Calibration of 2D Finite Element Method Rockbolt Models to Fiber-Optic Axial Load Testing Data for Grouted Rebar Rockbolts
Bibliographic record
Abstract
ABSTRACT: In modern numerical modelling software, several rockbolt models are available to practitioners for the design of tendon rock support in underground excavations. In this research, two applicable rockbolt models for the modelling of grouted rebar rockbolts are implemented in the 2D Finite Element Method (FEM) modelling program RS2, and compared to the behaviour of fiber-optic instrumented grouted rebar rockbolts in axial pull testing by Forbes (2020). Rockbolt models are assigned strength and stiffness properties based on manufacturer specifications and parameters found in the literature, and model results are compared to Forbes (2020) testing data to assess the fidelity of the rockbolt load development and deformation response. Subsequent model calibration and sensitivity studies are performed, demonstrating considerations for the selection of an appropriate bolt model and bond interface parameters for the design of rebar rockbolts, where pre-yield rockbolt behaviour is of focus. 1. INTRODUCTION Grouted rebar rockbolts are widely used in civil and mining engineering to control rockmass displacements and improve excavation stability (Kaiser et al. 1996, Hoek 2007). Grouted rebar support is coupled to the rockmass using a cementitious or resin grout material, and generates shear resistance to movement primarily through mechanical interlock between the bolt and the grout, and the grout and the rock. Grouted rebar rockbolts are classified as continuously mechanically coupled support (Windsor 1997). The numerical simulation of tendon rock support for excavation design can be achieved using material models or structural elements. Structural elements are practical for large-scale problems, as material models require finer mesh that is less computationally feasible for most larger-scale excavation design problems. A multitude of structural elements have been developed across industry numerical modelling programs (e.g. Jalalifar and Aziz 2012, Swoboda and Marence 1991, Marence and Swoboda 1995), incorporating different combinations of parameters to address material and interface strengths and stiffnesses. To properly represent the functioning mechanisms of a rockbolt in a numerical model, an appropriate rockbolt model and appropriate input parameters for the bolt materials and interfaces must be selected. Such parameters control how load is transferred between the rockmass and the modelled support element.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".