DIGITAL RECORDING AND NON-DESTRUCTIVE TECHNIQUES FOR THE UNDERSTANDING OF STRUCTURAL PERFORMANCE FOR REHABILITATING HISTORIC STRUCTURES AT THE KATHMANDU VALLEY AFTER GORKHA EARTHQUAKE 2015
Bibliographic record
Abstract
Abstract. On 25 April 2015, the Gorkha earthquake of magnitude 7.8, severely damaged the cultural heritage sites of Nepal. In particular, the seven monument zones of the Kathmandu Valley World Heritage Site suffered extensive damage. Out of 195 surveyed monuments, 38 have completely collapsed and 157 partially damaged (DoA, 2015). In particular, the world historic city of Bhaktapur was heavily affected by the earthquake. There is, in general, a lack of knowledge regarding the traditional construction technology used in many of the most important temple monuments in Bhaktapur. To address this limitation and to assist in reconstruction and rehabilitation of the area, this study documents the existing condition of different historic structures in the Kathmandu Valley. In particular, the Nyatapola Temple is studied in detail. To record and document the condition of this temple, a combination of laser scanning and terrestrial and aerial photogrammetry are used. By also including evaluation of the temple and its supporting plinth structure using non-destructive evaluation techniques like geo-radar and micro-tremor dynamic analysis, this study will form the basis of a structural analysis study to assess the anticipated future seismic performance of the Nyatapola Temple.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".