Developing a system reference architecture for assessing risks of heatwaves in residential buildings using a cloud-BIM environment: a design science research approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose As climate change accelerates, the frequency and intensity of extreme heat events are rising, making it critical to assess and improve the thermal resilience of residential buildings. Current assessment methods are time-consuming, costly and not easily scalable, while often lacking stakeholder engagement or integration with real-time climate data. This study aims to address these limitations by developing a scalable, cloud-based reference architecture that supports the assessment and mitigation of overheating risk in residential buildings. It offers a systematic approach to improving efficiency, automation and user collaboration within climate adaptation planning. Design/methodology/approach The study employs a design science research (DSR) methodology to develop a three-layered reference architecture for overheating risk assessment. The architecture includes a data management layer (building information modelling (BIM), climate and comfort data), a business logic layer (simulation and risk analysis) and an application layer (user interface and decision support). The design was informed by expert input across three evaluation phases and supported by visual tools and mock-up prototypes. Validation was conducted through expert reviews and a strengths, weaknesses, opportunities and threats analysis to assess scalability, technical feasibility and usability. Findings The proposed architecture demonstrates the potential to improve thermal risk assessment efficiency by integrating adaptive comfort models, climate projections and stakeholder-driven workflows. Expert evaluations confirmed the system's value in enabling scalable, automated simulation and visualisation of overheating risk across residential buildings. The mock-up interface supports informed decision-making and usability for non-expert users. The layered architecture enhances transparency, modularity and potential for future integration with digital twins or Internet of Things systems. While not yet implemented, the system offers a strong foundation for future software development and real-world application. Originality/value The originality of this study lies in the development of a system reference architecture for assessing heatwave risks in residential buildings, aimed at enhancing resilience to extreme heat. Unlike previous frameworks focused on energy or general risk management, this architecture integrates BIM, climate data and adaptive thermal comfort modelling into a single, cloud-based platform. It supports automation, user collaboration and scenario-based decision-making. The framework is designed to assist platform developers, engineers and policymakers in mitigating heatwave risks, improving building performance and advancing climate adaptation efforts within the built environment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it