Development of Accurate Bridge Structure Strain Response Function Due to Temperature Changes Effect
Bibliographic record
Abstract
Monitoring bridges performance is a vital task to ensure their safety and to plan their maintenance operations. The bridges are affected mainly by the traffic loads and the environmental changes. The bridge behaviour can accurately be monitored with the known traffic loads changes; however, the environmental changes effect is crucial and challenging to monitor. The most significant environmental changes effect is mainly produced by the temperature changes. Therefore, this research investigates the temperature changes effect on the concrete bridge behaviour. The objective of this paper is to develop an accurate bridge stain model to precisely represent the temperature changes effect. The current state-of-the-art method for bridge strain modelling is developed in time domain. This paper proposed a frequency response method for bridge stain modelling where the model is developed in the frequency domain. The frequency-domain response method is significantly preferable than time-domain method because the low frequency band of interest can be easily selected in the modelling and the high frequency band (noise band) can be neglected. To examine the performance of the proposed frequency-domain bridge strain response model, the datasets were collected from strain and temperature sensors installed on the Fu-Sui Bridge, China. The frequency-domain bridge strain response model is developed using the Least Squares Frequency Transform (LSFT). The input to the frequency-domain bridge strain response model is the temperature changes and the output is the static strain data. The results shows that the significant strain response dynamic due to the temperature changes is in low frequency band (0.00 -0.15 Hz) with the peak value at 0.05 Hz for Fu-Sui Bridge case study. Moreover, the bridge strain impulse response can be accurately developed form the bridge strain frequency response using the Inverse Least Squares Frequency Transform (ILSFT). The significance of the developed impulse response is that it can be convolved with the temperature changes in time domain to estimate the strain response of the concrete structure due to the temperature changes in real-time mode. Consequently, the strain differences between the estimated strains and the measured strains are used to monitor any anomaly that can be interpreted as a sign of fatigue in the concrete structure under investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".