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The precise determination of low-frequency wave loads on ship hulls is an indispensable cornerstone and core challenge in hull structural design. Low-cost ship model testing is often employed in engineering to forecast wave loads. However, test data frequently suffer from deficiencies or abnormalities due to various reasons. Consequently, predicting wave loads from data with defects or anomalies, remains a major engineering challenge. This paper presented an efficient method for accurately determining the wave design loads on ship hulls, specifically tailored to handle deficient or abnormal test data. By integrating 5,400 sets of wave load data calculated using two-dimensional strip theory, a machine learning transfer network was constructed. To address deficient data, we innovatively introduced a fine-tuning network layer, and designed a novel loss function that ignores zero terms, thereby enhancing the network's adaptability. This method achieved rapid wave load forecasting by transferring simulation results to ship model tests, with an accuracy better than 90%. This technique enhances design efficiency, reduces labor costs, and maximizes data utilization, providing a reliable and efficient solution for wave load prediction in hull structural design.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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