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Record W4387023887 · doi:10.1111/mice.13102

Assessment of out‐of‐plane structural defects using parallel laser line scanning system

2023· article· en· W4387023887 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

VenueComputer-Aided Civil and Infrastructure Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLaserLaser scanningSTRIPSRendering (computer graphics)OpticsParallelComputer scienceTriangulationLaser diodeLine (geometry)Computer visionDiodeMaterials scienceArtificial intelligenceOptoelectronicsPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

A precise parallel laser line scanning system has been developed to assess the depth of out-of-plane structural defects on concrete surfaces. This system comprises a digital camera, dual line laser diodes, and positioning rigid arms that create a triangulation-based setup. Laser lines are distorted when projected onto an out-of-plane defect. A new image processing algorithm has been devised to extract depth information from the distorted laser strips. Parallel laser lines are used to ensure that imaged laser strips do not intersect, thereby simplifying the depth assessment of defects at different distances and enabling the generation of defect profiles from a single image. The system has been validated through laboratory and field tests, demonstrating its effectiveness and accuracy. Compared to other noncontact measurement techniques, this system stands out due to its simplicity, cost-effectiveness, efficiency, and superior accuracy for long-range measurements, rendering it suitable for on-site scanning of textureless uneven engineering objects.

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)0.000
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 score0.155
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
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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