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Record W4403766025 · doi:10.3390/s24216856

Onsite Seismic Monitoring Behavior of Undamaged Dams During the 2023 Kahramanmaraş Earthquakes (M7.7 and M7.6)

2024· article· en· W4403766025 on OpenAlexaff
Alemdar Bayraktar, Mehmet Akköse, Carlos Eiji Hirata Ventura, T.Y. Yang, Emin Hökelekli

Bibliographic record

VenueSensors · 2024
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSeismologyAftershockGeologyForeshockFault (geology)CrestGeotechnical engineering

Abstract

fetched live from OpenAlex

On 6 February 2023, two major earthquakes struck Türkiye, with their epicenters in the Pazarcık (M7.7; focal depth: 8.6 km) and Elbistan (M7.6; focal depth: 7 km) districts of Kahramanmaraş city. Most of the dams in the earthquake region remained structurally safe and stable. However, 17 dams in Türkiye and 1 dam in Syria were damaged during the 2023 Kahramanmaraş earthquakes. The main objective of this study was to better understand the real seismic behaviors of the dams during the two mainshocks and significant aftershocks. An earthfill dam, a concrete-faced rockfill dam (CFRD), and a roller-compacted concrete (RCC) dam constructed in the disaster area were selected to identify the real seismic behaviors of different types of dams during strong earthquakes. Acceleration records measured at the crest, right and left abutments, and foundations of the selected dams during the 2023 Kahramanmaraş earthquakes were taken into account to determine the real seismic behavior of the dams before, during, and after the earthquakes. The results of this investigation provide valuable insights into the real seismic behaviors of different types of dams in the vicinity of fault lines during strong earthquakes.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.214
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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