Seismic Hazard Analysis for Proposed Smart City, Ludhiana, India: A Deterministic Approach
Bibliographic record
Abstract
The city of Ludhiana (Manchester of India) is in a phase of rapid development as it has been proposed as one of the smart cities of Northern India. Seismic activities in this part of the country are increasing. In the present study, deterministic seismic hazard analysis (DSHA) of proposed smart city, Ludhiana, has been carried out. Seven tectonic features have been identified as potential seismogenic sources. Each seismogenic source has been assigned a maximum magnitude considering the regional rupture character. For this purpose, seismic data for a period of 525 years has been collected from Indian Meteorological Department (IMD), Delhi, and earthquake catalogue has been compiled for the study region. Ground motion prediction equation (GMPE) developed for Indo-Gangetic region by National Disaster Management Authority (NDMA) of India has been used to assess the hazard. The peak ground acceleration (PGA) values are estimated by considering a grid of 0.025°×0.025° covering the Ludhiana region. Deterministic response spectra has also been developed for 5 major sites of Ludhiana region. Maximum PGA value (PGAmax) of 0.392 g for the study region has been estimated from the study and is found to be on higher side as compared to the IS code of practice. The study is very significant keeping in view resilient structures for upcoming smart cities of India.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".