Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter...

Area of Science:

• Civil Engineering
• Structural Health Monitoring
• Signal Processing

Background:

• Temperature significantly impacts damage detection performance in civil engineering structures.
• Quasi-static, long-term structural health monitoring data is often contaminated by temperature effects.
• Accurate damage detection requires effective separation of environmental influences like temperature.

Purpose of the Study:

• To propose a novel method, the Adaptive Bandwidth Filter Algorithm (ABFA), for separating temperature effects from structural health monitoring data.
• To enhance the performance of damage detection by mitigating temperature-induced noise.
• To explore the multi-scale nature of temperature effects on structural responses.

Main Methods:

• The Adaptive Bandwidth Filter Algorithm (ABFA) utilizes particle swarm optimization (PSO) to automatically adjust filter bandwidth.
• Time series data is decomposed into different time scales (daily, monthly, yearly).
• Frequency domain analysis is employed, with scales corresponding to adaptive filter center frequencies. Statistical regression establishes the temperature-response relationship.

Main Results:

• The ABFA successfully decouples temperature effects from structural monitoring data with high accuracy.
• Simulation and experimental results validate the algorithm's promising performance.
• The method effectively handles the multi-scale characteristics of structural response data.

Conclusions:

• The proposed ABFA provides an effective solution for removing temperature effects in structural health monitoring.
• Accurate temperature effect decoupling leads to significantly improved damage detection capabilities.
• The algorithm offers a robust tool for analyzing long-term structural performance data under varying environmental conditions.