Classification of Building Information Model (BIM) Structures with Deep Learning

TL;DR

This paper explores machine learning techniques, including deep learning, to classify BIM-derived images of building designs into three categories, achieving high accuracy with neural networks.

Contribution

It compares classical and deep learning methods for BIM image classification, demonstrating the effectiveness of neural networks over traditional approaches.

Findings

Deep learning models achieved over 89% accuracy.

Classical HOG + SVM model achieved 57% accuracy.

Neural networks outperform traditional machine learning methods.

Abstract

In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.