Actual construction errors and accumulated damage seriously affect the spatial structural form of the tensile membrane structure, making it difficult to characterize the full-life spatial form of the physical tensile membrane structure based on the original theoretical model. There are still significant challenges in achieving the virtual–real correspondence of digital twin results based on the original theoretical design model. It is necessary to propose a high-precision digital twin model construction method that adapts to the full life cycle of the tensile membrane structure. For this reason, a refined digital twin model reconstruction and morphological deviation visualization method for the umbrella-shaped tensile membrane structure combined with 3D point clouds, computational geometry algorithms, and 3D3S finite element simulation modeling is proposed in this paper. Firstly, three-dimensional point cloud data of the umbrella-shaped membrane structure test bench were acquired, and the point cloud data were accurately registered based on the Iterative Closest Point (ICP) algorithm. Secondly, the physical membrane surface reconstruction was obtained by applying the Screened Poisson Surface Reconstruction (SPSR) algorithm. Subsequently, the 3D3S theoretical model of the umbrella-shaped tensile membrane structure was updated according to the contour of the measured membrane surface reconstruction model. Finally, global spatial geometric morphological deviation was compared among the theoretical models and the reconstructed physical model. Furthermore, different environmental load combinations were analyzed to support hazard warning of the refined digital twin model of the physical umbrella-shaped tensile membrane structure. The results show that the maximum spatial form deviation of the original theoretical model was 43.56 mm, whereas the maximum form deviation of the updated theoretical model was only 0.05 mm. The updated theoretical model accurately reflected the spatial form characteristics of the physical membrane structure and satisfied the requirement for digital twin physical–virtual consistency. In addition, the global stress distribution between the original/updated theoretical models differed markedly, and the updated theoretical model is more suitable for identifying weak areas and evaluating safety performance of the actual umbrella-shaped tensile membrane structure under different ultimate environmental loads.

Membrane materials are crucial components that maintain the stability of tensile membrane structures, and damaged membrane material can readily trigger sudden rupture and collapse. To address the strong subjectivity and low efficiency of manual visual inspection methods, this paper proposes an improved dense convolutional classification network framework for damaged membrane material images, namely, DenseNet121-ECA-PTL. Firstly, two tensile membrane test platforms were independently constructed, and 10,000 damaged membrane material images covering five categories (tear, wrinkle, smudge, screen, and scratch) were collected by using smartphones and UAVs to establish a custom dataset. Secondly, an ECA-enhanced DenseNet121 model combined with transfer learning was then developed to improve complex feature extraction, convergence, and generalization, and its performance was validated through comparative and ablation experiments. The proposed model achieved overall testing accuracy, precision, recall, specificity, F1score, and Matthews correlation coefficient of 98.87%, 98.69%, 98.69%, 98.68%, 99.67%, and 98.35%, respectively. The minimum average prediction time of the proposed model was 0.0472 s for each damaged membrane material image. Finally, model interpretability analyses based on T-SNE and Grad-CAM were used to comprehensively examine the significant feature learning and attention-focusing capabilities of the proposed model for multiple damage types of membrane material images.

Large-amplitude and long-term vibration deformation under external environmental loads often occurs on tensile membrane structures. Proper sensor placement plays a vital role in effectively achieving the objectives of a structural health monitoring system. In order to optimize the sensor placement to identify the modal vibration parameters for tensile membrane structures, this paper proposes an optimal sensor placement method based on the Fisher information matrix (FIM) and improved random strategy discrete particle swarm optimization algorithm (IRSDPSO). Firstly, the structural modal order is selected by using the two-norm difference and trace change rate of FIM, and the number of sensors is determined based on the QR decomposition and MAC criterion. Secondly, an improved particle swarm optimization algorithm named IRSDPSO, which has the discrete characteristic, is proposed to arrange the placement of sensors. Finally, the convergence, stability and sensitivity are used to evaluate the effectiveness of optimal sensor placement results using a numerical modal test example of the plane bidirectional tensile membrane structure. The results show that the first nineteen modal frequencies can be accurately identified. This indicates that the proposed optimal sensor placement method can determine the number of sensors and arrange the placement of the sensors. The work is reasonable and feasible in the optimal sensor placement for the tensile membrane structure.