Validating a physics-based automatic classification scheme for Impact Echo signals on data from a concrete slab with known defects

Abstract

Impact Echo (IE) is capable of locating subsurface defects in concrete slabs from vibrational response of the slab to mechanical impact. For an intact slab (‘good’ condition), the frequency spectrum of IE is dominated by a peak corresponding to the slab’s ‘thickness resonance frequency’ since much of the impact energy is reflected from the bottom of the deck. The presence of subsurface defects (‘fair’ or ‘poor’ conditions) could manifest in various ways such as multiple distinct peaks at frequencies higher or lower than the thickness resonance. Energy distribution across different frequency bands varies with the type and extent of damage. In previous research, the authors have proposed a frequency partitioning of the spectrum for IE signal classification. First, the thickness resonance frequency band is identified using Gaussian Mixture modeling. Next, IE signals are represented by their energy distribution in three bands – frequencies less than, within and greater than thickness resonance. IE signals are reduced to feature vectors representing the proportion of energy in each band. Following the feature extraction, an unsupervised clustering approach is used to identify the centroids for each signal class – good, fair, poor, which is used to classify any test signal into one of the three classes. The classification is developed by training on unlabeled IE signals from real bridge deck data (Federal Highway Administration’s (FHWA) InfoBridge dataset) without making use of any labeled data. This study aims to validate the proposed methodology developed based on field data on a labeled dataset of eight reinforced concrete specimens constructed at FHWA Advanced Sensing Technology NDE laboratory with known artificial defects: shallow and deep delamination, honeycombing and voids. Findings indicate that the physics-based feature definition and the method developed on real bridge data is robust and can classify IE signals in the labeled data with high accuracy.