Integrating Value Engineering and Context-Sensitive Solutions
Bibliographic record
Abstract
The designers of transportation infrastructures are increasingly using the term “context-sensitive design” (CSD) or the broader term “context-sensitive solutions” (CSSs) to refer to a design process that strives to be more cognizant of its surrounding environment. Transportation infrastructures, especially in urban environments, are part of a much larger urban ecology that consists of a complex set of natural and human-made systems. As such, design guidelines that solely address engineering and safety considerations have proved themselves incapable of delivering street designs that respond to the functional requirements of the multitude of stakeholders within urban environments. Analysis of these requirements is a necessary first step for any successful CSD-CSS. In this regard, value engineering has been identified as a successful tool for product functional analysis. Several phases of value engineering overlap with the guiding principles of CSD-CSS. As such, this paper presents a value engineering framework that can be used for the analysis of the functional requirements of urban streets within a CSD-CSS approach. To place the proposed framework into context, a major transit improvement project in the city of Toronto, Ontario, Canada, was studied. Seven of the main design elements were analyzed against the primary and secondary objectives identified by the value engineering process. Almost all objectives were attained by the design elements selected. The proposed framework and analysis of the case study show that the value engineering methodology can be efficiently used to address the needs of CSD design of urban streets.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".