State-of-the-Art Review of Construction Performance Models and Factors
Bibliographic record
Abstract
Measuring and assessing construction project performance on an ongoing basis is an important part of management and control of a project. Described in this paper is the current state-of-the-art of research on prediction and explanation of construction project performance gleaned through an extensive literature search that identified 122 relevant articles published over the last 20 years. This review was carried out in support of an ongoing research program that seeks to embed within a decision support system a transparent reasoning schema that operates on fundamental relationships amongst influencing factors as well as user-defined, experience-based hypotheses for explaining project performance. Findings from the review are presented in two tables that identify performance measures treated (productivity, time, cost, scope, quality, safety, project success and others), level of analysis (overall project, work package, individual activity), and factors that affect performance outcomes. The paper concludes with a discussion of the findings in terms of areas of consensus, knowledge gaps, and steps to be pursued to develop a robust and practical schema for interpreting project data in order to explain the basis for performance to date.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".