Work

Concept for an ML-supported structural health monitoring of GFRP-tanks using strain data: A case study on the storage of a liquid organic hydrogen carrier under operational-like conditions

Year: 2026

Type: article

Abstract: The current work provides a model-based concept for a machine-learning supported structural health monitoring system for a glass-fibre-reinforced polymer tank for the storage of a liquid organic hydrogen carrier, focusing on damage classification using strain-field data and operational parameters. Finite element generated synthetic data based on a DIN EN 13121-3–conform tank geometry are used to train and evaluate models under operational-like conditions, incorporating both ideal and imperfect tank geometries derived from buckling analyses. A Deep Neural Network model achieves the highest accuracy, whereas for classic ML methods the LinearSVC (LinSVC) model shows the best performance. Fast Fourier Transform proves to be the most effective data-treatment method, especially for imperfect geometries. Strain data in the circumferential direction of the tank cylinder yield more accurate results than axial strain. Considering the DNN, 400–500 load scenarios suffice for ML training for ideal geometries, whereas 600–700 scenarios are required for imperfect geometries. Additive White Gaussian Noise from 1% onward impacts prediction accuracy, with 5% noise causing up to a 25% accuracy reduction. At the same time, Wavelet denoising recovers accuracy close to the original non-noise values. The DNN model also proves more robust against sensor malfunctions than the LinSVC model. • Feasibility of SHM demonstrated from a case study on GFRP-tanks for LOHC storage • ML-supported SHM concept is based solely on strain data and operational parameters • ML-model trained on synthetic data generated by finite element analysis • Fast Fourier Transform is the most effective data treatment method • Deep Neural Network is the most accurate classification model

Source: Materials Today Communications

DOI: 10.1016/j.mtcomm.2026.114681

Authors: Vinicius Carrillo Beber, Olaf Hesebeck

Institution: Fraunhofer Institute for Manufacturing Technology and Advanced Materials

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,830