Reliability-based formulation of building vulnerability to debris flow impacts
Bibliographic record
Abstract
Physical building vulnerability to debris flows is defined as the potential damage degree of buildings for a given debris flow intensity. In this paper, the physical characteristics of both debris flow intensity and building response are considered. Uncertainties in building capacity and debris flow intensity are explicitly quantified to evaluate the damage probability of a typical reinforced concrete building subjected to debris flow impact. Four damage states with clear failure mechanisms are defined using multi-source information from field observations, numerical simulation, and expert experience. Two series of fragility models are proposed based on practical debris flow impact pressure models. Several debris flow intensity measures are investigated. A better indication can be provided using the intensity measure that represents a specific failure mechanism; e.g., impact force (hv2) for force-dominated failures or overturning moment (h2v2) for moment-dominated failures, where h and v are debris flow depth and velocity, respectively. The corresponding fragility surfaces best express potential building damage. The intensity thresholds in the proposed fragility curves are consistent with those in empirical vulnerability curves. The methodology presented in this paper promotes vulnerability assessment using physics-based modeling, leading to a more reliable evaluation of building damage caused by debris flows.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".