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Record W7162092103 · doi:10.3997/2214-4609.202578002

Distributed Fibre Optic Sensing (DFOS) for Dam Breach Verification (DBV)

2025· article· W7162092103 on OpenAlexaboutno aff
Dr. Ramji Singh

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsOptical fiberNoise (video)Structural health monitoring

Abstract

fetched live from OpenAlex

Summary The application of Distributed Fibre Optic Sensing (DFOS) in dam safety particularly for Dam Breach Verification (DBV) represents a paradigm shift from traditional, reactive approaches to a more predictive, data-driven framework for structural health monitoring. By leveraging continuous, real-time, and spatially distributed data across the dam body, DFOS enables early detection of anomalies with unmatched precision. It transcends the limitations of conventional point-based instrumentation, offering full-length coverage of critical zones, including foundations, cores, galleries, and downstream filters. The integration of DFOS with SCADA systems and Emergency Action Plans (EAPs) further strengthens its value by enabling automated, actionable alerts in breach or deformation scenarios. Key to this capability is Optical Time Domain Reflectometry (OTDR), which allows for pinpointing the exact location of fibre breaks or deformation zones within a 1to 2 meters of margin, providing crucial lead time for emergency intervention. Moreover, the paper demonstrates how multi-modal sensing through DTS (for seepage), DSS (for deformation), and DAS (for acoustic erosion detection) supports a comprehensive, layered safety system that can detect both progressive and sudden failure mechanisms. This fusion of technologies makes DFOS not just a breach detection system, but a holistic surveillance solution for water-retaining structures. From a design and implementation perspective, DFOS offers versatility and scalability. It can be effectively retrofitted into existing dams during rehabilitation or seamlessly integrated during the construction phase of new dams. Real-world deployments in varied geographies Australia, Sweden, Canada, and the United States have validated its long-term reliability, even under harsh environmental conditions. These projects underscore DFOS’s robustness, high uptime, and resilience in large-scale applications such as tailings storage facilities (TSFs), embankments, and hydropower dams. Critically, DFOS also introduces operational and economic efficiencies. Fibre cables require no active power along their length, are immune to electromagnetic interference, and can serve multiple sensing functions using different interrogator units making the system both durable and cost-effective. Maintenance requirements are minimal, and system redundancy (e.g., dual-loop installations) further enhances reliability. In conclusion, DFOS-based DBV systems offer a technologically mature, field-proven, and highly adaptable solution for modern dam surveillance. Their role as the “last line of defense” is invaluable in emergency management, but their real strength lies in transforming dam safety into a proactive and intelligent discipline. DFOS empowers dam owners and authorities to move from post-failure analysis to pre-failure prevention, fulfilling regulatory mandates, protecting downstream populations, and ensuring sustainable infrastructure management for decades to come. Key Word: #DistributedFibreOpticSensing, #DFOS, #DamBreachVerification, #DBV, #DamSafety, #Dams, #SeepageMonitoring, #StrainMonitoring, #DTS, #DSS, #DAS, #StructuralHealthMonitoring, #EarlyWarningSystems, #FloodRiskManagement, #EmbankmentDams, #RealTimeMonitoring, #InfrastructureResilience, #SmartDams, #OpticalTimeDomainReflectometry, #XSeepT, #HECRAS #EAP, #EmergencyActionPlan, #TSFMonitoring, #GeotechnicalEngineering, #HydraulicStructures, #DamBreach

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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