A Mesh Updating Framework for Shell-Element Finite Element Models Based on Projection of Anomaly Regions Extracted from Point-Cloud Data.

Authors

Jiexiu Wang, Mayuko Nishio

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 18. Sep 20, 2026. Epub Sep 20, 2026.

Abstract

Finite element (FE) analysis that incorporates structural damages extracted using computer vision (CV) methods based on three-dimensional point-cloud data (PCD) is effective for evaluating the residual capacity of structures. In this study, observable surface damages are regarded as surface anomaly regions on the structure. Based on this assumption, a modular mesh-model updating framework is proposed to incorporate damage information into shell-element FE models through the anomaly detection from point-cloud data. The framework comprises three modules: a perception module for the anomaly detection and anomaly-region localization, a description module for the anomaly-region quantification, and a remeshing module for mapping the anomaly region onto the FE model. Its performance was evaluated using a steel angle member specimen with artificially introduced anomaly regions representing damage. The updating results were evaluated in terms of both geometric accuracy and FE analysis (FEA) applicability by comparison with the reference FE model manually constructed from the designed anomaly-region geometries. The Intersection over Union (IoU) between corresponding anomaly regions in the updated and reference models ranges from 0.44 to 0.87, including the individual plate results for cross-surface cases. Through static elastoplastic analysis, the predicted reductions in ultimate load-bearing capacity differed by approximately 1.2 percentage points between the two updated models. Moreover, local stress redistribution showed qualitative agreement, although quantitative discrepancies remained in the magnitudes and locations of stress concentrations. Together with several supporting components, the proposed framework provides an effective workflow for integrating PCD-based CV techniques into FE model updating and has potential for FEA-based damage assessment of structures.

PMID:
42817514
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.

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