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Record W2466668469 · doi:10.1061/9780784479957.045

New Technologies and Applications of a Multi-Sensor Condition Assessment for Large-Diameter Underground Pipe Infrastructure

2016· article· en· W2466668469 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePipelines 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersSyndicat Interdépartemental pour l’Assainissement de l’Agglomération Parisienne
KeywordsLidarMarine engineeringEnvironmental scienceSanitary sewerSoftware deploymentComputer scienceRemote sensingEngineeringGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

This paper describes the development and successful applications of a robust closed-circuit television (CCTV), LiDAR and sonar based pipe inspection system to gather quantitative data for critical underground pipe condition assessment. The system that can be deployed on a remotely operated vehicle (ROV) or on a float produces accurate cross-sectional analysis and sediment volume. This capacity is increasingly critical in large diameter pipes with high level of flow. The system employs a time of flight LiDAR with sub centimetre distance resolution. Results from recent projects are discussed, in detail in this paper. The Townline Road and Gladys Avenue Trunk sewers in Abbotsford, British Columbia, Canada are critical lines in the municipality’s waste-water system. These reinforced concrete interceptors range between 525 mm (20 inches) and 1050 mm (41.3 inches) diameters with highly variable flow conditions. Hard to access, off-street manholes created challenges during deployment. The robust, yet modular SewerVUE multi-sensor pipe inspection system (MPIS) was repeatedly reconfigured during the project to accommodate the challenging site conditions. The sonar results provided accurate sediment volumes and cross sectional restrictions. LiDAR data and a proprietary 4 in 1 visualization module complemented the deliverables. The avenue Lenine combined sewer in Saint Denis, a northern suburb of Paris, France is a critical interceptor in the city’s collection system. This 1400 mm (55.1 inches) diameter reinforced concrete pipe experiences wet weather overflows during extreme rainfall events. The primary objective of the survey was to quantitatively measure sediment volume and distribution within a 2134 m (7646 ft) long section. This paper presents the methodology and the results of the inspection. Advanced pipe condition assessment technologies, such as the CCTV, LIDAR and sonar system described in this paper are cost-effective, and quantitative methods that provide critical information for optimal operation and maintenance of pipe infrastructure. The reported results can also help better refine estimated remaining service life of an interceptor, accurately determine the overall severity of pipe degradation, as well as provide a basis for improved cost allocation and timing of rehabilitation efforts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.466

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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