Monitoring and detection of cracks in steel girders of bridges using a distributed binary crack sensor
Bibliographic record
Abstract
Bridges are key elements in transportation system. One of the critical deficiencies in aging bridges is formation and propagation of small cracks under cyclic loading. Cracks may form in steel girders due to defects in welding or material in the fabrication time. Over time, cracks may grow into the web of steel girders, propagate spontaneously and finally may result in the failure of the girder. Therefore, to prevent the unpredictable loss of service and associated expenses, it is critical to detect cracks before they reach a length that compromises the safety of the structure. Distributed sensors are required if there are many possible points of crack formation. Available distributed crack monitoring techniques are excessively costly to be applied to typical short and medium span bridges. In this work, an innovative distributed binary crack sensor has been developed. The sensor can be installed on the entire length of steel girders of bridges at a fraction of the cost and is capable of detecting cracks with opening of 0.2 mm or less. The crack sensor is composed of a thin wire- (0.09 mm diameter) and an adhesive. An experimental apparatus has been designed to simulate the crack opening on the web of steel girders and materials have been tested on the apparatus. A Finite Element Model of the sensor has been simulated in ABAQUS to study the effect of different parameters such as bonding properties between wire and epoxy as well as position of the wire in the epoxy on the detected crack opening. The crack sensor has been tested on small-scale girder in the lab in ambient temperature. It also has been tested on the girder in an environmental chamber for two extreme temperatures of -30ºC and +40ºC. The study shows that temperature has minimal effect on the performance of the crack sensor. A Finite Element Model of two typical steel girders from two medium span bridges predicts that a binary cracks sensor 15 cm to 25 cm above the tension flange will detect crack that have grown to a length of 35-40 cm at the threshold of 0.2 mm ABSTRACT ii detection. The FEM analysis also predicts that 35-40 cm long cracks do not compromises the safety of the structure. The distributed binary crack sensor has been field tested for more than a year on a real-scale girder of a bridge in Canada to study the installation procedure and effect of environmental condition on the sensor.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".