Analysis of Three-Party Contracts and Their Impact on Project Costs and Timelines: A Case Study of Construction Delays
Bibliographic record
Abstract
Construction projects require careful planning, activity sequencing, and prioritization to establish an effective schedule. In developing countries, where significant capital is allocated to infrastructure, successfully executing projects within time and budget constraints is critical. However, large-scale construction projects often experience delays and cost overruns, extending timelines and increasing expenses. Analyzing the causes of these issues, particularly in traditional design-bid-build (DBB) contracts, requires a systematic approach. This study investigates key factors contributing to time and cost overruns in projects using the conventional three-party implementation method (client, consultant, contractor). A case study built under a DBB contract identifies and ranks reasons for delays and cost increases. Data were collected through surveys of stakeholders, including employers, consultants, contractors, and government officials. The findings highlight primary factors influencing delays and overruns and provide recommendations for mitigating these issues in future projects. By examining stakeholder interactions and identifying inefficiencies, the study offers strategies for reducing delays and improving cost management in construction.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".