Digital Transformation of Construction Projects with an Advanced Agile Tool for Deformation Risk Management
Bibliographic record
Abstract
The Agile Monitoring Tool (AMT), integrated within the Smart Infinity Dimensions (S∞D) modeling platform as part of the Digital Transformation Project Management Platform, enhances safety and efficiency in large-scale construction projects by combining agile risk management with structural health monitoring (SHM). Traditional SHM systems monitor structural changes but often lack agile risk management, safety measures, and preventative safety alert functions. AMT addresses this gap by embedding agile risk management and real-time alerts, notifying key stakeholders to support informed decision-making. Through a five-stage process—identifying risks, notifying stakeholders, coordinating teams, allocating resources, and managing data—the AMT provides construction teams with actionable insights, potentially preventing up to 70% of onsite accidents by addressing risks proactively. While pilot testing indicates substantial accident prevention capabilities, further validation across diverse construction projects is necessary. Developed from six Calgary-based projects, the AMT leverages IoT-enabled Real-Time Monitoring and 3D modeling within the S∞D platform to monitor and optimize resource allocation, reduce energy consumption, and minimize waste and rework costs. Pilot testing highlights AMT’s effectiveness as a scalable, cost-efficient tool for high-stakes construction projects requiring rigorous safety management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".