Lean Construction 4.0: Exploring the Challenges of Development in the AEC Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 1994, Lean Construction was understood as the application of Toyota Production principles to Construction. Since then, Lean Construction researchers and advocates have made two fundamental contributions: i) Lean Construction has become a production management theory in its own right; ii) Lean Construction has involved not only production management, but also people, technology, sustainability, safety, education, among others. With the arrival of the "fourth industrial revolution" or Industry 4.0, there has been seminal research attempts to acknowledge the influence of Industry 4.0 on the architecture-engineering-construction (AEC) industry (e.g. Construction 4.0), where the focus has been primarily on technology. However, for Lean Construction to keep evolving and serving the AEC industry, it must embrace the changes propelled by Industry 4.0, but maintain the people-processes-technology triad at its core. We argue that a shift towards Lean Construction 4.0 is needed, paying attention to the synergies between production management theory and digital/smart technologies. The term "Lean Construction 4.0" does represent the vision where we envision the AEC industry to be in the future, rather than its current status. The goal of this paper is not to propose an implementation plan, but to identify research needs and to motivate a discussion on the role of Lean Construction in facing the challenges of adopting Industry 4.0 in the AEC industry.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it