Perceptions of Robotic Inspections for Confined Spaces in Lean Construction: a Qualitative Study
Bibliographic record
Abstract
This qualitative study investigates industry professionals' perceptions of robotic inspections for confined spaces within the framework of Lean Construction 4.0 with a focus on facility maintenance.Confined space inspections are crucial for safety and asset integrity but are often associated with risks, inefficiencies, and high costs.Robotic inspections offer a potential solution, aligning with Lean Construction 4.0 principles that integrates lean principles such as eliminating waste, respect for people, along with technology as a means to an end.Through a focus group with ten experienced facility maintenance professionals, the study explored current practices, challenges, expectations, and hesitations regarding robotic inspections.Findings revealed that while participants recognized the potential of robots to enhance safety, accessibility, and data quality, they also expressed concerns about sensor reliability, data security, cost, and integration with existing workflows.These concerns resonate with previously identified barriers to sensor adoption in construction.The study highlights the need for human-centered design, robust and reliable technology, and seamless integration to successfully implement robotic inspections.Future research should focus on addressing these technological and human factors to advance Lean Construction 4.0 goals and realize the full potential of robotic inspections in creating safer, more efficient confined space inspection processes.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".