A knowledge‐based automated development permit approval process in the housing industry
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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".