Towards Developing an Ontology for Earthwork Operations
Bibliographic record
Abstract
In a typical construction project, a significant amount of information is communicated to various stakeholders at different phases of the project lifecycle. The communication of this information tends to be informal and ad-hoc in the majority of the cases, which makes it more susceptible to loss of information or misinterpretation. Earthwork operations, which are one of the main operations of construction projects, also struggle with the challenge of effective information communication. There is an apparent shortcoming regarding the unified structure for data and information exchange in this domain. The existing models and ontologies do not address the explicit semantic representation of the earthwork operations. Accordingly, there is a need for a knowledge model to formalize the communication of information in an efficient manner. An ontological model can be used to organize the domain knowledge so that it can be utilized and reused by the stakeholders, e.g., project managers, designers, coordinators, equipment operators, etc. This paper purposes a framework to develop ontology for earthwork operations to support and enhance data exchange and communication among different stakeholders in the project. The main objectives of this paper are (1) to formalize the knowledge in the earthwork domain, and (2) to build an ontology that captures this formalization. The ultimate result of this ontology, which is demonstrated by means of a case study, is to facilitate the development of data standards that can be shared in actual projects to accelerate project execution.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".