Localization of Impacts on Thin-Walled Structures by Recurrent Neural Networks: End-to-end Learning from Real-World Data

Alexander Humer; Lukas Grasboeck; Ayech Benjeddou

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arXiv · 2505.08362

Localization of Impacts on Thin-Walled Structures by Recurrent Neural Networks: End-to-end Learning from Real-World Data

Alexander Humer·Lukas Grasboeck·Ayech Benjeddou

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Abstract

Today, machine learning is ubiquitous, and structural health monitoring (SHM) is no exception. Specifically, we address the problem of impact localization on shell-like structures, where knowledge of impact locations aids in assessing structural integrity. Impacts on thin-walled structures excite Lamb waves, which can be measured with piezoelectric sensors. Their dispersive characteristics make it difficult to detect and localize impacts by conventional methods. In the present contribution, we explore the localization of impacts using neural networks. In particular, we propose to use recurrent neural networks (RNNs) to estimate impact positions end-to-end, i.e., directly from sequential sensor data. We deal with comparatively long sequences of thousands of samples, since high sampling rate are needed to accurately capture elastic waves. For this reason, the proposed approach builds upon Gated Recurrent Units (GRUs), which are less prone to vanishing gradients as compared to conventional RNNs. Quality and quantity of data are crucial when training neural networks. Often, synthetic data is used, which inevitably introduces a reality gap. Here, by contrast, we train our networks using physical data from experiments, which requires automation to handle the large number of experiments needed. For this purpose, a robot is used to drop steel balls onto an aluminum plate equipped with piezoceramic sensors. Our results show remarkable accuracy in estimating impact positions, even with a comparatively small dataset.

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Alexander Humer, Lukas Grasboeck, Ayech Benjeddou. 2025-08-04. Localization of Impacts on Thin-Walled Structures by Recurrent Neural Networks: End-to-end Learning from Real-World Data. https://arxiv.org/abs/2505.08362

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