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Record W3155095572 · doi:10.3390/jrfm14040187

Structural Failures Risk Analysis as a Tool Supporting Corporate Responsibility

2021· article· en· W3155095572 on OpenAlexvenueno aff
Agnieszka Czajkowska, Manuela Ingaldi

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDemolitionHuman errorProcess (computing)Risk analysis (engineering)Structural failureBusinessControl (management)Point (geometry)EngineeringOperations managementConstruction engineeringComputer scienceCivil engineeringStructural engineeringMathematics

Abstract

fetched live from OpenAlex

The problem of the structural failures is inextricably linked with the construction industry. A structural failure can be defined as the unintentional, violent destruction of a building object or its part, as well as structural elements of scaffolding, forming elements, sheet piling and excavation linings. Structural failures always entail financial and environmental losses that cause a big problem for companies. The analysis of the structural failures allows to indicate the causes that led to them, but also to introduce actions to help avoid them or decrease their appearance in the future. From the point of view of sustainability risk, human life, corporate responsibility, but also possible financial penalties, it is a very important element of the business process management in an enterprise. In the paper the structural failures occurring in Poland in 2015–2019 were analyzed based on data from the General Office of Building Control (GUNB). They are divided into two categories: caused by random factors and resulting from human error. Failures caused by human error were divided into those related to construction, used material and building operation (exploitation). The structural failures occurring during construction works, e.g., construction, renovation, demolition works, as well as in existing facilities, e.g., during the use of the facility but also in facilities excluded from use, were analyzed. Then, the individual causes of the structural failures were analyzed in terms of repeatability in each category. The risk priority number was calculated for the causes in the group “random events” and nine causes related to “human error”. Actions aimed at reducing the risk of future failures were proposed. The results of the analysis provide conclusions that constitute input data for the improvement of both the processes themselves and the procedures for design, construction and exploitation, or methods and frequency of inspections.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0190.006
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.036
GPT teacher head0.324
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2021
Admission routes1
Has abstractyes

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