Impact of Using Price Fluctuation Related Conditions on Construction Projects
Bibliographic record
Abstract
The inherent nature of construction projects is long duration and high cost. Extended project duration leads to escalation of cost. If the project duration is too long, the initial estimation may not be sufficient to recover the actual cost of the contract. To address this problem, Construction Industry Development Authority (CIDA) has introduced a price adjustment method called the “ICTAD formula method for adjustments to contract price due to fluctuation in prices”. Contract documents related to construction projects also provide some provisions to address the issues with material price fluctuations. This study investigated the practices of contractors to minimize the effect of price variation and support of price fluctuation clauses to minimize the impact of price variation. A questionnaire survey was conducted among the professionals to represent CS2 to C5 grade construction companies registered at CIDA. The questionnaire consisted of practices of contractors to minimize the effect of price variation and the impact of using price fluctuation clauses in different aspects. Collected responses were converted to a quantitative value using the relative importance index (RII). In addition, SPSS software was used for the critical review of responses. The results revealed that contractors mostly agree with using price fluctuation clauses to recover the increased project cost due to increased construction input prices. Further, results are evident that using price fluctuation clauses helps to fair risk sharing between the contractor and the client.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".