Schedule Quality Assessment for nD Models using Industry Foundation Classes
Bibliographic record
Abstract
Schedule Quality Assessment for nD Models using Industry Foundation Classes Ashok Kavad, Rahul Dharsandia, Abdelhady Hosny and Mazdak Nik-Bakht Pages 1050-1056 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Schedule assessment models were created to ensure the proper development of a schedule. The checks can be categorized into scheduling-related and constructability reviews. Most of the existing automated models are targeted towards two-dimensional schedules, and not nth-dimensional, despite the emergence of building information modelling in the construction industry. The type, method and relations between stored temporal information for activities in nth-dimensional models differ than the typical two-dimensional schedules. Accordingly, this paper presents the adaptation of the existing schedule quality assessment criteria to evaluate nth-dimensional models, utilizing building information modelling and Industry Foundation Classes (IFC). The paper starts with a comprehensive review of previous assessment models, identifying the major checks performed, detailing out the needed activity information and evaluation techniques. The checks are then categorized as quantifiable and qualitative, to differentiate between the measures that can be fully automated and others which would require expert intervention. Afterwards, the paper presents the methodology for attaining the inputs required for the quantitative measures in nD models. The methodology revolves around using IFC, as a standard data model for storing building and construction data. Accordingly, a technological review was conducted of the existing nD modelling software, to view the capabilities and limitations that could affect the development of a schedule assessment model. Initial Algorithms were developed to measure the wellness of schedule properties such as activity duration, criticality levels and accuracy of relationships. These developed algorithms were then verified by testing them versus different schedules with known errors. Keywords: 4D Modelling; Schedule health assessment; Schedule quality checks; IFC DOI: https://doi.org/10.22260/ISARC2019/0140 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".