Proceedings of the 21st International Ship and Offshore Structures Congress VOLUME 1 Technical Committee Reports
Bibliographic record
Abstract
Preface The first volume contains the eight Technical Committee reports, and the second volume contains the reports of the eight Specialist Committees, presented and discussed at the 21st International Ship and Offshore Structures Congress (ISSC 2022) in Vancouver (Canada), on September 11–15, 2022. The Official Discussers’ reports and all floor discussions, including the replies by the committees, will be published after the Congress in electronic form. Table of Contents Preface .............................................................................................................iii Committee I.1: Environment .........................................................................1 Alexander Babanin (Chair); Mariana Bernardino; Franz von Bock und Polach; Ricardo Campos,; Jun Ding; Sanne van Essen; Tomaso Gaggero; Maryam Haroutunian; Vanessa Katsardi; Alexander Nilva; Arttu Polojarvi; Erik Vanem; Jungyong Wang; Huidong Zhang; Tingyao Zhu Committee I.2: Loads ................................................................................125 Ole Andreas Hermundstad (Chair); Shuhong Chai; Guillaume de Hauteclocque; Sheng Dong; Chih-Chung Fang; Thomas B. Johannessen; Celso Morooka; Masayoshi Oka; Jasna Prpić-Oršić; Alessandro Sacchet; Mahmud Sazidy; Bahadir Ugurlu; Roberto Vettor; Peter Wellens Committee II-1: Quasi-Static Response ....................................................227 James Underwood (Chair); Erick Alley; Jerolim Andrić; Dario Boote; Zhen Gao; Ad Van Hoeve; Jasmin Jelovica; Yasumi Kawamura; Yooil Kim; Jianhu Liu; Sime Malenica; Heikki Remes; Asokendu Samanta; Krzysztof Woloszyk; Deqing Yang Committee II.2: Dynamic Response .........................................................301 Gaute Storhaug (Chair); Daniele Dessi; Sharad Dhavalikar; Ingo Drummen; Michael Holtmann; Young-Cheol Huh; Lorenzo Moro; Andre Paiva; Svein Sævik; Rong-Juin Shyu; Shan Wang; Sue Wang; WenWei Wu; Yasuhira Yamada; Guiyong Zhang Committee III.1: Ultimate Strength ...........................................................395 Paul E. Hess (Chair); Chen An; Lars Brubak; Xiao Chen; Jinn Tong Chiu; Jurek Czujko; Ionel Darie; Guoqing Feng; Marco Gaiotti; Beom Seon Jang; Adnan Kefal; Sukron Makmun; Jonas Ringsberg; Jani Romanoff; Saad Saad-Eldeen; Ingrid Schipperen; Kristjan Tabri; Yikun Wang; Daisuke Yanagihara Committee III.2: Fatigue and Fracture ......................................................501 Yordan Garbatov (Chair); Sigmund K Ås; Henk Den Besten; Philipp Haselbach; Adrian Kahl; Dale Karr; Myung Hyun Kim; Junjie Liu; Marcelo Igor Lourenço de Souza; Wengang Mao; Eeva Mikkola; Naoki Osawa; Fredhi Agung Prasetyo; Mauro Sicchiero; Suhas Vhanmane; Marta Vicente del Amo; Jingxia Yue Committee IV.1: Design Principles and Criteria .......................................643 Matthew Collette (Chair); Piero Caridis; Petar Georgiev; Torfinn Hørte; Han Koo Jeong; Rafet emek Kurt; Igor Ilnytskiy; Tetsuo Okada; Charles Randall; Zbigniew Sekulski; Matteo Sidari; Zhihu Zhan; Ling Zhu Committee IV.2: Design Methods .............................................................745 Andrea Ivaldi (Chair); Abbas Bayatfar; Jean-David Caprace; Gennadiy Egorov; Svein Erling Heggelund; Shinichi Hirakawa; Jung Min Kwon; Dan Mcgreer; Pero Prebeg; Robert Sielski; Mark Slagmolen; Adam Sobey; Wenyong Tang; Jiameng Wu Subject Index .............................................................................................815 Author Index ...............................................................................................817
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.263 |
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".