Proceedings of the 4th International Conference on Civil, Structural and Transportation Engineering (ICCSTE'19)
Bibliographic record
Abstract
He obtained his B.Sc. and M.Sc.degrees in civil engineering from Alexandria University of Egypt in 1985 and 1990, respectively, and his Ph.D. in structural engineering from University of Windsor in 1998.Dr. Sennah, core area of expertise includes design, evaluation and rehabilitation of bridges on which he has more than 260 publications and supervised over 75 graduate students.He has demonstrated numerous evidences of impact and contribution to economical design and sustainable construction that led to field applications and standards.In collaboration with Ontario Ministry of Transportation (MTO) and industry partners in USA, Canada and Germany, his research team conducted innovative research including (i) the development of prefabricated bridge elements and connection technologies to accelerate bridge construction, (ii) development of crashworthy and cost-effective, bridge barrier and deck slabs reinforced with glass fibre reinforced polymer (GFRP) bars for sustainable construction and (iii) cost-effective and accelerated FRP repair strategy to bridge girders damaged by vehicle impact.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.050 |
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".