Fatigue Life Evaluation of the Diefenbaker Bridge Using Structural Health Monitoring
Bibliographic record
Abstract
As bridge infrastructure continues to age, public agencies must reliably determine which structures can remain in service, and which structures require rehabilitation or replacement. Structural fatigue is a common problem for many aging steel structures and its evaluation is one that carries a high level of uncertainty. Structural health monitoring is one technique that infrastructure owners can employ to reduce this uncertainty, thereby allowing them to make the necessary investment with confidence. Structural fatigue occurs when steel components of a bridge are subjected to stress cycles, with most details able to withstand only a limited number of cycles. The challenge in determining the remaining fatigue life of a bridge is the uncertainty in stress cycle history and in-situ structural behaviour. The Canadian Highway Bridge Design Code (CSA S6-14) does not address fatigue life evaluation directly, which creates an even larger challenge for engineers. Structural health monitoring is a technique that engineers and owners can use to reduce this uncertainty because it helps to reveal actual stress levels and cycle counts. Structural health monitoring was used to inform the numerical determination of the remaining fatigue life of Diefenbaker Bridge’s bracing connections to the girder webs. The Diefenbaker Bridge is located in Prince Albert, Saskatchewan, Canada. The 304 meter long, seven span bridge consists of two separate fracture critical superstructures, each comprising a cast-in-place concrete deck supported by two welded steel I-beams. The separate superstructures share a cast-in-place concrete substructure. Given the age of this bridge, and its history of frequent rehabilitation, an understanding of the remaining fatigue life was of critical importance to its owner since asset management plans depended on the outcome. To perform the evaluation, the structure was instrumented with strain gauges, accelerometers, and a weather station. Data was collected for six months and was used to characterize in-situ bridge behaviour (i.e., lateral load distribution, degree of composite action, and dynamic load influence) and to evaluate the bridge’s remaining fatigue life using various methods of fatigue life evaluation, including deterministic methods (including the method outlined in AASHTO), and a probabilistic method. Lastly, fatigue damage was characterized to determine what stress magnitudes contribute the most damage, and which connections are the most heavily loaded. In addition, fatigue damage was computed per girder on a daily and monthly basis. This research demonstrated that costly improvements to the lateral bracing’s connection to the girder webs on Diefenbaker Bridge are not required, and that, under the most conservative scenario, 52 years of fatigue life remain. A strong correlation between the deterministic and AASTHO methods of fatigue life evaluation was found, with the probabilistic method providing a consistently longer remaining fatigue life. By characterizing the fatigue damage accumulated during the monitoring period, identification of which details are the most heavily loaded, on both a daily, and monthly basis, was established. From the six months of data acquired, it was found that the northbound barrier lane is the most heavily loaded lane on the bridge, Wednesday is the most heavily loaded day of the week, and May is the most heavily loaded month. In addition to this, unexpected full composite action and no dynamic load influence was found to exist on the bridge under service conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".