Transfer Learning Enabled Process Recognition for Module Installation of High-rise Modular Buildings
Bibliographic record
Abstract
High-rise modular buildings (HMB), based on the advanced approach of modular construction, have gained momentum in practice due to their offered benefits in accelerated construction, improved quality, reduced health and safety risks, and enhanced productivity. Modular construction with standard design of modules and repetitive processes of module installation is in favor of the development of construction automation. As module installation is one of the critical activities in the delivery of HMBs, it is important to recognize the module installation process automatically so as to facilitate automation in modular construction. However, there is no detailed phase-division of module installation process. Also, little research has been carried out on intelligent process recognition for module installation due to the limited amount of images of real-life projects. To fill in the knowledge gaps, this paper aims to build a transfer learning enabled process recognition model using convolutional neural network (CNN) for module installation of HMBs. The study first divided the module installation process into three stages: hooking, lifting and positioning, with a comprehensive literature review. Then the recognition model for module installation process was created and trained with the adoption of CNN-based transfer learning, and verified with images taken from real-life projects. The results show that the three stages of module installation process are effectively recognized with the proposed model. The transfer learning enabled image recognition model for module installation process accelerates automation in the construction of HMBs for enhanced productivity and accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".