Implementation of Prefabrication and Modular Offsite Construction using BIM and Lean Construction Techniques
Bibliographic record
Abstract
The construction industry continues to experience productivity rates that lag behind other industries. Additionally, an increasingly competitive market and a decreasing skilled labor pool are challenging construction firms. Prefabrication and offsite modular construction techniques offer alternatives to traditional site-built construction methods that have the potential to provide improved productivity as well as other added benefits. Prefabrication methods, applied effectively, offer results that produce value to the project team. Such value includes improved productivity and efficiency in construction operations, reduced project costs, reduced schedule durations, and improved safety, increased levels of quality and improved sustainability and waste reduction. Currently the implementation of prefabrication and offsite construction techniques on the construction project remains subjective and unstandardized. The aim of this research is to develop a framework that will assist the project team to make decisions regarding the use of prefabrication and modular construction based on factors that have proven to be the most successful in implementing modern methods of construction. The concentration is on emphasizing the use of Building Information Modeling and Lean Construction methods as catalysts to maximize the effectiveness of the use of modular offsite construction. This research is primarily toward the use of prefabrication and modular construction methods for vertical construction and should prove valuable for all project players including Owners, Designers and Constructors. The development of this framework utilizes information compiled through interviews and case studies to develop a proposed framework for implementing prefabrication and off-site modular construction techniques at the project level. The framework will be validated in the future using a Delphi survey to qualitatively generate quantified data on the best methods to implement prefabrication and offsite construction techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".