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Most existing parameter inversion methods of the dynamic constitutive model parameters do not take the concept of uncertainty into account. Therefore, the adaptive cloud transformation algorithm (AGCT) was combined with RBF neural network (RBFNN) to construct an adaptive cloud neural network parameter inversion model (AGCTNN), which converts the uncertainty concept into quantitative values and better takes into account the influence of the randomness and ambiguity between dam systems on the inversion of dynamic parameters. AGCT was compared and analyzed with three traditional clustering algorithms, K-Means, SOM and DBSCAN, to verify the superiority and feasibility of the algorithms. The inversion analysis was then carried out on engineering examples using two inversion models, AGCTNN and RBFNN. The results show that the positive coupling results of the proposed inversion model are in better agreement with the measured values, and the error range between the measured and inverse values of peak acceleration at measurement points is reduced from 8.73%-25.17% to 2.31%-8.16%, which confirms the reasonableness of the inversion model and the possibility of its application in practical engineering.
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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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