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Record W4382400439 · doi:10.1061/jcemd4.coeng-12764

Practitioners’ Concerns about Their Liability toward BIM Collaborative Digital Mockups: Case Study in Civil Engineering

2023· article· en· W4382400439 on OpenAlexaffabout
Élodie Hochscheid, Maggie Falardeau, James Lapalme, Conrad Boton, Louis Rivest

Bibliographic record

VenueJournal of Construction Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsLiabilityCLARITYAsset (computer security)Context (archaeology)Process (computing)Work (physics)Building information modelingExploratory researchLegal liabilityKnowledge managementComputer scienceBusinessEngineering ethicsEngineeringComputer securitySociologyOperations managementAccounting

Abstract

fetched live from OpenAlex

Building information modeling (BIM) involves the use of collaborative digital mock-ups of an asset to streamline design, building, and operation processes. Collaborative work and the use of an integrated digital mock-up offers many advantages but raises several problems regarding the liability of stakeholders in construction projects. Practitioners involved in the design process of a building (engineers and architects) practice very high-liability professions for which the use of a digital mock-up implies potentially high stakes. Although liability issues have been identified in the literature as a hindrance to BIM implementation, practitioners’ concerns toward their liability have only barely been investigated. In this paper, we propose to explore engineers’ concerns about their liability toward using BIM collaborative digital mock-ups with a case study in civil engineering. We documented these concerns through an exploratory study consisting of semi-structured interviews. The main contribution of the paper is therefore an organized list of concerns. These include: the alignment between their way of working and professional rules, the clarity of the assignment of liabilities, and the reliability of the digital mock-up. These stem from a liability risk that practitioners perceive because of uncertainty about liability allocation and uncertainty regarding the reliability of digital mock-ups. Our research work is part of an overall effort to understand the problems faced by practitioners when implementing new practices associated with BIM and to provide solutions. The results are therefore extensively discussed in order to identify hypotheses and avenues of work to address the identified concerns. The specific context (engineers, in Quebec) and the exploratory nature of the study implies that the results are not generalizable to a wider population. However, the identified concerns may be likely to emerge in similar context like high-liability professions involved in design stages of BIM projects. This paper is a very first step toward identifying these concerns in the construction sector and must be subject to future work.

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.

How this classification was reachedexpand

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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