Unsupervised deep learning framework for ultrasonic-based distributed damage detection in concrete: integration of a deep auto-encoder and Isolation Forest for anomaly detection

Vahab Toufigh, Iman Ranjbar

Structural Health Monitoring 2023 article cited by 24

importance 6/10 ai assisted Ultrasonics and Acoustic Wave Propagation structural health monitoring stage: research

Problem

Detecting hidden damage inside concrete structures using ultrasonic signals, without needing labeled data from damaged specimens.

Why it matters

Undetected concrete degradation can lead to costly and life-threatening structural failures in buildings and infrastructure.

What the AI did

A deep auto-encoder trained only on intact-specimen ultrasonic responses reconstructs signals; reconstruction-error features feed an Isolation Forest that flags damaged specimens as anomalies.

Results

Framework described as 'successfully implemented'; no quantitative performance metrics stated in abstract

Impact

Evidence supports a working lab-scale detection framework; no field deployment or benchmark numbers stated.

Record

Venue	Structural Health Monitoring
Published	2023
Work type	article
Authors	Vahab Toufigh, Iman Ranjbar
Citations	24 (OpenAlex, at import time)
References	53
Model or method	Deep fully connected auto-encoder and Isolation Forest
Organization	not stated in source
Primary source	https://doi.org/10.1177/14759217231183143
Open access	closed
Keywords	auto-encoder, Isolation Forest, ultrasonic testing, anomaly detection, concrete damage

Importance rating

6 / 10

significant — thousands to millions affected

Undetected concrete degradation threatens buildings and infrastructure broadly, but abstract shows only lab-scale validation on five specimens; no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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