Given that the impact of rough surfaces on assembly accuracy had been insufficiently considered in the existing assembly accuracy prediction for complex products, leading to inaccurate precision prediction and limited practical assembly applicability, an assembly-accuracy prediction method considering rough surfaces was proposed. Firstly, an assembly-accuracy information model was constructed to express mating feature, geometric tolerance, and roughness information. Based on the model, an assembly-precision knowledge graph was constructed. Secondly, a geometric-tolerance representation model was established based on the Small-Displacement Torsor (SDT) theory; a simulation method for rough surfaces of plane and cylindrical parts as well as a determination method of SDT expressions were studied. Thirdly, the error-propagation path of the assembly was determined according to the assembly sequence, and a pose-relationship graph for the assembly was constructed. Then, the assembly-precision prediction was achieved using a Jacobian-torsor model. Finally, the feasibility of the method was verified using the crank-connecting-rod mechanism of a specific construction-machine model as an example. The simulation results demonstrated that the method could achieve accurate assembly-precision prediction and provided valuable guidance for practical assembly operations.

To address the challenges of morphological similarity and the resulting difficulty in classifying vehicle damage images uploaded by clients for auto insurance claims, a model named ResAWDNet was proposed for vehicle damage classification. Firstly, to effectively augment the model’s capacity for extracting damage features, the traditional down sampling operation was replaced with weighted dynamic convolution. This approach dynamically adjusted the weights of convolutional kernels based on the input features, thereby enhancing the model’s adaptability to features of varying scales and orientations. As a result, it enabled more precise capture of the subtle differences in vehicle damage. Secondly, to ensure that the model could concentrate on the salient discriminative regions and feature channels within the images, a dual attention mechanism was embedded after the convolutional layers of the backbone network. This mechanism concurrently learned the important weights in both spatial and channel dimensions, significantly enhancing the model’s ability to capture crucial information. Consequently, it further enhanced the decision-making accuracy of the model in the task of vehicle damage classification. Finally, experimental validation was conducted based on a dataset of vehicle damage images sourced from real accident cases. The experimental results demonstrated that the ResAWDNet model was feasible and offered significant advantages for vehicle damage classification tasks, achieving an accuracy rate of 73.79%. Compared with baseline models, ResAWDNet achieved higher accuracy in classifying multiple types of damages, robustly validating the effectiveness of the proposed model.

With the widespread application of Building Information Modeling (BIM) technology in the field of engineering design, the Revit platform-driven 3D forward design has developed into a relatively mature solution for superstructure. However, as a critical structural component, isolated foundations still face challenges such as low efficiency and poor information integrity in 3D modeling and drawing generation. Moreover, due to the closed data interfaces of mainstream structural analysis software, it is extremely challenging to directly extract reinforcement information from fundamental database files and synchronize to the properties of Revit elements. To address this limitation, a new parametric modeling algorithm for isolated foundations based on AutoCAD layer and text recognition was proposed, utilizing the Revit Application Programming Interface (API) and the Model-View-ViewModel (MVVM) design pattern. By importing foundation layout and reinforcement annotation drawings generated by structural analysis software (e.g., YJK) into Revit, the algorithm calculated the center coordinates and planar dimensions of foundations using layer-recognition methods. Then, a matching logic was established between foundations and reinforcement annotations according to their relative positions. Subsequently, text recognition was employed to extract the corresponding annotations’ numbering, height, and reinforcement information, which were then automatically written into the family properties of the model. Additionally, the extraction of model attributes and geometric face references enabled automatic annotation of foundation dimensions and reinforcement. Finally, the proposed method was applied to the parametric modeling and drafting of isolated foundations for a self- built cold storage factory and was compared against conventional methods. The results demonstrated that this algorithm significantly improved the efficiency of three-dimensional modeling and annotation drawing for isolated foundations, while exhibiting excellent compatibility with calculation files exported from PKPM software.

In roof engineering design, the rationality of equipment maintenance circulation routes directly impacts maintenance efficiency and safety. Traditional design methods often rely on empirical judgment, making it difficult to sufficiently evaluate the rationality of these routes during the design phase. To address this, a hybrid algorithm combining graph theory with an improved A* algorithm was developed. Integrated with Building Information Modeling (BIM) technology, an intelligent analysis and design tool for roof equipment maintenance circulation routes was created to address the shortcomings of traditional design via digital model-based route analysis. First, the roof was converted into a weighted equivalent grid map using collision detection and an octree algorithm. Next, an improved A* algorithm was employed to optimize the maintenance paths, comprehensively considering equipment collision volumes and spatial constraints to calculate the optimal maintenance circulation route and evaluate the rationality of detailed route-area design. Finally, the intelligent analysis and design tool based on this algorithm was tested on an actual project. Experimental results demonstrated that the algorithm accurately revealed potential spatial conflicts and irrational layouts, providing data to support design optimization, and enhanced design rationality and operability; it also improved efficiency by more than five times compared with traditional manual design. The intelligent analysis tool based on this algorithm is currently in use in several projects by the Shanghai Construction (No.4) Group Co., Ltd.

