Abstract

Data interoperability between domain-specific applications is a key prerequisite for building information modeling (BIM) to solidify its position as a central medium for collaboration and information sharing in the construction industry. The Industry Foundation Classes (IFC) provides an open and neutral data format to standardize data exchanges in BIM, but is often exposed to data loss and misclassifications. Concretely, errors in mappings between BIM elements and IFC entities may occur due to manual omissions or the lack of awareness of the IFC schema itself, which is broadly defined and highly complex. This study explored the use of geometric deep learning models to classify infrastructure BIM elements, with the ultimate goal of automating the prechecking of BIM-to-IFC mappings. Two models with proven classification performance, Multi-View Convolutional Neural Network (MVCNN) and PointNet, were trained and tested to classify 10 types of commonly used BIM elements in road infrastructure, using a dataset of 1496 3D models. Results revealed MVCNN as the superior model with ACC and F₁ score values of 0.98 and 0.98, compared with PointNet's corresponding values of 0.83 and 0.87, respectively. MVCNN, which employs multiple images to learn the features of a 3D artifact, was able to discern subtle differences in their shapes and geometry. PointNet seems to lose the granularity of the shapes, as it uses points partially selected from point clouds.

中文翻译：

---

用于检查基础架构建筑信息模型中元素到实体映射的几何深度学习方法

摘要

特定领域应用程序之间的数据互操作性是建筑信息模型（BIM）巩固其在建筑业中协作和信息共享的中央介质的地位的关键前提。工业基础类（IFC）提供了一种开放且中立的数据格式，以标准化BIM中的数据交换，但是经常会遭受数据丢失和分类错误的影响。具体地，由于广泛定义和高度复杂的人工遗漏或缺乏对IFC模式本身的了解，BIM元素与IFC实体之间的映射可能会发生错误。这项研究探索了使用几何深度学习模型对基础设施BIM元素进行分类的最终目标，即自动进行BIM到IFC映射的预检查。两种型号具有经过验证的分类性能，多视图卷积神经网络（MVCNN）和PointNet经过培训和测试，使用1496个3D模型的数据集对道路基础设施中的10种常用BIM元素进行分类。结果显示MVCNN是具有ACC和F的卓越模型₁分的得分值为0.98和0.98，而PointNet的得分分别为0.83和0.87。MVCNN使用多个图像来学习3D伪像的特征，因此能够辨别其形状和几何形状的细微差异。由于PointNet使用部分从点云中选择的点，因此似乎失去了形状的粒度。