Causes of Defects Associated with Tolerances in Construction: A Case Study
Bibliographic record
Abstract
Defects associated with dimensional and geometric variations (tolerance issues) are among the most costly and recurring defects in construction projects, yet the identification and mitigation of the causes of tolerance issues appear to be lacking in the construction industry. To enable the development of widely acceptable solutions for the perennial challenge of tolerance management, a more in-depth understanding of the causes of tolerance issues should be established. The aim of the research presented in this paper is to identify the potential causes of tolerance issues in construction based on a literature review and empirical studies. This research uses a case study approach. The empirical data are collected through direct observations, group interviews, semistructured interviews, and document reviews. Having triangulated the findings, a list of 18 potential causes was derived for the 11 observed tolerance issues in two case study projects. The contribution of this paper to knowledge in engineering management is fourfold: (1) the limitations of prior studies on causes of tolerance issues are revealed, (2) the empirical studies led to not only verifying and refining the causes collected from the literature by considering them in the context of the identified tolerance issues, but also finding new causes in the context of tolerance management when compared to literature, (3) the identified causes provide insight into reasons behind the recurrence of tolerance issues across the industry, and (4) it investigates the causes of tolerance issues while balancing managerial and engineering views. The findings of this study provide a pivotal basis for construction practitioners to develop effective solutions for tolerance management whereby tolerance risks can be identified and mitigated in a prescient manner, which can result in a significant amount of savings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".