Static strain-based identification of extensive damages in thin-walled structures

Abstract

Interest has been expressed during the past few years toward incorporating structural health monitoring (SHM) systems in ship hull structures for detecting damages that cause significant load-carrying reductions and subsequent load redistributions. The guiding principle of the damage identification strategy considered in this work is based upon measuring, through a limited number of sensors, the static strain redistributions caused by an extensive damage. The problem is tackled as a statistical pattern recognition one, and therefore, methods sourcing from machine learning (ML) are applied. The SHM strategy is both virtually and experimentally applied to a thin-walled prismatic geometry that represents an idealized hull form solely subjected to principal bending stresses (sagging/hogging). Damage modes causing extensive stress redistribution, are abstractly represented by a circular discontinuity. The damage identification problem is treated in a hierarchical order, initialized by damage detection and moving to an increasingly more localized prediction of the damage location. Training datasets for the ML tools are generated from numerical finite element simulations. Measurement uncertainty is propagated in the theoretical strains by information inferred from experimental data. Two different sensor architectures were assessed. An experimental programme is performed for testing the accuracy of the proposed damage identification strategy, yielding promising results and providing valuable insights.

Real-Time Digital Twin of Ship Structure Deformation Field Based on the Inverse Finite Element Method

“…Structural health monitoring (SHM) is a multidisciplinary technical field offering reliable insights into the mechanical behavior of structures under load using measurement data from sensor systems, thus facilitating decision support for structural safety evaluations. Existing stress monitoring systems of hull structures mainly use limited and discrete strain sensors placed directly at monitoring points, which makes it difficult to obtain the full field information of the hull structure and cannot provide a complete stress state description of each detailed structure in the whole ship [2][3][4][5][6][7][8].…”

Deep learning-based bridge damage identification approach inspired by internal force redistribution effects

“…21 This effect is often accompanied by damage or permanent loss of prestressing and can therefore be used for characterizing potentially undesirable structural changes. 22,23 Silionis et al 24 indicated that damage modes cause extensive stress redistribution on thin-walled structures. Lin et al 25 confirmed the stress reduction effects of railway bridges in real service conditions through stress variations on the mid-span section due to running trains in the field test.…”

Damage Diagnostics on Post-buckled Stiffened Panels Utilizing the Digital-Twin Concept

“…finite element (FE) model. The idea of training offline meta-models (also referred as surrogate models) with data generated by FE models is currently gaining spiraling attention [2,4,7,11,12,14,[16][17][18]. Diagnostics, as well as prognostics, are tackled utilizing surrogate models in an attempt to leverage in-situ sensor measurements and provide near real-time predictions.…”