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Accurately constructing the nonlinear hysteresis loop model at the bolt connection is crucial for the vibration reduction and safety performance evaluation of a satellite load-carrying structure. Traditional time-domain analysis methods of computational models require substantial time costs, and typical data-driven models struggle to construct high-precision hysteresis models. To address these challenges, a novel Residual Improvement Deep Learning Algorithm (RIDLA) is proposed for constructing the hysteresis loop model of displacement and force at the bolt connection. The algorithm fully leverages the capacity of Long Short-Term Memory (LSTM) neural networks to fit nonlinear relationships in time series. It adopts an innovative approach by creating a multi-level residual improvement deep learning model that iteratively refines predictions based on measured responses, resulting in highly accurate modeling of hysteresis at bolt connections. The performance of the RIDLA method is validated using experimental data from cyclic loading of a subcomponent of a satellite load carrying structure. The findings demonstrate that RIDLA achieves highly accurate predictions of the displacement and force hysteresis loop at the bolt connection. Additionally, the RIDLA method could be applied to predict the dynamic responses of other complex non-linear systems.
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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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