Spatially resolved strain measurement at meter scale using a carbon fiber based strain sensor and artificial neural networks

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Life cycle optimization, maintenance planning and adaptive control systems in fiber-reinforced structures such as aircraft wings require the monitoring of loads and stresses during operation. State of the art systems using strain gauges can measure strains at limited numbers of discrete points, while systems based on fiber optic time domain reflectometry require complex and cost intensive evaluation units. A novel sensor based on electrical time domain reflectometry (ETDR) allows to acquire information about the spatial distribution of strain along a fractured carbon fiber (CF) embedded in a composite structure. This sensor concept has been investigated in previous studies with specimens up to 60 mm in length. Based on this work, a demonstrator with an improved sensor layout and two embedded sensors of 1 m length is developed. A shallow feed-forward network and a convolutional neural network are compared regarding their ability to infer strain profiles from measured ETDR reflectograms. The simultaneous evaluation of two sensors with a convolutional neural network allowed the inference of strain distributions with a good generalization ability.


Original languageEnglish
Title of host publicationProceedings of the 10th ECCOMAS Thematic Conference on Smart Structures and Materials (Smart 2023)
Number of pages12
ISBN (electronic)978-960-88104-6-4
Publication statusPublished - Jul 2023


Title10th ECCOMAS Thematic Conference on Smart Structures and Materials
Abbreviated titleSMART 2023
Duration3 - 5 July 2023

External IDs

ORCID /0000-0003-2834-8933/work/143495369
ORCID /0000-0003-1385-1528/work/143495834
ORCID /0000-0002-8854-7726/work/143496306
Mendeley fbb3fa9e-ab5a-3c42-b766-3d7c5925303d
unpaywall 10.7712/150123.9945.444359
ORCID /0000-0002-0110-3066/work/156812785



  • carbon fiber, composite, continuous strain measurement, spatially resolved strain sensor, convolutional neural network, structural health monitoring