Development of a probabilistic seismic microzonation software considering geological and geotechnical uncertainties
Bibliographic record
Abstract
Modern seismic risk assessment software includes seismic hazard analysis algorithms or relies on precomputed hazard maps. In both cases, a major step is incorporating the impacts of the local geological and geotechnical conditions. The common practice to address this phenomenon, often referred to as site effect, is to use shear wave velocity (Vs)–depth correlations and a deterministic geological model as a proxy for Vs mapping. The Vs of the top 30 m (Vs30) and often the fundamental site period (T0) are then used as predictors of the potential amplification of seismic shaking. Recognising that uncertainty in local soil properties inevitably affects the seismic site response, a stochastic approach for evaluating Vs30 and T0 is proposed herein, considering a combination of probabilistic Vs–depth correlations and a probabilistic 3D geological model. Monte Carlo simulations are then applied to study the impact of the uncertainties on the seismic site characterisation model. The generated stochastic maps consist of Vs30 and T0 realisations accompanied with spatial distribution of uncertainty as an indication of areas where further fieldwork is needed to improve predictions. The developed methodology is currently being used in a homemade risk assessment software.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".