Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks

TL;DR

This paper introduces a machine learning framework using Impact Echo signals and neural networks to automatically detect and classify concrete defects, improving the reliability and scalability of bridge health assessments.

Contribution

It develops a novel data-driven Impact Echo analysis method combining FFT, spatial mapping, and LSTM networks for multi-class defect classification in concrete structures.

Findings

Achieved 73% overall accuracy in defect classification.

Validated model generalization on in-service bridge decks.

Enhanced objectivity and scalability of non-destructive testing.

Abstract

Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and multi-class classification of common concrete defects. Raw IE signals from Federal Highway Administration (FHWA) laboratory slabs and in-service bridge decks are transformed via Fast Fourier Transform (FFT) into dominant peak-frequency features and interpolated into spatial maps for defect zone visualization. Unsupervised k-means clustering highlights low-frequency, defect-prone regions, while Ground Truth Masks (GTMs) derived from seeded lab defects are used to validate spatial accuracy and generate high-confidence training labels. From these validated regions, spatially ordered peak-frequency sequences are constructed and fed into a stacked Long Short-Term Memory (LSTM) network that classifies four defect types shallow delamination, deep delamination, voids, and honeycombing with 73% overall accuracy. Field validation on the bridge deck demonstrates that models trained on laboratory data generalize under realistic coupling, noise, and environmental variability. The proposed framework enhances the objectivity, scalability, and repeatability of Non-Destructive Evaluation (NDE), supporting intelligent, data-driven bridge health monitoring at a network scale.