A mechanistic and economic analysis of two-lift concrete pavements
Bibliographic record
Abstract
The government of India has embarked on the construction of major highways with concrete pavements to eliminate frequent maintenance of bituminous pavement, damaged by heavy commercial vehicles and moisture. Hence, it is necessary to re-examine the current pavement design with a sound analytical approach. The current practice in the construction of concrete pavement in India is to place pavement quality concrete (PQC) over dry lean concrete (DLC) layer with a bond-breaking layer of 125-micron plastic sheets between the DLC and PQC layers to eliminate possible reflective cracks from the DLC to the PQC layer. Concrete pavement can be bonded to lean concrete (LC) when both layers are laid one after the other with two pavers (‘fresh-on-fresh’ or ‘wet-on-wet’). This type of pavement is also known as two-lift concrete pavement (TLCP), and such pavements were constructed in India during the last three years. No readymade solutions are available to compute stresses in such TLCPs. The objectives of the research are manifolds. Firstly, this article illustrates the analysis of Two-Lift Concrete Pavement (TLCP) using Finite element programming software (ANSYS) with an interface layer CONTA 174, which is able to capture interfacial stresses occurring between layers due to non-linear temperature gradient distributed over the depth of the slab. Then the design of pavements with TLCP has been introduced using the cumulative fatigue damage method. Finally, the cost of construction for TLCP is determined and compared with that of conventional concrete pavement. It was found that the material cost for TLCP is less than that of conventional concrete pavement because the stresses induced due to both load and environmental effects in TLCP are significantly lower when compared to those on the conventional concrete pavement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".