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Outline
Predicting and controlling the surface settlement induced by shield tunneling is a critical engineering issue in underground space development. With the development of artificial intelligence technology, machine learning algorithms have demonstrated significant effectiveness in surface settlement prediction and control. We summarize the theory of machine learning, focusing on four key aspects: database construction, data preprocessing, model selection and evaluation, and shield tunneling parameter optimization under settlement control. Additionally, it critically examines the limitations of existing research in this domain. Current research often neglects the additional impacts of surface structure loads, traffic flow, and pedestrian activities, lacks model interpretability, and struggles to balance prediction accuracy with practical applicability. Future research should focus on developing a comprehensive measurement and evaluation system that incorporates the dynamic effects of surface buildings, traffic, and ground surcharge into settlement prediction models. Furthermore, future work should enhance model interpretability and promote the dynamic updating of machine learning models with real-time monitoring data, thereby providing more scientifically sound guidance for safety control in shield tunneling construction.
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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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