Bibliographic record
Abstract
Disputes in the construction industry originate primarily from the occurrence of delays, which are the major causes of time and cost overruns in construction projects. Delays affect project parties, the owner and the contractor. Loss of either anticipated revenue or opportunity cost, on the owner’s side, and increased overhead cost, cost escalation and liquidated damages, on the contractor’s side, are considered as the main impacts of delays on key project stakeholders. Meanwhile, preparing delay claims is a time consuming process that requires extensive resources. Facilitating this process will benefit both project parties. In this regard, this research presents a new systematic delay analysis technique that is capable of evaluating concurrent delays, while considering the critical path of the project. The developed technique precisely allocates delays among the different project parties. The technique is tested against a hypothetical case to highlight its advantages and limitations, in comparison to existing delay analysis methods. In support of the proposed technique, a robust expert system is designed to classify the different types of delays, as well as to offer recommendations on delays or delaying events. The expert system and the proposed delay analysis technique are integrated with a scheduling software which accesses a project database. Likewise, an embedded feature of computing associated costs enhances the capability of the system. The developed system assist the analyst to reduce the time and cost associated with delay claim preparation in a systematic approach. Finally, the reliability of the integrated system is validated through a real case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".