Year: 2025
Type: article
Abstract: Concrete bridge decks are susceptible to subsurface defects such as delamination, caused by aging, corrosion, and environmental stressors, underscoring the need for timely, reliable non-destructive evaluation (NDE). While traditional acoustic methods, such as, hammer or chain drag remain widely used, they suffer from subjectivity, inconsistent impact forces, and limited applicability on overhead or vertical surfaces. This study introduces a novel Smart Acoustic Sounding System (SASS) that modernizes impact sounding through an integrated framework consisting of a broadband electronic chirp excitation source, high-sensitivity MEMS microphones with acoustic shielding, and a tracking camera for automated and location-aware inspections. Advanced signal processing techniques, such as Empirical Mode Decomposition (EMD), Power Spectral Density (PSD), and the Hilbert-Huang Transform (HHT), are employed to filter noise, extract frequency-based features, and support machine learning-based defect classifications. Laboratory testing on a full-scale concrete slab embedded with known artificial defects (e.g., shallow and deep delamination, voids, and honeycombing), as well as a deteriorated concrete beam, confirmed the system’s ability to accurately identify defect zones, particularly shallow delamination with characteristic frequency signatures in the range of 1–3 kHz. The system produced real-time defect maps with minimal human input, demonstrating its potential to improve the accuracy, repeatability, and efficiency of bridge deck inspections and support data-driven maintenance decisions.
Source: BER :
DOI: 10.70465/ber.v2i4.54
Authors: Deepak Kumar, Anil K. Agrawal
Institution: City College of New York
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Awards: G5624514283, G7774286821, G949076093
OpenAlex ID: W4415064046