Integrating acoustic tapping with a UAV platform for tile condition classification

TL;DR

This paper develops a framework combining UAV-induced vibrations and acoustic testing for building tile inspection, proposing an energy-based correction method to maintain high classification accuracy despite motion disturbances.

Contribution

It introduces an energy-based signal correction technique and evaluates its effectiveness in preserving acoustic classification accuracy under UAV-like vibrations.

Findings

Classification accuracy drops with increased vibration but can be restored above 98% with correction.

PCA effectively reduces data dimensionality while retaining defect-related features.

The framework enables systematic assessment of UAV-induced effects on acoustic testing reliability.

Abstract

Ensuring the structural integrity of building tiles is important for public safety and the durability of urban infrastructure. This study proposes a controlled experimental framework to quantify the effect of Unmanned Aerial vehicle (UAV) induced dynamic perturbations on acoustic tap-testing reliability for facade inspection. This work explicitly analyzes vibration-induced degradation and introduces an energy-based signal correction method to preserve classification performance under motion disturbances. In addition, Principal Component Analysis (PCA) is applied to process and classify wirelessly acquired acoustic data, reducing dimensionality while preserving key defect related features. A Stewart platform is used to reproduce controlled oscillatory conditions derived from UAV flight characterization, enabling systematic evaluation across multiple vibration amplitudes. Results show that classification accuracy degrades significantly under increasing perturbations, but can be restored above 98% using the proposed energy-based filtering approach.