Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN-Adaptive Threshold Wavelet Method

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement.

Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.

Intelligent Control of Building Operation and Maintenance Processes Based on Global Navigation Satellite System and Digital Twins

Building operation and maintenance (O&M) processes are tedious. Controlling such tedious processes requires extensive visualization and trustworthy decision-making strategies. Unfortunately, challenges still exist as existing technologies and practices can hardly achieve effective control of building O&M processes. This study has established a method for achieving intelligent control of building O&M processes by integrating Global Navigation Satellite System (GNSS) with Digital Twins (DTs) techniques. Specifically, GNSS could be used to capture real-time building information during building O&M processes. Such captured real-time information realizes the intelligent closed-loop control of building O&M driven by DTs. In this study, the authors have (1) captured the dynamic information required for achieving intelligent control of building O&M processes, (2) established a DT model of building O&M processes, (3) established a data management mechanism of intelligent building O&M processes, and (4) formalized an intelligent building O&M decision control platform. Finally, the authors have validated the proposed method using the 2022 Beijing Winter Olympics venue as a case study. The three-dimensional coordinates of various building information are captured based on GNSS automatic monitoring system. This realizes the precise positioning of O&M elements and feedbacks to the twin model of the venue. Through the intelligent analysis and prediction of O&M information, the characteristics of various O&M accidents are obtained. Finally, under the navigation function of GNSS, the processing measures are accurately formulated. Results indicate that the proposed GNSS–DTs-based method could help to achieve intelligent control of large building O&M processes.