Optimising Design of Lime Stabilised Temporary Working Platforms
Bibliographic record
Abstract
Working platforms are temporary geotechnical structures that provide stability to heavy plant on construction sites.Traditionally made from granular unbound material of sufficient thickness, platforms are implemented where the natural ground is not strong enough to support imposed loads.To reduce the depth of minimum required fill, hydraulically bound materials (HBM) can be used.However, there is no design guidance on HBM working platforms as any available methods were developed for purely granular material.This paper considers the case of HBM platforms of varied thicknesses made from lime treated Mercia Mudstone (MMG).The platforms were designed under the industry approved methods and the outputs were analysed using Discontinuity Layout Optimization software.The analysis included comparison between the bearing capacity of granular platforms and HBM of different strength parameters.Results showed that the industry design methods are heavily reliant on the frictional angle of platform material, and they could not properly account for strength of HBM which mobilise substantial strength through cohesion.Although the granular platform design obtained through these methods aligned well with the DLO analysis, they were found to underestimate bearing capacity of HBM platforms when compared to the software.Further DLO analysis showed that the granular platforms had much lower bearing capacity than that of HBM.In the scenarios considered, even adding 0.75% of lime had the potential to decrease the required platform depth to 0.1m (although such a large reduction is not recommended with the design guidance limit advised as 0.3m), compared to 0.7m which would be required if a granular platform was used.It is concluded that future work into the subject with the use of additional analytical software method, while considering the strength of the subgrade as another variable, would give stronger understanding of how HBM platforms could be designed to the greatest benefit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".