Developing a grading tool for sustainable design of structural systems in buildings
Bibliographic record
Abstract
Construction is known for consuming large quantities of raw materials and high amounts of energy. In 2018, the construction industry was responsible for 6% of global energy consumption, 11% of global CO2 emissions, and approximately 36% of the total waste in the European Union. These drawbacks are just a part of the gap between the construction sector and Sustainability, which can also be perceived as challenges to the industry and demands for new and innovative strategies to increase Sustainability. For example, recent efforts of EcoDesign on structural systems show a trend in the importance of materials efficiency, durability, adaptability, and reuse. This thesis aims to create a set of guidelines that will help designers and other construction stakeholders apply Design for Deconstruction and Adaptability DfD/A principles to increase the knowledge of how structural design and structural systems in buildings can be designed to promote Sustainability. For this purpose, a grading tool to assess structural systems based on the ISO 20887 was developed. The general methodology for this research was adapted from Design Research Methodology with a particular focus on the Product Development approach for the tool development. A literature Review was conducted in both scientific and grey literature to identify relevant information and current efforts on sustainable design of structural systems and application of DfD/A principles on the construction sector. Three additional methods for data collection were used: (1) questionnaire for identification of customer needs and expectations, (2) benchmarking to identify similar tools, strategies, and certifications systems that include sustainability performance in buildings; and (3) workshops with the purpose to rate the usefulness quality of the tool based on the application of the tool by potential users in different case studies. A ready-to-use computer-based EcoDesign tool was developed. The assessment performed by this tool consists of an indicator system of DfD/A strategies to enhance sustainable development by improving material efficiency and stimulate a circular economy in the construction sector. A top-down approach was used for the concept generation, which starts with the ReBuilding Index as an indicator of sustainable performance for structural systems. This index is based on five categories defined on the relationship of the DfD/A principles with the design process of the structural system. A total of 20 principles are distributed in these categories, defined by 54 strategies to reach the goal of the principles. The tool was tested by 11 potential users with different roles in the construction sector. Five case studies were selected to grade the design of five different typologies of structural systems. The usefulness quality of the tool was evaluated based on indicators of usability, utility, and user experience. It was found that developing the tool based on DfD/A principles and the ISO 20887 gave the tool a solid theoretical background and a flexible structure that can be used for sustainable design or as part of an extensive framework of certification systems or ecolabel programs. The tool accomplishes the goal of grading and helping to improve the structural design. However, during the evaluation of the tool, many barriers and difficulties of application were found. Therefore, these findings and obstacles are instead identified as challenges and turn them into opportunities for improvements in future versions of the tool.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".