Engineered Wood in Cold Climate - Application to Monitoring of a New Swedish Suspension Bridge
Bibliographic record
Abstract
Engineered wood is increasingly used in large structures in Europe, though little is known of its behavior in cold climate. This paper presents the structural health monitoring (SHM) system of a newly built suspension bridge with a deck of glulam timber as well as a bond stability study regarding cold climate performance of engineered wood. The bridge is located in Skellefteå in northern Sweden, and it connects two parts of the city situated on opposite shores of the Skellefteå river. In this ongoing study of the timber-bridge, a structural health monitoring system is employed to verify structural design and long-term performance. This 130m-span bridge is monitored using GNSS receivers, MEMS accelerometers, laser positioning systems, wireless moisture content sensors, strain gauges and weather stations. Data from the monitoring systems is analyzed regarding accuracy, complexity, costs and reliability for long time use. Engineered wood application in bridges, sports centers and timber buildings are discussed. Bond stability of glulam structures in cold climate is also examined in a range of experiments ranging from small glued wood joints to full size glulam bridge performance over time. From an engineered wood material point of view, the study is relevant to cold regions such as Scandinavia, Canada, Alaska, Russia, and the northern parts of China and Japan etc. The engineered wood constructions in these areas will be exposed to low temperature in a quite long period each year. The goal is to determine how engineered wood behaves when exposed to temperatures between 20 °C to -60 °C.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".