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Constructing a high-precision dam settlement prediction model is of great significance for ensuring the safety and risk control of dam during the construction period. Taking dam height, rainfall and aging as the influencing factors of dam settlement deformation during construction period, the long-term and short-term memory neural network LSTM algorithm is introduced, and the attention mechanism is embedded. Thus, a prediction model suitable for dam settlement of concrete face rockfill dam during construction period is proposed. The engineering application shows that the attention-LSTM model makes up for the defect that the LSTM cannot dynamically adjust the weight coefficient at the network layer, improves the computational efficiency and accuracy of the model, and has better nonlinear data processing ability, which can more accurately reflect the change trend of monitoring data in the time dimension during the construction period. The relevant experience can be used as a reference for similar projects.
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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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