Blast Performance of Ultra-High Performance Concrete Beams Tested Under Shock-Tube Induced Loads
Bibliographic record
Abstract
Modern day structures are reaching higher, spanning longer and undergoing new design methods. In addition to regular loads, it is becoming increasingly important to consider the potential risks of intentional and accidental explosions on structures. In the case of reinforced concrete buildings, critical elements such as beams and columns must de designed with sufficient strength and ductility to mitigate against the effects of blast loads to safekeep the public and prevent progressive structural collapse. Recent advancements in structural materials have led to the development of ultra-high-performance concrete (UHPC) with high compressive strength, tensile resistance, toughness and energy absorption capacity, properties which are ideal for blast protection of structures. Combining UHPC with high-performance steels, such as and high strength reinforcement is another potential solution to enhance the blast resilience of structures. This experimental and analytical research program investigates the advantages of combining high performance materials to increase the blast capacity of reinforced concrete beams. The experimental program includes tests on 21 beam specimens, fourteen of which are subjected to extreme blast loading using the University of Ottawa shock-tube, with seven companion specimens tested statically. Parameters investigated include: effect of concrete type (NSC vs. UHPC), effect of steel reinforcement type (NSR vs. HSR), effect of longitudinal reinforcement ratio, effect of fiber type/content and effect of transverse reinforcement on structural performance under static and dynamic loads. The experimental study includes three series having specified material combinations as follows: series 1 (NSC & NSR), series 2 (UHPC & NSR) and series 3 (UHPC & HSR). Each dynamically tested beam specimen is subjected to gradually increasing blast shockwaves until reaching failure. Performance assessment criteria included; maximum and residual displacements, overall blast resistance and resistance to secondary fragmentation. Results show that the specimens detailed with UHPC can resist greater blast loads with reduced mid-span displacement and debris generation when compared to beams built with conventional concrete. The combination of UHPC and high strength reinforcement further enhances blast performance and delays failure as both high strength materials balance themselves for optimum efficiency. Similarly, for specimens subjected to static loading, the use of UHPC increased the maximum load resisted by the beams, although failure mode alters from concrete crushing to rebar rupture. The combination of UHPC and high strength reinforcement further heightens beam resistance, at the expense of reduced specimen ductility. The analytical component of this thesis presents an analysis program called UO Resistance which is capable of predicting structural element resistance curves and conducting a dynamic inelastic single degree of freedom (SDOF) analysis of members subjected to blast loads. Resistance curves generated using UO Resistance were compared to data obtained through static testing and were found to effectively predict specimen response. Similarly, dynamic analysis methods implemented in UO Resistance prove to be effective at predicting specimen response under blast load. Additionally, a sensitivity analysis was performed to evaluate the effect of various modeling parameters on the static and SDOF dynamic predictions of specimen response.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".