Experimental Investigation on Damage Feature Extraction and Identification for Sustainable Infrastructure Resilience via Improved Empirical Fourier Decomposition

Hu Sun; Zhuyao Du; Rong Chen; Wang Yan; Sang Dan; He Jia; Yongfeng Li

doi:10.62177/amit.v1i7.1113

Authors

Hu Sun School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
Zhuyao Du School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
Rong Chen School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
Wang Yan School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
Sang Dan School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
He Jia School of Architecture and Thermal Engineering, Shaanxi Institute of Technology
Yongfeng Li School of Architecture and Thermal Engineering, Shaanxi Institute of Technology

DOI:

https://doi.org/10.62177/amit.v1i7.1113

Keywords:

Damage Identification, Experimental Research, Energy Value Difference

Abstract

This study presents an initial damage damage feature extraction based on improved empirical Fourier decomposition. Standard Empirical Fourier Decomposition (EFD) is a time-frequency signal processing technique that adaptively decomposes non-stationary, multi-component signals into mono-component intrinsic mode functions (IMFs) by iteratively fitting Fourier series and screening components with physical significance, which is widely used in signal analysis for structural health monitoring but prone to modal aliasing when processing vibration signals with overlapping frequency bands. Our improved EFD differs from the standard version by introducing two key optimizations: a 5th-order Butterworth low-pass preprocessing step (cutoff frequency 100 Hz) to eliminate high-frequency noise, and a correlation coefficient threshold (≥0.9) for IMF screening to discard spurious components, effectively mitigating modal aliasing and improving decomposition accuracy by approximately 15% compared to the standard method. The method identifies structural damage by decomposing structural vibration responses, obtaining modal values and energy values, and comparing energy differences, the technology identifies structural damage. Leveraging the relationship between damage-induced vibration mode changes and energy variations, it allows single-point identification, reducing data collection costs. This technology delivers tangible contributions to sustainable development goals (SDGs): enabling cost-effective and precise early-stage damage detection, it supports proactive and sustainable infrastructure maintenance, extends structure lifespan, reduces resource waste and reconstruction-related environmental impacts. Additionally, it enhances urban structural resilience and safety against disasters and wear, directly aligning with the core tenets of SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities). Finally, the method's effectiveness is verified by slope beam and stand simulator tests.

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