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The key to damage pattern recognition lies in digging and classifying damage features from the response data of civil structures. To this end，a stack auto-encoder network with several auto-encoder hidden layers and a Softmax classification layer is built for analyzing frame structures. A hybrid learning mechanism is adopted to combining unsupervised and supervised learning strategies. Finite element analysis is used to generate the transmissibility function samples corresponding to different scenarios of a frame structure. The transmissibility samples are then divided into training，validation，and test sets. The parameters of the auto-encoder hidden layers，such as the weights and bias，are determined by a pre-training strategy in order to avoid the phenomenon of network over fitting. A fine-tuning step is employed to adjust the pre-trained network parameters，and the network hyper parameters are further adjusted based on the validation set. The measured transmissibility data are input into the network to evaluate the damage of the frame structure. The analysis results show that the proposed method can effectively extract and classify the damage features. Both the single and double damage scenarios at the frame joints were identified with higher accuracy and better anti-noise ability than the traditional shallow neural network.
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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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