Revolutionizing Construction: The Impact of Artificial Intelligence on Productivity
Bibliographic record
Abstract
The construction sector has begun to embrace the digital revolution, intending to improve efficiency.On the other hand, how should the industry adopt digital tools?.And how should the connection between humans and technology function?.This study aims to shed light on how the construction sector may bridge the gap.between AI deployments's potential and realised advantages.This paper presents research based on a comprehensive review of the literature, case studies of Speller Metcalfe, a design-build and refurbishment project in Malvern, England, Jacobsen Construction, a project digitising the planning process in Salt Lake City, Utah, USA, and Menkes Development Inc., real-time visibility to the construction site insights and data-driven decision-making in Toronto, Canada.The experiences gained via this study show that it is feasible to acquire expertise while adopting sophisticated technologies, such as Artificial Intelligence (AI), by installing fundamental digital tools.However, when it comes to AI, the level of trust between humans and machines will be the deciding element in its success.This paper is a pioneering effort to examine the deployment of AI and how people and technology should interact.This study is limited to three case studies, three digital technologies.To further the study, it is suggested to debate the adaptation of AI on the user's premises, gather more empirical data, and examine case studies from different sectors.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".