Advancing urban resilience with modular construction: An integrated sustainability assessment framework
Bibliographic record
Abstract
Given the rapid growth of sustainable construction strategies globally and the importance of resiliency in civil infrastructure, it is crucial to adopt best practices. Modular construction is one such practice and is considered a better alternative to conventional construction in terms of resilience, construction times, resource efficiency, and sustainability. However, the continued expansion of modular construction relies on quantifying and evaluating its sustainability and the purported benefits. This paper develops and checks feasibility through an integrated multi-level decision support framework to empirically evaluate the sustainability performances of single-family residential modular homes. Criteria and indicator development and calculation, benchmark scale establishment, quantitative and qualitative data collection from literature and surveys, and multi-criteria decision analysis are unique aspects of this framework. The results of the two case studies located in the Okanagan region, Canada showed that modular homes perform at a higher level of sustainability than their conventional counterparts across multiple metrics and levels related to environmental and economic factors. The modular homes scored eco-efficiency values of 62.5 and 56.0, respectively and fell into higher performance range. The proposed framework offers flexibility in examining different dimensions of sustainability, providing valuable insights into the key parameters that need to be addressed to enhance overall sustainability. This research, which integrates life cycle thinking and decision-making, helps the construction industry and, municipalities, governments, and policymakers in making informed decisions on the selection of suitable construction methods in city developments and move towards a more resilient and sustainable sector.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".