Dynamic Deformation Analysis of Super High-Rise Buildings Based on GNSS and Accelerometer Fusion

Area of Science:

• Structural Engineering
• Geomatics Engineering
• Signal Processing

Background:

• Accurate dynamic displacement monitoring is crucial for super-tall buildings.
• Existing methods face challenges with sensor noise and data fusion.

Purpose of the Study:

• To develop and validate a data fusion algorithm for high-precision dynamic displacement reconstruction.
• To integrate Global Navigation Satellite System (GNSS) and accelerometer data effectively.

Main Methods:

• Proposed a data fusion algorithm integrating Noise Reduction by Bayesian Optimization-Forward Model Decomposition (NRBO-FMD) with Adaptive Robust Kalman Filtering (ARKF).
• NRBO-FMD preprocesses GNSS and accelerometer data to mitigate errors and noise.
• ARKF fuses preprocessed data for displacement estimation.

Main Results:

• NRBO-FMD significantly reduced noise in GNSS and accelerometer data, improving signal-to-noise ratio (SNR).
• The NRBO-FMD-ARKF fusion algorithm achieved high accuracy with low root mean square error (RMSE) values (e.g., 0.7 mm for 100s dataset).
• Successfully fused 1 Hz GNSS with 100 Hz accelerometer data, demonstrating precision and robustness.

Conclusions:

• The proposed algorithm effectively reconstructs 3D dynamic displacement of super-tall buildings.
• It enhances structural health monitoring by providing accurate and stable displacement estimations.
• The method has potential for real-time and predictive monitoring applications.