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Record W2256234294

Instrumentation and Overall Evaluation of Perpetual and Conventional Flexible Pavement Designs

2009· dissertation· en· W2256234294 on OpenAlexfundaboutno aff
Mohab El-Hakim

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInstrumentation (computer programming)Computer scienceEngineeringSystems engineeringProgramming language
DOInot available

Abstract

fetched live from OpenAlex

The perpetual structural pavement design is currently being explored for usage in Canada and worldwide. This thick structural design can provide many potential benefits but it also has associated costs. Cold Canadian winters and warm summers impact pavement performance and make pavement design challenging. This is further complicated by a heavy dependence on trucks to transport imports and exports. Consequently, most Canadian roads are subjected to rapid deterioration due to high fatigue stresses and rapid growth of the traffic loads.
\nThe concept of a perpetual pavement design was raised to overcome the limitation of structural capacity of the conventional pavement designs. The concept of perpetual pavement was explained and introduced in this thesis and the benefits behind the perpetual pavement construction were studied. 
\nThe Ministry of Transportation of Ontario (MTO) and the Centre for Pavement and Transportation Technology (CPATT) joined their efforts in partnership with Natural Sciences and Engineering Research Council (NSERC), Ontario Hot Mix Producers Association (OHMPA), Stantec Consultant, McAsphalt and others to construct three test sections on the Highway 401. The goal was to monitor and evaluate the performance of three different pavement structural designs. Performance evaluation of test section was performed by evaluating the expected ability of pavement section to withstand the traffic loads and climate impact throughout the design life of that pavement section with minimum damage. The minimum damage is expressed as low vertical pressure on top of subgrade, low shear stresses in the surface course and low tensile strain at the bottom of asphalt layers. Perpetual pavement design with Rich Bottom Mix (RBM) layer, perpetual pavement design without RBM and a conventional pavement design were constructed and instrumented with various types of sensors. These are capable of monitoring the tensile strain in asphalt layers, vertical pressure on the subgrade surface, moisture in the subgrade material and the temperature profile in the pavement sections. The test section construction, sensor installation and preliminary modeling are all part of this thesis.
\nPreliminary structural evaluation was performed by analyzing the three designs using a Mechanistic Empirical Pavement Design Guide (MEPDG) model representing the three pavement designs constructed on the Highway 401. In addition, the WESLEA for Windows software was used to validate the long life performance of the perpetual pavement design. Life Cycle Cost Analysis (LCCA) was also performed for the perpetual and conventional pavement designs to evaluate the cost benefits associated with pavement designs for 70 year analysis period.
\nIn addition, the perpetual Pavement design philosophy for moderate and low traffic volume roads was also examined in this research. This pavement design involved creating a complete comparison and validation of the benefits of using perpetual asphalt pavements versus the conventional pavements in all road types and traffic categories. Structural evaluation of the pavement sections in moderate and low traffic volume roads was performed. In addition, LCCA was implemented to validate the perpetual and conventional structural pavement designs in moderate and low traffic volume roads.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.864

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 head0.026
GPT teacher head0.244
Teacher spread0.218 · 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 teacher head, not a consensus.

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

Citations6
Published2009
Admission routes2
Has abstractyes

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