SEISMIC DESIGN AND BEHAVIOUR OF PRECAST CONCRETE CONNECTIONS FOR ACCELERATED BRIDGE CONSTRUCTION
Bibliographic record
Abstract
Accelerated Bridge Construction (ABC), which utilizes precast concrete elements, has emerged as a viable alternative to conventional cast-in-place (CIP) methods by reducing construction timelines and enhancing quality control. The success of ABC in seismic regions depends on the performance of precast connections, which is a key focus of this thesis. Precast connections in ABC range from emulative systems, which mimic CIP behaviour, to non-emulative systems, which incorporate controlled rocking for minimized damage. This thesis examines one representative connection from each category: member socket connections (MSC), referred to as MSC piers (emulative), and segmental post-tensioned precast concrete (SPPC) piers (non-emulative). These two systems have different levels of existing knowledge. For SPPC piers, this thesis focuses on defining engineering demand parameter (EDP) drift ratio limits through genetic programming (GP)-based predictive equations, establishing the onset of four damage states. A two-tier performance-based seismic design (PBSD) framework is subsequently developed by integrating displacement-based design and fragility analysis to ensure seismic performance objectives are achieved at both the component and system levels. The framework’s effectiveness is validated through a comparative case study of SPPC piers and conventional CIP piers. For MSC piers, the experimental program was divided into two sets of tests—one at the connection level and another at the pier component level—each targeting a distinct damage mechanism within the MSCs that connects precast columns to spread footings. Test data from connection-level side shear load tests informed the development and validation of a finite-element model that accurately simulates capacity and damage modes under direct shear loading. Meanwhile, quasi-static cyclic tests at the pier component level demonstrated the effectiveness of ultra-high-performance concrete (UHPC) in mitigating diagonal shear damage from prying action, permitting shorter embedment lengths while maintaining connection integrity. Overall, the work presented in this thesis advances the seismic design of precast concrete bridges by providing quantitative EDP limits, a systematic PBSD framework, and experimental validation of precast concrete connections under seismic loading. These outcomes support the broader implementation of ABC in Canada for modern bridge infrastructure in seismic regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".