Design rules to improve efficiency in the steel construction industry
Bibliographic record
Abstract
In steel construction projects, 88% of total decisions impacting cost are made during the design phase.These decisions are made by design professionals, who have neither the knowledge nor the experience of manufacturing operations.In manufacturing engineering, collaboration between designers and manufacturers is well established and formalized through different methods and design rules such as design for manufacturing and assembly (DFMA).These rules provide designers with essential knowledge to reduce the cost and time of manufacturing and assembly of parts during their design, while increasing customer satisfaction Building Information Modeling (BIM) and TFV Theory (Transformation Flow and Value) provide to the construction industry, tools and processes to improve collaboration between design and manufacturing phases while reducing waste during projects.However, BIM and TFV theory do not formalize collaboration between designers and manufacturers of steel structures.Yet, the lack of collaboration between these two phases causes lot of rework, lot of waste of time and material during projects.The aim this research is to develop design rules to overcome some of these issues.These rules use the information taken from the BIM model of 1000 steel structures from a steel manufacturer, to reduce the manufacturing time.These information are grouped and classified according to criteria evaluated using a neural network algorithm.In addition, the recent integration of artificial intelligence in construction projects provides industry with methods to draw from previous projects, essential knowledge for better decision-making.The research shows the strong dependence of the manufacturing time of the steel structures on the quantities of complete cuts and weld in full penetration and on the number of beams that do not come in right angles in the connections.
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 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".