Key factors shaping post-disaster building damage assessment: insights from the Gaza Strip as a conflict zone
Bibliographic record
Abstract
The accurate and swift assessment of building damage is essential for effective post-disaster restoration, yet this process is often hindered by managerial, technological, financial, and humanitarian challenges, especially in conflict-affected regions. While this study focuses on the Gaza Strip as a case study, the findings are applicable to other regions experiencing similar conflict-induced hazards. The research explores factors impacting post-disaster damage assessment and reconstruction, identifying key barriers to effectiveness and proposing guidelines for improvement. A literature review provided insights into existing challenges, which were further examined through expert interviews. A survey was conducted among site engineers, disaster managers, emergency officers, and project managers, achieving a 78.7% response rate. The findings highlighted that unstable structures, absence of safety permits, and residual hazards were among the most significant challenges, with field circumstances such as the scale of damage and geographical location having the greatest impact. Community participation was deemed less influential. The study recommends standardizing assessment procedures, improving data management, and prioritizing safety measures to enhance rehabilitation efforts and improve the quality of life for affected populations. Future research should refine these recommendations and assess their practical implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".