Applying Virtual Reality to Improve the Construction Logistics of High-rise Modular Integrated Construction
Bibliographic record
Abstract
Modular and offsite construction is becoming increasingly popular around the world. In Hong Kong, a modular integration construction (MiC) method is identified as a pragmatic approach to speed up the housing construction program and to solve the productivity and manpower problems of the industry. Using the MiC, virtually all the construction works including the finishing as well as the mechanical and electrical installation are completed offsite. The MiC units are then delivered to and installed on site. While the MiC can shift the risks of construction projects to the factories, this construction method is not without challenges. This is particularly the case for Hong Kong as most of the construction sites in the city are cramped due to the high-density urban environment. The problem is aggravated when every modular unit is unique and they are time consuming to produce. Any damages to the MiC components during the lifting process could seriously affect the entire construction sequence under a just-in-time management philosophy. Therefore, it is imperative to plan and monitor the logistics carefully when the MiC technique is used. To reduce any human errors and increase the efficiency and accuracy of the lifting process, a virtual reality (VR) approach may be adopted to simulate the construction logistics of MiC construction and train the crane operators. In this paper, a VR model is developed to simulate the construction of a high-rise residential building in a confined site. Various functions are built into the VR model to support the decisions pertinent to lifting logistics planning. In this paper, the design considerations and functions of the VR model are identified through a series of interviews. Moreover, the validation interviews help unveil the potentials and pitfalls of the developed VR model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".