Structural Failures Risk Analysis as a Tool Supporting Corporate Responsibility
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".