A knowledge‐based automated development permit approval process in the housing industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The residential construction industry has a major share in Canada's GDP. In spite of huge spending and technical advances in the residential construction sector, the current permit approval process still adheres to traditional manual permit approval process. Consequently, this has contributed to project delays and increased monetary costs to the stakeholders associated with the process. The research presented in this paper seeks to explore key issues related to the current housing development permit approval process. Design/methodology/approach This paper describes a proposed methodology for the automation of the residential construction development permit approval process. The proposed methodology has been incorporated into a computer system that integrates a knowledge‐based expert system (KBES), database management system (DBMS), and computer‐aided design (CAD). Various concepts related to the database structures, system architecture, process flow and user interfaces are introduced and described in the context of the development permit approval process. Findings This paper presents a knowledge‐based prototype for the development permit approval process that can be customized as per the needs of various cities. A case study is also presented in order to demonstrate the effectiveness of the proposed method and to illustrate the implementation of the research. Research limitations/implications The prototype is application‐independent and may be implemented anywhere in the AutoCAD environment. The research paves the way for the setting of drafting standards for the residential industry. Originality/value Prototype provides significant gains in productivity and accuracy over the current practices by minimizing the redundancies involved in the development permit approval process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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