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Record W4407045339 · doi:10.1061/jbenf2.beeng-7106

Resilience Assessment of Shape Memory Alloy–Reinforced Concrete Coastal Bridges Subjected to Tsunami Loads

2025· article· en· W4407045339 on OpenAlexaff
Jesika Rahman, A. H. M. Muntasir Billah

Bibliographic record

VenueJournal of Bridge Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsResilience (materials science)Reinforced concreteForensic engineeringStructural engineeringGeotechnical engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

The consequences of natural hazards like tsunamis often lead to severe damage to coastal bridges, resulting in socioeconomic loss, delays in rescue operations, and even loss of human lives. It is therefore important to quantify the vulnerability and improve the resilience of coastal bridges subjected to tsunami loading using advanced construction materials. This paper is the first-ever attempt to study the potential of the nickel–titanium shape memory alloy (Ni-Ti SMA) reinforcement in enhancing the resilience of reinforced concrete coastal bridges exposed to tsunami loads. The load modeled included drag, inertia, and slamming components. The impact of time-varying debris load associated with the tsunami waves was also analyzed. Under three representative wave periods, several combinations of wave heights were considered as the variable intensity measures for the fragility and resiliency assessments. The analysis results showed that the Ni-Ti SMA reinforcement in the plastic hinge region allows the piers to sustain higher drifts before significant damage compared to the conventional steel-reinforced piers. Moreover, the SMA-reinforced piers exhibited lower fragility and functionality loss under tsunami loads having wave periods greater than 5.50 s.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
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
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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 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
Published2025
Admission routes1
Has abstractyes

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