Towards commands recommender system in BIM authoring tool using transformers

TL;DR

This paper presents a transformer-based command recommender system for BIM authoring tools, aiming to improve modeling efficiency by predicting user commands and reducing effort in BIM software workflows.

Contribution

It introduces a novel end-to-end framework that applies transformer architectures to predict BIM commands, leveraging large-scale log data for real-time suggestions within Vectorworks.

Findings

Model outperforms previous approaches in command prediction accuracy

Real-time command suggestions enhance user efficiency

Framework demonstrates potential for broader BIM tool integration

Abstract

The complexity of BIM software presents significant barriers to the widespread adoption of BIM and model-based design within the Architecture, Engineering, and Construction (AEC) sector. End-users frequently express concerns regarding the additional effort required to create a sufficiently detailed BIM model when compared with conventional 2D drafting. This study explores the potential of sequential recommendation systems to accelerate the BIM modeling process. By treating BIM software commands as recommendable items, we introduce a novel end-to-end approach that predicts the next-best command based on user historical interactions. Our framework extensively preprocesses real-world, large-scale BIM log data, utilizes the transformer architectures from the latest large language models as the backbone network, and ultimately results in a prototype that provides real-time command suggestions within the BIM authoring tool Vectorworks. Subsequent experiments validated that our proposed model outperforms the previous study, demonstrating the immense potential of the recommendation system in enhancing design efficiency.