Human-centered design and development framework for autonomous inspection robot systems in Lean Construction 4.0
Bibliographic record
Abstract
Purpose The construction industry is undergoing a transformation driven by the need to optimize workflow, maximize value and eliminate waste – principles outlined in the transformation-flow-value (TFV) model, widely regarded as the theoretical cornerstone of Lean Construction. Lean Construction 4.0 builds upon these principles by integrating advanced technologies and digitalization to create a more efficient, responsive and human-centered construction process. Within this context, autonomous inspection robot systems hold immense potential to transform the construction industry by automating essential tasks that are often hazardous and non-value-adding. Design/methodology/approach This paper introduces a human-centered design framework for autonomous inspection robot systems, which is validated through a case study, addressing the need for human-centered design, value-driven development, adaptability and information flow management in robot-driven system development. Findings A case study demonstrates the framework's application, showing that the robot inspection system significantly improved usability, enhanced information flow efficiency, minimized human involvement in hazardous inspection tasks and increased value generation. Originality/value The framework integrates principles of human-centered design, lean startup methodology and agile development, guiding developers through four distinct phases: empathize and define, ideate and prototype, develop and deploy and monitor and improve.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".