Improved Target-Value Design Approach through the Integration of Environmental Performance and Reliability Theory
Bibliographic record
Abstract
Stakeholders in the construction industry continually strive to improve processes at all levels in order to achieve better performance while remaining competitive and meeting client demands. The recent and rapid level of industrialization in the construction industry necessitates stronger integration into the practice of design, planning, and management. Moreover, it is essential to understand the impact of management decisions on the performance of the systems in a building—such as architectural, structural, and mechanical—in order to achieve a high level of compatibility among the various systems in a building throughout the lifecycle of the project. However, such integration and understanding is not supported by the current practice of design in the construction industry. Thus, it is important to develop the concept of target-value design (TVD), an adaptation of Target Costing for the construction industry, in the following aspects: (1) extend the concept of values from a cost-centric system to a multiple-value system, such as incorporating cost, time, and energy consumption into the evaluation equation; (2) incorporate the performance of systems in the building, accounting for the interdependencies amongst the components of the systems, and the effect of the values on the performance; and (3) provide a method for design assessment that increases the likelihood of meeting client values, achieving a high level of performance for the building systems. Thus, this paper proposes a framework that consolidates these three aspects to provide a decision-support tool that helps decision makers in the construction industry to ensure that the end-product meets stakeholder requirements (e.g., cost), while at the same time the building systems reach more efficient performance as one holistic system.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".