Feasibility Study of Signal Processing Techniques for Vibration-Based Structural Health Monitoring in Residential Buildings

The growing vulnerability of residential buildings to seismic activity and dynamic loading underscores the need for robust, real-time Structural Health Monitoring (SHM) systems. This study investigates the feasibility of utilizing piezoelectric sensors integrated with advanced signal processing techniques, including Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT), for vibration-based SHM in residential structures. A scaled three-story building prototype was fabricated and subjected to controlled base excitations at 25 Hz and 0.6 g acceleration to evaluate the response under three structural conditions: healthy, randomly damaged, and thickness-damaged. The experimental results revealed that structural degradation significantly altered sensor outputs, with random damage causing irregular signal dispersion and thickness damage introducing additional frequency harmonics. PSD analysis effectively identified shifts in energy distribution, signifying structural degradation, while STFT provided a detailed time-frequency representation, facilitating real-time damage detection. The findings confirm that piezoelectric sensors, when combined with PSD and STFT, can serve as a low-cost, scalable solution for early damage detection in residential buildings, offering a practical framework for enhancing structural resilience against earthquakes and other dynamic forces.

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Publication Details

Journal
Sensors
Published
2025-04-03
DOI
https://doi.org/10.3390/s25072269
Citations
3
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
2.86
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article

Feasibility Study of Signal Processing Techniques for Vibration-Based Structural Health Monitoring in Residential Buildings

Abolghassem Zabihollah, Seyed Ghorshi, Fedaa Ali, Alwathiqbellah Ibrahim et al.
3 citations
Sensors
Structural Health Monitoring Techniques
2.86
article

Feasibility Study of Signal Processing Techniques for Vibration-Based Structural Health Monitoring in Residential Buildings

Abolghassem Zabihollah, Seyed Ghorshi, Fedaa Ali, Alwathiqbellah Ibrahim, Leila Donyaparastlivari, Mohammad Alghamaz
article en
3 citations

Abstract

The growing vulnerability of residential buildings to seismic activity and dynamic loading underscores the need for robust, real-time Structural Health Monitoring (SHM) systems. This study investigates the feasibility of utilizing piezoelectric sensors integrated with advanced signal processing techniques, including Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT), for vibration-based SHM in residential structures. A scaled three-story building prototype was fabricated and subjected to controlled base excitations at 25 Hz and 0.6 g acceleration to evaluate the response under three structural conditions: healthy, randomly damaged, and thickness-damaged. The experimental results revealed that structural degradation significantly altered sensor outputs, with random damage causing irregular signal dispersion and thickness damage introducing additional frequency harmonics. PSD analysis effectively identified shifts in energy distribution, signifying structural degradation, while STFT provided a detailed time-frequency representation, facilitating real-time damage detection. The findings confirm that piezoelectric sensors, when combined with PSD and STFT, can serve as a low-cost, scalable solution for early damage detection in residential buildings, offering a practical framework for enhancing structural resilience against earthquakes and other dynamic forces.

SensorsVol. 25(7)
The University of Texas at Tyler (US), Tarleton State University (US)
Affordable and clean energy
Openalex Percentile: Top 11%
Structural Health Monitoring Techniques
2.86
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Feasibility Study of Signal Processing Techniques for Vibration-Based Structural Health Monitoring in Residential Buildings — Abolghassem Zabihollah, Seyed Ghorshi, et al. · Sensors (2025) | TGRS Research Map | TGRS