Ontology for Linking Delay Claims with 4D Simulation to Analyze Effects-Causes and Responsibilities
Bibliographic record
Abstract
Visualizing and analyzing the specifics of delay claims in relation to effects and causes and assigning responsibility are a challenge for attorneys, jurists, and judges. Four-dimensional (4D) simulation can be considered as a part of a claim management system for representing the responsibility and impact, and can be used as the main scheduling method of claims resolution. Building information modeling (BIM), 4D simulation, delay effects and causes (DEC), and claims are knowledge domains with active research in the construction industry, which are individually described in the literature using taxonomies and ontologies. However, there is a gap in integrating these ontologies in a more formal and overarching ontology-based approach to grasp essential concepts such as liability, causality, and quantum in a delay claim using 4D simulation. This article proposes a new method for using 4D simulation for visual analytics of delay claims based on an integrated ontology (called Claim4D-Onto), which includes a taxonomy of the quantum, causality, and assigned responsibility. A case study is used to demonstrate the benefits of the proposed method. This method can provide a promising multidisciplinary tool for quicker and fair settlement of construction delay claims by facilitating hearing procedures and catalyzing pretrial negotiations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".