基于Revit二次开发与深度学习的钢框架节点智能审图

Intelligent drawing review of steel frame structure joints based on Revit secondary development and deep learning

近年来,为了提高审图的工作效率、降低返工率,基于BIM的智能审图与自动审图技术成为BIM领域中的研究热点,然而在结构中至关重要的节点模型目前尚未有自动合规性检查的相关研究。为此,提出了基于Revit二次开发与深度学习的智能审图框架,包括信息抽取、语义丰富、合规性推理与建议三部分内容,针对钢框架四种梁柱节点完成了智能审图系统开发。以节点特征值为样本所训练的一维卷积神经网络充分考虑了模型的几何特征,在优化后对梁柱节点分类的准确率达到98.59%,高于其他常用的机器学习分类算法。通过所设计的合规性推理算法可完成节点模型的构造规则校核与强度校核,并提出优化建议。所开发的智能审图系统完成了一栋四层钢框架模型,共计136个梁柱节点的智能审图,准确率为97.79%,耗时86 s,与人工审图相比提高了准确率与审图效率。

In recent years,in order to improve the work efficiency of drawing review and reduce the rework rate,BIM-based intelligent drawing review and automatic drawing review technologies have become a hot topic in the field of BIM research.However,the joint model,which is crucial in structures,has not yet been studied for automatic compliance check.Against this background,an intelligent drawing review framework based on Revit secondary development and deep learning is proposed,which includes three parts:information extraction,semantic enrichment,as well as compliance reasoning and suggestions.The intelligent drawing review system is developed for four kinds of beam-column joints in steel frame structures.The one-dimensional convolutional neural network trained with joint eigenvalues as samples fully considers the geometric features of the model,and the accuracy of beam-column joint classification reaches 98.59%after optimization,which is higher than that of other commonly used machine learning classification algorithms.The developed compliance reasoning algorithm can complete the construction rule checking and strength checking of the joint model,and put forward optimization suggestions.The developed intelligent drawing review system has completed the intelligent drawing review for a four-story steel frame structure model with a total of 136 beam-column joints.The accuracy rate is 97.79%and the time-consuming is 86 s,which improves the accuracy and efficiency of drawing review compared with manual drawing review.