Advanced Fibre Modelling for Accurate Prediction of Splitting Tensile Strength and Failure Behaviour in Self-Compacting Concrete
Bibliographic record
Abstract
Developing Steel Fibre-Reinforced Self-Compacting Concrete (SFR-SCC) requires a precise balance of fibre properties and content to optimise rheological and mechanical performance while maintaining cost efficiency [1].However, variations in mix constituents raise concerns regarding mechanical behaviour, particularly in strength, durability, and failure mechanisms.The lack of well-defined mix design procedures further complicates achieving consistent and reliable mechanical properties, necessitating advanced predictive tools for mix optimisation and structural performance evaluation [2].Finite element modelling (FEM) offers an efficient solution for predicting mechanical properties and analysing the structural response of SFR-SCC, reducing reliance on costly and time-consuming experimental testing [3].This study presents an automated fibre modelling approach to evaluate the splitting tensile strength and failure patterns of SFR-SCC using FEM and experimental validation.Unlike conventional methods that rely on idealised or manually defined fibre arrangements, this research integrates a Python-based algorithm within ABAQUS to generate realistic, non-intersecting, and randomised fibre distributions in the concrete matrix.In addition, to account for the fibre pull-out response, equivalent stress-strain relationships, derived from an analytical load-slip model, are assigned to steel fibres based on their orientation angles, improving the accuracy of fibre-matrix interaction predictions.To validate the numerical model, experiments were conducted using hooked-end steel fibres at 0%, 0.25%, and 0.5% volume fractions.Slump flow and J-ring tests confirmed excellent flowability, while compressive and splitting tensile strength tests assessed hardened properties.The results showed that splitting tensile strength increased with fibre content, with failure transitioning from brittle fracture to distributed cracking, indicating enhanced ductility.Finite element simulations based on the Concrete Damage Plasticity (CDP) model captured the splitting tensile response, with deviations of up to 2.75% from experimental results, demonstrating the accuracy and efficiency of the proposed modelling framework.The Python-based FEM framework successfully modelled random fibre distribution, while the embedded element approach simulated bond-slip interactions between fibres and the matrix.Numerical results closely aligned with experimental findings, confirming the model's predictive capability.These results highlight how computational modelling can refine mix design parameters, optimising the balance between workability, mechanical performance, and material efficiency.Increasing the fibre volume fraction led to a progressive improvement in splitting tensile strength, demonstrating the positive correlation between fibre content and mechanical performance.The shift from brittle fracture to distributed microcracking confirmed enhanced energy absorption and ductility due to fibre reinforcement.The automated Python-based modelling approach significantly reduced manual pre-processing time, enhanced simulation reproducibility, and facilitated parametric studies on fibre orientation, volume fractions, and bond interactions.These improvements position FEM as a powerful tool for optimising SFR-SCC mix designs, minimising trial-and-error in material testing.
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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".