FIDIC claim analysis during COVID-19: lessons from Saudi Arabia
Bibliographic record
Abstract
As a response to the global challenges posed by the COVID-19 pandemic, the construction industry, particularly in Saudi Arabia, faced unprecedented disruptions, with economic contractions and shifts in project dynamics. The main motivation behind this research is the fact that, in building projects under Federation Internationale des Ingenieurs-Conseil (FIDIC) contracts, claims can recover up to 25% of the initial contract value. The lack of comprehensive studies on such claims necessitates the development of a robust claim analysis tool to assist decision-makers in resolving disputes arising from COVID-19-related impacts in the Saudi construction industry. This research aims to provide insights into the root causes and impacts of claims, emphasising their relevance in the context of the coronavirus pandemic within Saudi Arabia. The proposed claim flowchart depicts the development process of the claim throughout project construction, and it encompasses five major steps: notification of intent to sue, claim reports, claim exhibits, audit by the supervising engineer, and owner feedback. By focusing on the intricacies of FIDIC contracts in the wake of the COVID-19 pandemic, this research aims to enhance understanding and facilitate better dispute resolution in the Saudi construction sector.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".