Role of Artificial Intelligence in the Construction Industry – A Systematic Review
Bibliographic record
Abstract
Artificial intelligence (AI) is crucial in promoting Industry 4.0 worldwide. AI has the potential to revolutionize the engineering and construction industry by automating tasks, improving project efficiency and accuracy, and enabling new capabilities. One application of AI in engineering and construction is in the design and planning phase of projects. AI algorithms can analyse data from previous projects and make recommendations for optimal designs, materials, and construction methods. This can lead to cost savings and improved project outcomes. AI can also be used in the construction phase to assist with surveying, quality control, and equipment maintenance tasks. For example, drones equipped with AI can survey construction sites and generate accurate 3D models, which can be used for progress tracking and identifying potential issues. Another area where AI can have a significant impact is in operation and maintenance of buildings. AI-powered building management systems can optimize energy usage, detect and diagnose equipment malfunctions, and predict maintenance needs. Overall, integrating AI into engineering and construction can improve project efficiency, reduce costs, and increase the safety and reliability of projects. However, it is essential to consider the ethical implications of using AI in the industry, such as potential job displacement and the need for proper training and oversight.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".