Le Module Complexe et l'Impact du Granulat sur la Prédiction du Module Dynamique des Enrobés Bitumineux. Application aux Dimensionnements Rationnel et Mécanistique-Empirique
Bibliographic record
Abstract
In a context marked by a budget deficit of the Autonomous Road Maintenance Fund in Senegal (FERA) and the scarcity of road aggregates, marked by exhaustion announced the basalt quarry at Diack, design of durable roads at low cost and enhancement of new materials from Senegal have become necessary. Among the alternatives, quartzite of Bakel is proposed. A good pavement structure design requires a good characterization of the actual behavior of materials used. Rational design with ALIZÉ-LCPC, consider all materials as linear elastic and characterized by its elastic modulus and Poisson's ratio. This is a lack of precision in the design, because the behavior of granular materials is non-linear elastic, while the behavior of the bituminous materials is linear viscoelastic at low deformations. For bituminous materials, Pavement M-E as ALIZÉ-LCPC software uses the laws of linear elasticity. The viscoelastic behavior of asphalt concretes can be characterized by its complex modulus E *, mechanical models or its creep compliance J(t). The elastic behavior of asphalt concretes is characterized by dynamic modulus |E*|. Complex modulus tests are expensive and require revealing much time; therefore prediction models are used in the mechanistic-empirical pavement design (Witczak models). But none of these models takes into account asphalt concretes characterized by metric sieves or the impact of the aggregate in its mineralogical and chemical aspects. In order to take a first step in the consideration of these factors in understanding the behavior of asphalt mixtures, studies were carried out in the ETS-LCMB in Montreal on the characterization of the viscoelastic linear behavior (complex modulus test E*) of asphalt mixtures made with basalt Diack, quartzite of Bakel and limestone of Bandia. The different results obtained, supplemented by studies already carried out on the rough rock classification based on their SiO2 content, helped calibrate the sigmoidal model Witczak and develop a mineralogical model SILICA-ZETA. This model in auto evaluation enables better prediction of dynamic modulus of asphalt mixtures and explains their behavior through the predictor variables. But, it must be calibrated by a new free database. These studies have also shown that the asphalt mixtures made with aggregates of Senegal are thermorheologically simple materials. Quartzite Bakel can make just as basalt Diack of asphalt mixtures conform to the applicable specifications. They have a good fatigue resistance determined by the method of LCMB, but this remains to be verified in the test conditions of NF EN1108-1 standards (10°C, 25 Hz). The overestimation of the life of the specimens by the failure criterion Nf50% on Nf II/III during fatigue testing, does not seem too affect the values of the slope (1/b) of the Sn dispersion and ɛ6. The viscoelastic behavior of asphalt studied is well represented by the analogic advanced models. Compared to the viscoelastic pavement design of asphalt layers, the elastic design overestimates rutting at the top of the subgrade, underestimates the longitudinal deformation and transverse deformation of the bituminous layer and makes substantially well aware of the deflection of the floor. The low circulation speeds impose an amplification of the transverse deformation. Witczak predictive models of the dynamic modulus can be used for asphalt in size characterized by the metric sieve by replacement and optimization of U.S sieve.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".