Enhancing project performance by developing multiple regression analysis and risk analysis models for interface
Bibliographic record
Abstract
Interface management (IM) is a main factor in the success of construction projects. The failure to correctly manage interfaces impacts a project’s performance measurements, such as scope control and schedule. Using Alberta’s data, collected using a web questionnaire from a large group of experienced industry experts, three phases are conducted in this research. The first identifies the top ten interface problems that affect IM. The second phase includes enhancing project performance by developing and applying multiple regression analysis models between the underlying interface problem factors and the project performance indicators. The last phase includes measuring the severity of the impact of each IM problem to develop an IM risk analysis model. The results of the multiple regression models indicate that the interface problems caused by the “technical engineering and site issues factor”, the “bidding and contracting factor”, and the “information factor” were the strongest influences on the schedule and cost project performance indicators. The results will assist engineers, architects, and others in analyzing and predicting the project performance. This will in turn serve to minimize project delay and cost and reduce conflict among project participants.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".