Developing an Ontology for Concrete Surface Defects to Enhance Inspection, Diagnosis and Repair Information Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it