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Record W4409607306 · doi:10.3390/civileng6020023

Energy Dissipation Technologies in Seismic Retrofitting: A Review

2025· review· en· W4409607306 on OpenAlexaff
Mohamed Algamati, Abobakr Al-Sakkaf, Ashutosh Bagchi

Bibliographic record

VenueCivilEng · 2025
Typereview
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConcordia University
Fundersnot available
KeywordsDissipationRetrofittingSeismic retrofitEnergy (signal processing)Architectural engineeringEngineeringStructural engineeringComputer scienceEnvironmental sciencePhysicsReinforced concrete

Abstract

fetched live from OpenAlex

In order to ensure the safety of existing buildings constructed many years ago in zones with high seismicity, it is very important to consider and apply retrofitting measures. The seismic retrofitting of buildings can be achieved by techniques such as increasing the stiffness and ductility of the building and reducing the seismic demand. Energy dissipative devices such as various types of dampers are among the most popular and widely studied devices for improving the performance of buildings exposed to earthquakes. This paper presents a systematic literature review of the seismic retrofitting of existing buildings using energy dissipating devices. More than 230 journal and conference articles were collected from three well-known scientific resources published from 2010 to 2024. The main classification of papers considered was based on energy-dissipating devices employed for retrofitting goals. According to this analysis, there is a vast number of energy dissipative devices and design methods studied by scholars, and energy dissipation based on friction, viscous, and hysteretic mechanisms are the most useful for dampers. On the other hand, only relatively few articles were found about seismic loss assessment and the economic aspects of buildings retrofitted with the proposed damping tools.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations6
Published2025
Admission routes1
Has abstractyes

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