Implementation of Lean Thinking to Improve Masonry Construction and Design
Bibliographic record
Abstract
Masonry construction provides multiple functions with a single element, is cost-effective, durable, and provides a visually appealing finish.In addition, its flexibility in design and reasonable construction cost makes it more attractive.Specifically, characteristics of loadbearing masonry make it a viable choice for residential buildings, hence a viable solution to address housing demands.However, evidence shows this type of building is less desired nowadays due to its reputation as having traditional shapes and low productivity in the construction process.Lean thinking has been widely applied in the construction industry.However, lean applications in the masonry industry can be widened.In this research, site visits, consultations with industry professionals and stakeholders, and an extensive literature review have been conducted to understand existing problems of design and construction of load-bearing masonry systems in Canada.To address the discovered problems, several lean thinking solutions are proposed with the focus on consideration of complex wall configurations and providing early feedback in the conceptual design stage of masonry buildings.Development of one-piece flow for mortar transportation and generative design tools are two of proposed solutions.Development of intelligent BIM and construction simulation models are presented as future research ideas to validate the proposed lean solutions.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".