Developing an Ontology for Concrete Surface Defects to Enhance Inspection, Diagnosis and Repair Information Modeling
Bibliographic record
Abstract
Facility maintenance requires thorough inspections throughout a facility’s lifecycle to ensure structural integrity and longevity. A significant challenge lies in managing the semantic relationships between various inspection data across different lifecycle phases and effectively representing inspection results. While numerous studies have focused on identifying, analyzing, repairing, and preventing defects, organizing and integrating this information systematically for future use remains unaddressed. This paper introduces the Ontology for Concrete Surface Defects (OCSD), a unified knowledge model that enables stakeholders to access information systematically. OCSD aims to enhance future asset management systems by providing comprehensive knowledge about concrete surface defects, encompassing inspection, diagnosis, 3R (Repair, Rehabilitation, and Replacement), and defect concepts. Although the integration with Building Information Modeling (BIM) standards like the Industry Foundation Classes (IFC) is not undertaken in this study, OCSD provides a foundational framework that can facilitate such mappings in subsequent studies or applications. The methodology includes reviewing existing literature to define relevant concepts, outlining steps for developing OCSD, creating its basic components, and evaluating its effectiveness. The semantic representation of OCSD was assessed through a survey, confirming its ability to clarify concepts and relationships in this field.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".