Semantic segmentation of plastic greenhouse frames and its application to finite element model reconstruction for structural response assessment

• Semi-automated framework for reconstructing the finite element model of plastic greenhouses was proposed. • Proposed segmentation model (GHFrameFormer) extracts steel frame from plastic film with 65.97 % mIoU. • Reconstructed FE model showed below 0.7 % (MAPE) geometric error. • Predicted linear load–displacement in FE analysis matched the on-site loading test with 3.6 % NRMSE. • Proposed framework establishes a foundation for digital-twin safety monitoring of horticulture. Plastic greenhouses (PG) are widespread, low-cost agricultural structures that are highly vulnerable to extreme weather events. However, reliable on-site methods for evaluating structural safety are still lacking. Furthermore, no prior study has presented a pipeline for reconstructing finite element (FE) model of a PG from its point cloud data (PCD) using semantic segmentation. This study proposes and validates a semi-automated framework to address this gap through: 1) automated segmentation of steel frames from PCD extracted through LiDAR scanner; 2) conversion of segmented PCD into as-built FE model; and 3) evaluation of structural response. A transformer-based semantic segmentation model, GHFrameFormer, was trained on a diverse dataset of four PG and evaluated on a completely independent, large-scale test PG. GHFrameFormer achieved the highest mean intersection-over-union of 65.97 %, outperforming baseline models (e.g., Point Transformer v1) and maintaining high computational efficiency. The segmented PCD was converted into a FE model via a density-controlled iterative clustering algorithm. Geometric validation across three independent PG confirmed high fidelity, with a mean absolute percentage error remaining below 0.7 % compared to on-site measurements. Finally, on-site loading test results were compared with the FE analysis results of the reconstructed FE model. The reconstructed FE model reliably reproduced the structural response (load–displacement) in a linear-elastic range with a normalized root mean squared error of 3.6 %. The proposed framework demonstrates that an AI-based approach can dramatically reduce manual post-processing of raw PCD while delivering geometric fidelity and reliable structural response prediction, establishing a foundation for digital-twin-enabled structural monitoring in horticulture applications.