MétaCan
Menu
Back to cohort

Causes of Defects Associated with Tolerances in Construction: A Case Study

2021· article· en· W3163210510 on OpenAlex
Saeed Talebi, Lauri Koskela, Patrícia Tzortzopoulos, Chris Rausch, Faris Elghaish, Mani Poshdar

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management in Engineering · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContext (archaeology)Identification (biology)Empirical researchDamage toleranceRisk analysis (engineering)Management scienceOperations managementKnowledge managementEngineeringComputer scienceBusinessGeographyMathematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.315
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it