顾及几何细节特征的铁路基础设施BIM简化算法

A Simplified Algorithm to Railway Infrastructure BIM Considering Geometric Detail Features

  • 摘要: 铁路基础设施建筑信息模型(building information modeling,BIM)的简化对于数据高效处理具有重要意义,然而现有简化算法存在几何特征保持能力弱、细节信息丢失严重等问题。提出了一种顾及几何细节特征的铁路基础设施BIM简化算法,重点探讨了轨道、路基、隧道和桥梁BIM几何细节特征及其简化需求,构建边界保持、角度误差控制、邻域三角形平均面积等简化规则,设计多类型铁路基础设施简化算法,并通过5种评价标准对算法的有效性进行了详细分析。实验结果表明,所提算法在保留更多几何细节特征的同时,可有效降低简化模型的几何误差。此外,所提算法展现出优异的视觉效果,适用于铁路基础设施模型的简化。

     

    Abstract:
    Objectives Railway infrastructure is diverse in type, large in data volume, and complex in structure. Simplifing its building information modeling (BIM) models is crucial for efficient data processing. However, existing simplifing algorithms face issues such as poor retention of geometric features and significant loss of detailed information.
    Methods We propose a simplifing method for railway infrastructure BIM that explicitly accounts for geometric detail features. First, we identify the specific simplifing challenges associated with track, roadbed, tunnel, and bridge models. Second, based on the unique geometric characteristics of railway infrastructure, establish a set of optimization rules including boundary preservation, angle control, and neighborhood area metrics. Specifically, the boundary preservation factor maintains model contours and prevents the formation of voids. The angle error control factor prevents the loss of triangular faces caused by excessive rotation during edge collapse. The neighborhood triangular average area factor helps retain local fine-grained details. Finally, customized simplification strategies are designed for different infrastructure types, and the method's effectiveness is evaluated using five distinct criteria.
    Results Comparative analysis of three algorithms shows that our proposed method simplifies the railway infrastructure model more effectively. The method retains more geometric detail features, effectively reduces the geometric error of the simplified model, and provides high-quality visual effects. Furthermore, the test results of the simplification factors demonstrate that the boundary preservation, angular error control, and average area of neighboring triangles introduced in this paper effectively reduce model voids and maximize the retention of model contours, surface features, and local details.
    Conclusions The proposed method effectively simplifies the model and is well-suited for simplifing railway infrastructure models.

     

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