189 and the damping ratio coefficient values are 8.10 and 8.08, under windows 1 and 2, respectively. The operation times using the improved Data-SSI method are 2.02 s and 7.61 s under windows 1 and 2, respectively. This proves that the proposed improved Data-SSI method has higher accuracy and computational efficiency.

Autocorrelation matrix

10.3390/buildings12020253

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An Innovative Gradual De-Noising Method for Ground-Based Synthetic Aperture Radar Bridge Deflection Measurement

Applied Sciences (2024)

Runjie Wang Haiqian Wu Songxue Zhao

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment.

Ground-Penetrating Radar

10.3390/app142411871

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Heterogeneous Atmospheric Parameters Correction (H-Apc) for Micro-Deformation Monitoring of High-Rise Buildings Using Gb-Sar

Runjie Wang Haoran Wu Xianglei Liu Songxue Zhao

Deformation monitoring

High rise

10.2139/ssrn.5076662

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Enhanced Wide-Area Glacier Velocity Monitoring in Svalbard via Synthetic Aperture Radar Offset Tracking Noise Suppression

Applied Sciences (2024)

Honglei Yang Songxue Zhao Zeping Wang Ao Yan Zheming Shi

Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging due to factors such as low temporal correlation and high noise effects. This paper presents the pixel offset tracking (POT) technology based on Synthetic Aperture Radar (SAR) data for glacier velocity monitoring, with enhanced cross-correlation matching window and noise suppression approaches. In particular, a noise suppression optimization method and a matching window optimization index suitable for wide-area glacier velocity monitoring are proposed. The inter-annual wide-area two-dimensional plane flow velocity of glaciers in the Svalbard archipelago was obtained by using a total of seven Sentinel-1 data sets from two orbits covering the entire Svalbard archipelago in 2021. The results indicate that 25 large glaciers in Svalbard destabilized in 2021, with a peak flow velocity of 6.18 m/day. At the same time, the influence of climate, topography, and other factors on glacier surface velocity is discussed. The wide-area glacier velocity monitoring method and its application demonstrated in this paper will serve as a valuable reference for studying glacier migration in the Arctic Svalbard archipelago and for other large-scale wide-area deformation monitoring efforts.

Glacier mass balance

10.3390/app142310834

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Implications of equalities among the elements of CKM and PMNS matrices

Chinese Physics C (2016)

Hong-Wei Ke Songxue Zhao Xue-Qian Li

Investigating the CKM matrix in different parametrization schemes, it is noticed that those schemes can be divided into a few groups where the sine values of the CP phase for each group are approximately equal. Using those relations, several approximate equalities among the elements of CKM matrix are established. Assuming them to be exact, there are infinite numbers of solutions and by choosing special values for the free parameters in those solutions, several textures presented in literature are obtained. The case can also be generalized to the PMNS matrix for the lepton sector. In parallel, several mixing textures are also derived by using presumed symmetries, amazingly, some of their forms are the same as what we obtained, but not all. It hints existence of a hidden symmetry which is broken in the practical world. The nature makes its own selection on the underlying symmetry and the way to break it, while we just guess what it is.

Matrix (chemical analysis)

Cabibbo–Kobayashi–Maskawa matrix

Sine

CP violation

10.1088/1674-1137/40/5/053101

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A Normal Vector Constrained Gaussian Curvature Method for Detecting Potential Damage Areas in Bridges Using Single-Temporal Tls

Xianglei Liu Di Cai Songxue Zhao Runjie Wang

Accurate acquisition of potential damage areas is a prerequisite for precise detection of bridge damage. The single-temporal terrestrial laser scanning (TLS) technique can be used to detect bridges potential damage areas with its advantages. However, there will be a sharp feature in areas of a measured bridge that undergo morphological changes. It is prone to being incorrect or missing in acquired point cloud curvature values, which impacts damage detection accuracy. To solve this problem, we propose a normal vector constrained coordinate transformation method for Gaussian curvature model construction. In addition, we establish criteria for potential damage detection. The effectiveness of the proposed method is verified based on the Beishatan Bridge in Beijing, China. Experimental results show that the method can improve the accuracy of Gaussian curvature and effectively detect potential damage areas in the bridge.

Gaussian curvature

10.2139/ssrn.4590571

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A Normal Vector Constrained Gaussian Curvature Method for Bridge Potential Damage Area Detection Using Single-Phase Tls

Xianglei Liu Di Cai Runjie Wang Songxue Zhao

Single-phase terrestrial laser scanning (TLS) can be used to detect bridge potential damage areas based on Guassian curvature model. However, there will be mangy ambiguity points existed in small cracks or potential damage areas in point cloud model, which will reduce the integrity of the Gaussian curvature model. Thus, the study proposes a normal vector constrained coordinate transformation method for Gaussian curvature model integrity improvement and potential damage detection. The method first identifies and eliminates the ambiguity points through integrating the initial judgment by normal distance constraint and further screening by neighborhood growth method. Then the point cloud normal vectors are normalized to improve the integrity of Gaussian curvature model. In addition, the criterion for bridge potential damage detection is established. The effectiveness of the proposed method is verified through the Beishatan Bridge in Beijing, China. The experimental results showed the method can detect the bridge potential damage areas effectively.

Gaussian curvature

Single phase

Bridge (graph theory)

10.2139/ssrn.4429046

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Extreme-point Symmetric Mode Decomposition-based Energy Integral Model for Bridge Abnormality Detection Using Ground-based Synthetic Aperture Radar

Sensors and Materials (2023)

Songxue Zhao Xianglei Liu Runjie Wang

Bridge (graph theory)

Mode (computer interface)

Abnormality

10.18494/sam4413

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