A Study on Effects of Risk Management in Urban Tunnel Constructing Projects
Bibliographic record
Abstract
Although many studies have been conducted on project management and risk management until now, tunnel constructing projects are not under risk management studies. The focus of this study is to define the risks which are effective on tunnel constructing projects and also the method of configuration, relationships and amount of such risks. Then, the responses of the project and the methods of risk management in tunnel constructing projects will be discussed in this study; in order to get favorite results of project through conducting risk management routines.Tunneling projects consist of complicated events and sophisticated technical systems. So, the risk management must of high importance for managers and engineers involved in such projects. In order to understand the involved risks, some questionings were conducted on tunnel constructing companies. At the end of these questionings, some solutions were proposed to solve the risk problem. In this study, the projects involved in Tehran subway system’s construction were studied.Based on the Standish Group’s report, 40 percent of construction projects don’t come to end and 50 percent of construction projects consume more budget than estimated. Furthermore, about 50 percent of finished projects don’t have the enough functionality. Since covering the most aims and missions of organizations are depicted in operational projects, management and risk control play a vital role in success of projects.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".