PRECAST CONCRETE BRIDGE SUBSTRUCTURES: FROM SERVICE PERFORMANCE TO SEISMIC RESILIENCE
Bibliographic record
Abstract
Highway bridges in Canada are rapidly aging and deteriorating due to various environmental factors, resulting in a significant backlog in repair and replacement. To support and advance accelerated bridge construction, this dissertation tackles the multifaceted challenges faced by precast bridge elements and systems (PBES) under both service and extreme load conditions. It focuses on two distinct PBES substructures: emulative piers, which replicate the behavior of cast-in-place structures, and non-emulative piers, which use controlled rocking to minimize damage. The goal is to develop resilient PBES substructures that not only withstand seismic forces but also adapt to evolving environmental and transportation demands. The research begins with a seismic risk assessment of conventional cast-in-place highway bridges in a designated case study area, incorporating the compounded effects of chloride-induced corrosion and climate change. This analysis reveals varying degrees of progressive earthquake-induced damage across regions over time, emphasizing the critical need for PBES to address the growing risks and maintenance challenges associated with seismic and environmental deterioration. With this critical need for PBES established, the thesis then focuses on the seismic design of PBES substructures. For emulative piers, finite element analyses validated against experimental data led to the development of a strut-and-tie model for precast column-to-pile shaft assemblies. This model, which predicts force transfer and strain distributions, was used in a parametric study that indicates that enhancing the transverse reinforcement ratio is particularly effective in preventing prying-action failure in pile shaft foundations. Subsequent experimental investigations demonstrated that employing ultra-high-performance concrete (UHPC) in pile shafts not only prevents prying-action damage but also reduces the required dimensions and reinforcement by 13.3% and 25%, respectively, without compromising overall seismic performance. For non-emulative piers, the thesis presents seismic design approaches for post-tensioned (PT) rocking piers. The study develops explicit analytical equations for viscous dampers and genetic programming-derived models for ED bars, and validates these design frameworks through nonlinear response history analyses, achieving displacement demand predictions within acceptable margins. Addressing serviceability concerns under disruptive transportation technologies—specifically automated truck platooning—the thesis develops a reliability analysis framework to evaluate its impact on PBES substructures. By incorporating dynamic vehicle-bridge interaction and soil-structure uncertainties, the framework identifies critical thresholds for platoon operation parameters to prevent excessive axial loads and settlement. The contributions of this thesis include quantifying the impacts of climate change and corrosion on elevated seismic damage risks, developing innovative seismic design approaches and tools, experimentally validating UHPC-enhanced designs, and performing reliability analyses to characterize the effects of truck platooning on service performance. These contributions to PBES can address the critical need for rapid bridge construction and replacement while ensuring infrastructure remains resilient in the face of intensified environmental stresses and evolving transportation technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".