BIM-based integrated solution for analysis and management of mismatches during construction
Bibliographic record
Abstract
Purpose Prompt and effective responses to incompatibilities between as-designed and as-built drawings prevent cost and time overruns and material waste. This paper aims to provide an efficient framework to handle mismatches between these two models with the least negative impact on the whole project. Design/methodology/approach First, 11 most frequent mismatches were identified through questionnaires. Also, the respondents were asked to determine the mismatches’ roots and solutions and the impact of applying solutions on the whole project. Afterward, the process to present the optimum solution to one of these mismatches was modeled. After running the application programming interface developed in Navisworks software, decision-makers access a form, showing mismatches, their causes and solutions, as well as the solutions’ effect. To finalize the optimal solution, a platform was provided on whether to accept the system solution or to propose an alternative. Findings To empirically validate the reliability of the proposed framework, two projects were investigated. Two different approaches to dealing with the same mismatch occurred in these projects were compared in terms of time, cost and material required. The results showed that addressing the mismatches through the proposed framework can efficiently enhance time, cost and material consumption, in comparison with the traditional approach. Originality/value There is currently no building information modeling-based holistic framework for managing mismatches between as-designed and as-built drawings. The results of this research can help contractors to make the best decision, saving project resources, when setting about a mismatch during construction.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".