AI-Supported Real-Time Schedule Updating and Maintenance in 4D BIM
Bibliographic record
Abstract
This paper presents a novel approach that leverages speech recognition and natural language processing (NLP) to streamline construction schedule management within the four-dimensional (4D) Building Information Modeling (BIM) environment.A tool named "Voice-Integrated Scheduling Assistant for 4D BIM" (VISA4D) has been developed as part of this study.The users can input schedule updates into this tool via voice or text commands, and the updates are integrated with the Autodesk Navisworks API in real-time.The goal of this tool is to enable superintendents to engage with construction schedules on-site through their mobile devices in a practical manner, allowing for changes to project timelines without requiring direct access to the BIM model of the project.This simplifies the process of updating and maintaining schedules based on the progress observed on-site.Moreover, the developed tool improves project progress tracking by enabling automated colour-coding within the 4D BIM environment to visually differentiate completed components from pending tasks.For instance, building elements turn green upon installation, while those on hold are highlighted in red to facilitate status monitoring and visualization.This paper provides the initial findings of the study with an overview of the VISA4D tool and details of its system architecture and components.Examples of the tool's usage for updating and maintaining the schedule of an office building and an educational building project in Canada are presented.Ongoing work includes application the tool for other types of building projects and its validation through user testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".