A hybrid learning strategy for structural damage detection

Lorena Andrade Nunes, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa, Alexandre Cury

Structural Health Monitoring 2020 article cited by 29

importance 5/10 ai assisted Structural Health Monitoring Techniques structural health monitoring stage: research

Problem

Most structural damage detection techniques fail to automatically identify novelties such as damage or abnormal behavior from dynamic measurements, making human decisions necessary.

Why it matters

Undetected structural damage risks infrastructure failure with safety and economic consequences.

What the AI did

Proposes an original hybrid learning approach combining statistical parameters extracted from raw time-domain data (e.g., acceleration) with machine learning for automatic damage detection.

Results

not stated in source

Impact

Proposed approach targets real-time automatic damage detection; outcomes not stated in source.

Record

Venue	Structural Health Monitoring
Published	2020
Work type	article
Authors	Lorena Andrade Nunes, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa, Alexandre Cury
Citations	29 (OpenAlex, at import time)
References	40
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1177/1475921720966943
Open access	closed
Keywords	structural health monitoring, damage detection, machine learning, hybrid learning, acceleration data

Importance rating

5 / 10

real but narrower advance

Automatic novelty and damage detection from raw dynamic measurements for structural health monitoring; safety-relevant infrastructure problem, but abstract gives no scale or deployment evidence.

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

More in Engineering & Materials

#	Paper	Year	Citations	Importance
1	Scaling deep learning for materials discovery Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, G...	2023	1,471	8
2	Application of Machine Learning in Material Synthesis and Property Prediction Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie	2023	165	7
3	Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang	2023	115	7
4	Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review Gennaro Scarselli, Francesco Nicassio	2025	44	7
5	Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology Mihail D. Senyuk, Murodbek Safaraliev, Firuz Kamalov, Hana Sulieman	2023	42	7