EVALUATING PASSIVE EARTH PRESSURE TRENDS OF LARGE-SCALE PLATE LOAD TESTING OF GEOGRIDSTABILISED AGGREGATE LAYERS OVER CLAY
Bibliographic record
Abstract
The manuscripts presented in this thesis focus on two main topics. The first manuscript describes \nthe procedures and material characterisation of the large-scale plate load testing, while the \nsecond manuscript focuses on relating the large-scale plate load tests to Meyerhof’s punching \nshear bearing capacity theory. The abstracts for the two manuscripts are presented below.\n\nPart 1: Large-Scale Plate Load Testing of Geogrid-Stabilised Aggregate Layers over Clay\n\nGeogrid stabilised aggregate layers are used to increase the bearing capacity of weak subgrade \nmaterial. Applications of these geogrid-aggregate composite layers include, but are not limited \nto, temporary working platforms, road construction, and railway ballast. The inclusion of the \ngeogrid in aggregate creates an interlocked structure that improves the bearing capacity of the \ncomposite layer. In this study, what we believe to be the largest full-scale controlled plate load \ntesting was carried out near Clavet, Saskatchewan, to test various geogrid-aggregate composite \nlayers. A 1m2\nplate pushed stabilised and non-stabilised aggregate pads within a pre constructed \ntrench above a silty clay past their ultimate bearing capacity using a hydraulic cylinder with a \ncapacity of 100 tonnes. Load and displacement were measured directly. The cylinder was \nattached below a moveable steel platform equipped with counterweights to account for the large \nloads required to fail the testing pads. Analyses, including cone penetration testing, \nphotogrammetry, and intensive material characterisation, were done before and after plate load \ntesting.\n\nPart 2: Evaluation of Meyerhof’s Semi-Empirical Bearing Capacity Solution Using Large \nScale Plate Load Testing of Geogrid Stabilised Working Platforms\n\nIncreasing the bearing capacity of aggregate layers placed over soft subgrades by stabilising the \naggregate with geogrids is a relatively novel practice with limited standardized design \nprocedures. This is caused by the growing number of stabilising geogrid products that are \nbecoming available as well as how each of these products will have a unique interaction with the \ngeogrid used in design. The research presented relates large-scale plate load testing results to \nMeyerhof’s widely used punching shear bearing capacity theory of a strong layer overlying a \nweak layer. Mobilisation trends for two different thicknesses of unstabilised aggregate layers are \ncompared to Meyerhof’s originally proposed trend of δ = 0.667φ. Mobilisation trends of the \naggregate stabilised with three different types of geogrid are then determined.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".