To address the challenges in integrating Digital-Twin (DT) technology with large-scale generative models in industrial design, a CAD-LDT digital-twin modeling framework based on generative foundation models was proposed. The framework adopted a triadic architecture consisting of a physical-entity module, an intelligent generation module, and a virtual-entity module, and innovatively incorporated multi-modal data fusion mechanisms and domain-knowledge constraints to enable autonomous generation of parameterized CAD models from physical-entity descriptions. Utilizing LLaVA-7B and LLaMA-7B as backbone models, the framework employed LoRA-based lightweight adapters to achieve cross-modal alignment between visual and textual features, and introduced a constraint encoder that transformed geometric tolerances and physical rules into structured JSON objects. To enhance the mathematical consistency of spatial transformations, Lie-group algorithms were adopted for the optimization of rigid-body transformations, while a geometric-weight binning strategy was proposed to discretize complex assembly relationships. Moreover, a spatiotemporal-decoupled generation strategy was designed to jointly optimize spatial layout and assembly sequencing. Experimental results on the DeepCAD dataset indicated that the proposed framework achieved an Intersection- over-Union (IoU) of 83.6%, a constraint satisfaction rate of 91.3%, and a 26.5% improvement in generation efficiency, significantly outperforming existing baseline models. Further ablation studies confirmed the critical contributions of multi-modal fusion, constraint encoding mechanisms, and Lie-group optimization to modeling performance, providing a novel DT modeling paradigm for intelligent manufacturing with demonstrated value in parametric design and assembly process optimization.

To address the issues of high computational cost and complex network models in point cloud classification and segmentation methods, a point cloud classification and segmentation algorithm based on lightweight networks and weighted Random Forest (RF) was proposed. The algorithm achieved efficient classification and segmentation in a hierarchical manner. Firstly, to address the issues of multiple layers and complex computation in traditional neural networks, a lightweight neural network was constructed to extract point cloud features such as global shape, inter-regional relationships, curvature, normal vector, and color, thereby achieving rapid rough classification and segmentation of point clouds. Then, to address data imbalance, an adaptive classification and segmentation strategy was designed. By introducing a weighted RF and combining inconsistency-measurement screening with dynamic-weighting optimization mechanisms, fine classification and segmentation of point clouds were achieved. The algorithm conducted classification experiments on the ModelNet40 dataset and segmentation experiments on the Semantic3D dataset and outdoor-scene point-cloud data. The results showed that compared with Local Geo-Transformer, PointNeXt, and FastPointNet++, classification and segmentation accuracy increased by approximately 1.9%, 1.6%, and 1.7%, respectively, while classification and segmentation time was reduced by approximately 40%, 30%, and 20%, respectively. Thus, the proposed point-cloud classification and segmentation algorithm based on lightweight networks and weighted RF can effectively reduce the training time of the model and improve the efficiency of classification and segmentation while maintaining high accuracy, making it an effective point cloud classification and segmentation algorithm.

The detection of Focal Liver Lesions (FLLs) is crucial for disease diagnosis and treatment. Traditional detection methods face many challenges, and the application of deep-learning technology brings new opportunities. In view of this, this paper systematically reviewed the deep-learning-based FLLs detection methods, and provided specific research directions for the development of FLLs detection technology by analyzing the advantages and disadvantages of related technologies. First, the public datasets of liver radiological images were organized and summarized, and the key role of data preprocessing in improving model performance was expounded. Secondly, the 2D and 3D detection algorithms based on convolutional neural networks, Transformer, knowledge distillation, and other technologies were compared and analyzed, revealing the technical evolution path from local feature modeling to global spatio-temporal correlation. In addition, the temporal feature fusion methods for multi-phase images were examined in depth, providing new ideas for dynamic lesion characterization. The review showed that existing methods had achieved breakthroughs in detection accuracy and efficiency, but still faced challenges such as insufficient sensitivity to small lesions, weak cross-device generalization, and lack of clinical verification. Future research was recommended to accelerate the clinical transformation and application of deep learning in auxiliary diagnosis of liver lesions through multi-center data collaboration, lightweight algorithm design, and enhanced interpretability.

