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Record W4409051257 · doi:10.1038/s41598-025-91198-3

A damage zone detection method in concrete hydraulic structures based on multi-frequency ultrasonic characteristics

2025· article· en· W4409051257 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsGeomechanica (Canada)
FundersNational Natural Science Foundation of China
KeywordsUltrasonic sensorComputer scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

To avoid severe threats to the safety of people's lives and property caused by the ultimate collapsing of damaged concrete hydraulic structures and to overcome the technical bottleneck related to the low precision level of conventional acoustic non-destructive testing methods in distinguishing between different structural characteristics of damaged areas, this article focuses on the shortcomings in accuracy and detection capability of current acoustic technologies for damage detection in concrete structures. Different damaged areas of concrete structures exhibit distinct characteristics of frequency acoustic signals, and these multi-frequency ultrasonic characteristics were studied in this research to improve the detection method of damaged areas in concrete hydraulic structures. First, a damage area detection model that can synchronously reflect the non-smooth surface and multi-layer structural characteristics of the damaged area was established based on the real-state characteristics of concrete damage areas, providing a theoretical basis for the fine detection of the internal characteristics of concrete structures. Subsequently, an acoustic response feature function for the damaged area was constructed based on the multi-frequency information of acoustic signals, constituting an acoustic response feature extraction method that can effectively distinguish among multiple reflected echo signals. At the same time, by introducing the concept of the damage area recognition feature quantity, a structural damage area recognition method was formed to effectively distinguish between non-damaged and damaged areas in concrete structures, altogether encircling a complete set of the hydraulic concrete structure damage area detection technology system. Finally, the feasibility and superiority of the proposed method were verified through local and global testing experiments. The results indicated that the method proposed in this paper can improve the accuracy and efficiency of detecting damaged areas in concrete structures. The theoretical error was below 10%. The proposed method exhibited stronger adaptability, providing more effective and accurate diagnostic methods for assessing the current state of damaged areas in concrete structures in practice.

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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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