WAM-ONTO: A semantic framework for water infrastructure asset management
Why this work is in the frame
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Bibliographic record
Abstract
Water treatment plants face growing challenges in managing complex infrastructure assets due to fragmented data systems and poor interoperability between design and operational tools. Traditional workflows require manual re-entry of Building Information Modeling (BIM) data into asset management platforms, leading to inefficiencies and information loss. This paper introduces WAM-ONTO, a semantic framework designed to enhance water infrastructure asset management by leveraging building information modeling, ontologies, and rule-based reasoning. WAM-ONTO integrates the Industry Foundation Classes (IFC4) standard with Web Ontology Language (OWL2) to formally represent asset types, lifecycle properties, and spatial-functional relationships. The framework comprises 996 ontology classes structured into twelve domain-specific categories, enriched with 61 object properties and 89 data properties. Semantic reasoning is implemented using SWRL rules and SPARQL queries, enabling automated classification, risk profiling, and maintenance prioritization. The framework was validated through expert interviews with fourteen domain specialists, consistency checks using multiple reasoners, and performance tests demonstrating 94.2% domain coverage and 91% automated classification accuracy on real-world facility data. Expert validation achieved 96% consensus on practical utility and industry alignment. A Clean-in-Place system case study demonstrates WAM-ONTO’s ability to preserve design knowledge during BIM-to-operations transitions while enabling real-time decision support for maintenance planning and risk assessment.
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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 | 0.000 |
| 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