Enhancing Construction As-Built Documentation Using Interactive Voice Response
Bibliographic record
Abstract
This paper utilizes interactive voice response (IVR) technology to enhance progress tracking of projects and as-built documentation. First, possible site events and tracking needs of construction activities have been analyzed based on literature and sample daily progress forms. Accordingly, activity logical-flow diagrams have been developed to guide IVR sessions. Afterward, the IVR technology has been implemented to enhance an existing e-mail-based framework for as-built documentation by integrating a cloud-based IVR service and a customized scheduling application. The IVR features work by either receiving calls from supervisors at any time or by configuring eligible activities to automatically initiate calls to their supervisors. Compared with lengthy e-mails, the IVR sessions are interactive, minimize the questions asked, and allow supervisors to specify site events and any requests for information. Responses are also received instantaneously to update the schedule and visualize the latest as-built information directly on the daily segments of a schedule. The paper contributes to automating site-data collection, designing low-cost voice applications for construction, facilitating bidirectional communication between site personnel and head office, and enhancing project tracking and control.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".