Bibliographic record
Abstract
In this thesis, a decision support system (DSS) for selecting the most suitable project delivery systems (PDSs) for capital projects is proposed. Project delivery systems continue to evolve, to meet challenging project objectives. Selecting a PDS is an early project decision, which can greatly affect the project execution process and its outcomes. The proposed DSS encompasses a multi-tiered process; designed on the basis of an in-depth analysis of 15 case studies of projects constructed in the USA and 207 projects in Canada which utilized public-private partnership delivery methods. The selection criteria were developed utilizing related literature and the findings of the analysis of the case studies. The developed system operates in two distinct modes; elimination, first, to narrow the search field, and ranking, second, to find the most suitable delivery method. In the first mode, the suitability of public-private partnership (PPP) is identified and a number of PDSs are eliminated based on a set of key project characteristics. In the second mode, evaluation and ranking of the remaining PDSs are performed using multi-attributed decision method (MADM). The MADM model utilizes relative effectiveness values (REV) of PDS’s in the evaluation process. These values build upon those developed by CII (2003) to account for PDSs and selection factors beyond those considered in the CII study. The proposed DSS is intended for decision makers of owner organizations, and their consultants. It incorporates knowledge about PDSs and their suitability in meeting a set of targeted project objectives. The decision maker provides project-specific inputs including project information and judgments regarding the importance of specific evaluation and selection criteria. An automated software tool was developed to facilitate the use of the proposed DSS. Three case projects were analyzed using the proposed DSS, including one private sector project and two public sector projects. Two of these cases where also analyzed in the CII study. The results obtained by the proposed DSS were identical to those of the CII study, under the same criteria and the same set of alternative DSSs. The two cases were further analyzed to consider the expanded set of PDSs and the developed criteria. In the latter case, the results revealed a more suitable PDS method. This also applies to the third case. In two of the three cases, the selected PDS was recently developed and known as an integrated project delivery (IPD). The developed method, aside from expanding upon the CII study in the criteria and in the number of PDSs, introduces and makes available newly developed PDSs including IPD and the family of PPPs. The developed method is expected to be useful to owners of capital and public projects.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".