Cost-effective mitigation strategies for residential buildings in Australian flood plains
Bibliographic record
Abstract
In recent years, floods have impacted many Australian communities.The floods have resulted in significant logistics for emergency management and considerable costs to all levels of government and property owners to undertake damage repair and enable community recovery.These impacts are fundamentally the result of inappropriate development on floodplains and a legacy of high risk building stock in flood-prone areas.The Australian Bushfire and Natural Hazards Collaborative Research Centre project entitled "Cost-effective mitigation strategy development for flood-prone buildings" aims to address this issue and is targeted at assessing mitigation strategies to reduce the vulnerability of existing residential building stock in Australian floodplain area.This paper presents the outcomes of this ongoing project.Key tasks of the project include: (1) a classification of residential building stock, (2) a review of flood mitigation strategies, (3) vulnerability assessment of typical building types with and without mitigation, and (4) benefit cost analyses of all retrofit options for a range of severity/likelihood of flood hazard covering a selection of catchment types.The work will provide information on the optimal retrofit strategies in the context of Australian construction costs and catchment characteristics.The research will also entail experimental testing of preferred materials to ascertain their resilience to flood water exposure.The outcome of this research will be an evidence base to inform decisions about mitigating the risk posed by buildings on floodplains.The information will be targeted to all levels of government, insurance industry and private property owners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".