Bibliographic record
Abstract
Continuous construction site monitoring and resource tracking can assist in ensuring workers’ safety, efficient materials handling and inventory tracking, timely use of resources, and theft prevention. Traditionally, on site data are collected manually, which is an error-prone, costly and time consuming task, particularly in large scale projects. Automated tracking can improve the management of construction projects through effective site monitoring. Based on the comparative evaluation of remote sensing technologies applicable to tracking conducted in this dissertation, Ultra-Wide Band (UWB) technology is shown to be efficient under dynamic characteristics and the harsh environment of construction sites. The use of UWB tracking systems for construction site applications has attracted the attention of researchers in recent years. However, to enable an effective application, it is important to establish the accuracy of its location estimation and its performance under suboptimal conditions of construction sites, which has not been systematically addressed in previous studies. Expanding on previous research, this dissertation presents a systematic approach towards assessing the accuracy of UWB Real-time Location Estimation System (RTLS) in both static and dynamic modes under conditions that can commonly occur on construction sites. A number of variables affecting the performance of the UWB technology in construction sites were identified that have not been addressed or conclusively treated previously; namely: the presence of metal objects that reflect UWB signals; best signals being blocked by other objects; tracking metal objects; removing the timing cables; tracking multiple tags simultaneously; decreased number of receivers; tracking resources with increasing velocities; tracking multiple tags to identify the orientation of the resource and tracking static and dynamic resources simultaneously. Series of experiments were conducted in which the impact of each condition was simulated by changing a variable. Each experiment was designed to reflect a scenario that commonly occurs on indoor construction sites. The experiments were carried out in two different environments, a laboratory and an active workshop. The accuracy and the relative error of location estimations were calculated for each experiment and the effects of influential variables were quantified. In this context, representative correlations were established between the accuracy and different influential variables. For all the experiments carried out here, more than 96% of the measurements in both 2D and 3D achieved an accuracy of less than 1 metre. On the basis of the results achieved in this work, recommendations have been provided for effective utilisation of the UWB technology in detecting objects and resources on construction sites. The findings of this research are essential to assess the feasibility of using UWB RTLS for different construction site applications, such as material handling and safety management.
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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".