Few-shot image generation has important application value in fields such as medical imaging and artistic creation. In recent years, significant research progress has been made in this task, with mainstream approaches typically relying on transferring generative models pretrained on large-scale source domain datasets to target domains to mitigate data-scarcity challenges. However, when substantial semantic gaps exist between source and target domains, direct transfer often introduced incompatible source-specific features, degrading image realism and style consistency. Although existing methods have removed redundant features via static pruning strategies, such as fixed-threshold filter pruning, they struggle to adapt to the dynamic evolution of features across different layers of deep networks, often resulting in the mistaken removal of general low-level features while retaining redundant high-level ones, thereby affecting the adaptation performance and generation quality of the model. To address this, a dynamic pruning method based on filter-importance estimation was proposed. Specifically, the method continuously tracked the changes in Fisher information of each layer’s filters during training to evaluate their importance for image generation quality. Based on the Fisher information, a cumulative importance weight-based adaptive pruning mechanism was constructed to dynamically determine the pruning ratio for each layer, enabling more precise removal of redundant or incompatible filters while preserving general structural semantic information. Experiments were conducted on several representative few-shot target domains, and results showed that the proposed method significantly outperformed existing approaches in terms of image quality (Frechet Inception Distance, FID) and image diversity (Intra-domain Learned Perceptual Image Patch Similarity, Intra-LPIPS). In target domains exhibiting significant semantic differences from the source domain, the proposed method achieved superior FID scores compared with the current state-of-the-art methods, demonstrating its stability and superiority for cross-domain few-shot image generation tasks.

Traditional 3D point-cloud classification methods tend to suffer from insufficient generalization and catastrophic forgetting in Few-Shot Class-incremental Learning (FSCIL) scenarios. The pretrained vision-language model CLIP (Contrastive Language-Image Pre-training), which contains rich 2D shape priors, has been shown to effectively enhance 3D FSCIL performance. However, existing CLIP-based frameworks still lack flexibility and adaptability in multimodal feature extraction and fusion, which limits classification accuracy during incremental stages. To address these shortcomings, a 3D FSCIL approach with deeply fused multimodal features was proposed. An adaptive adapter based on gated units and residual blocks was introduced to achieve multi-scale feature alignment and redundancy suppression, and a multimodal global feature dynamic fusion module with self-attention was designed to adaptively adjust the weight allocation of different feature streams according to sample characteristics, thereby obtaining more consistent and complementary fused representations. Specifically, point clouds were rendered into multi-view depth maps, and features were extracted using both the original CLIP visual encoder and a CLIP encoder pretrained on depth maps, combined with point-cloud geometric features. After processing through the adaptive adapter, these features were fed into the attention-based fusion module and aligned with semantic features extracted by the CLIP text encoder for classification. In addition, contrastive learning loss, multi-view and geometric perturbation-based data augmentation strategies, and a memory-replay mechanism were incorporated to effectively mitigate overfitting and forgetting under few-shot conditions. Experiments on ShapeNet, ModelNet, and CO3D demonstrated that the proposed method consistently achieved higher accuracy across incremental stages compared with existing 3D FSCIL approaches, while significantly reducing both relative accuracy drop rates and maximum stage fluctuations.

Among all safety accidents in construction scenarios, collision accidents are regarded as one of the most common types of injury. To effectively prevent and monitor the occurrence of collision accidents, the computer graphics analysis technology has been used to assist collision detection and analysis; however, limitations remain in balancing the real-time performance with high precision of detection. To address this, a collision-detection method based on dynamic voxelization was proposed. This method integrated the generation of dynamic spatial voxel tree with the dynamic spherical voxelization calculation of resources to construct a collision detection and analysis mechanism. The core ideas are as follows: ① Based on the crowding-degree threshold, the space was recursively divided to generate a dynamic voxel tree, effectively filtering out non-collision risk areas. ② The side length of voxel units were dynamically calculated according to the relative distance between resources and resource volume, realizing the adaptive adjustment of voxel granularity. ③ Spherical voxels were used instead of traditional cubic voxels to avoid the computational burden of non-axis-aligned detection. ④ A hollowing-out procedure was introduced to eliminate internal invalid voxels, further optimizing detection efficiency. This method can accurately capture resource interactions in complex dynamic construction environments, significantly improving detection accuracy and optimizing computational efficiency. Experimental results showed that compared with traditional methods, the proposed method significantly improved the detection accuracy, with precision and accuracy reaching 94.64% and 96.67%, respectively. In terms of collision detection time, it was more efficient than most existing methods, with a calculation speed increase of at least about 11.36%. At the same time, the study analyzed the impact of key parameters such as voxel-tree depth, root-node size, and voxel side length on performance, and analyzed the consumption of CPU resources and memory resources by the method in scenarios of different scales. The consumption was within an acceptable range, verifying the applicability of the method in construction scenarios. The method provided an effective new idea of information processing for enhancing the intelligent level of construction safety management.