Enhancing Request for Information (RFI) Process in Construction through Digitalization and Automation
Bibliographic record
Abstract
The Request for Information (RFI) process is a fundamental component of construction project management, enabling the resolution of ambiguities, clarification of contract obligations, and coordination among stakeholders.Despite its critical role, the RFI process remains fragmented, inefficient, and prone to delays due to decentralized documentation, inconsistent workflows, and a lack of standardized procedures.These inefficiencies contribute to project delays, cost overruns, and increased risks of disputes.This study examines the limitations of the existing RFI process and explores how digitalization and automation can enhance efficiency, accuracy, and transparency.By reviewing ontological models and analyzing reported process deviations, this research identifies common bottlenecks and their root causes.The study investigates the potential of digital transformation toolsincluding Natural Language Processing (NLP), process mining, and AI-driven decision support systemsto automate and optimize RFI workflows.These technologies offer solutions for structured data management, real-time tracking, and predictive analytics to streamline RFI submission, review, and response processes.Additionally, a SWOT analysis framework evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in RFI management, providing a structured approach for industry adoption.The findings highlight the need for a balanced implementation of AI and human oversight to mitigate risks while maximizing efficiency gains.This study contributes to the ongoing discourse on digital transformation in construction by proposing a roadmap for RFI process improvement, emphasizing the role of automation in reducing inefficiencies and enhancing project communication.
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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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".