Reliability-Based Hybrid Data Fusion Method for Adaptive Location Estimation in Construction
Why this work is in the frame
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Bibliographic record
Abstract
Materials tracking and locating, which can be accomplished through various technologies and data sources, are key elements affecting construction productivity. The need for developing fundamental methods to take advantage of the relative strengths of each technology and data source while dealing with their limitations motivates the development in this paper of data fusion methods for improving materials location estimation. Particular attention is paid to situations in a construction environment in which radio-frequency identification (RFID) tags are attached to each piece of material, and the materials may be repeatedly moved around the site. The construction dynamics, the high noise ratio, and the limitations of the utilized sensing systems result in imperfect data that is imprecise and uncertain. A key challenge is using this imperfect data to improve accuracy and precision while maintaining cost-effectiveness and scalability. To address this issue, a hybrid data-fusion method was developed to increase confidence, accuracy and precision, and add robustness to measurement estimates. This hybrid method leverages evidential belief reasoning and soft computing techniques. The experimental results show that the hybrid fusion method outperforms the traditional methods in data fusion for location estimation. This study has successfully addressed the challenges of fusing data from a range of simple to complex sensor sources within a very noisy and dynamic construction environment. The results presented in this paper indicate that the proposed method has the potential to improve location estimation and to be robust to measurement noise and future advances in technology.
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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 | 0.001 | 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 it