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Vertical ground motions have a significant impact on the seismic response of engineering structures, making the development of reliable vertical ground motion prediction models an important topic in the field of earthquake engineering. Traditional ground motion predictions are primarily based on actual strong motion records, using least squares regression to derive seismic motion parameter prediction models. However, conventional least squares regression often assumes linear relationships or predefined functional forms between variables, which may fail to fully capture the complex nonlinear relationships inherent in seismic data. In contrast, deep learning models can learn patterns from data and provide higher prediction accuracy for complex data distributions. In this study, deep learning methods were applied, and 9 953 vertical ground motion records from the NGA-West2 database were selected for model training and prediction. The self-DNN vertical seismic response spectrum prediction model was established and its performance was compared with traditional prediction models and a DNN neural network models. The results indicate that the vertical seismic response spectrum prediction model established using deep learning algorithms achieves high accuracy and delivers excellent predictive performance. These findings and analyses provide valuable references for vertical seismic response spectrum prediction and structural seismic 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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