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Record W3008352864 · doi:10.1061/9780784482810.093

Common Geotechnical Design Challenges for Solar Power Plant Development in the USA and Canada

2020· article· en· W3008352864 on OpenAlexaboutno aff
Bruno Mendes, Eric Ntambakwa, Hao Yu, Matthew A. Rogers

Bibliographic record

VenueGeo-Congress 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsPilePhotovoltaic systemFrost heavingFoundation (evidence)Civil engineeringEngineeringFrost (temperature)Environmental scienceGeotechnical engineeringSolar powerEngineering design processForensic engineeringPower (physics)MeteorologyMechanical engineeringElectrical engineeringGeography

Abstract

fetched live from OpenAlex

By 2023, global utility-scale solar photovoltaic (PV) installations are expected to reach almost 1,000 GW. Ground-mounted solar PV racking systems typically consist of a steel structure supported by drilled or driven pile foundations. Due to the relatively light-weight nature of solar racking installations, environmental conditions such as hazards due to frost action are of critical importance for PV ground-mounted systems. Pile uplift due to adfreeze stresses from frost action typically controls the foundation design for projects in the northern portions of the United States and in Canada. This paper presents a discussion of important considerations for geotechnical design for utility scale solar PV racking systems, with a focus on frost action risk, typical evaluation methods, design recommendations, and mitigation options for impacted sites. The important role of a typical design process including performing appropriate geotechnical investigations and incorporating results of pile load testing for optimization of design are also discussed.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.201
Teacher spread0.175 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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