Full
Outline
In order to improve the prediction efficiency and accuracy of uniaxial compressive strength of rocks, a combined model based on convolutional neural network (CNN) and long short-term memory (LSTM) network was proposed and optimized using Newton-Raphson-based optimizer (NRBO). Extensive collection of rock physical parameter experimental data, including point load strength, Schmidt hammer rebound number, porosity, and longitudinal wave velocity, was conducted. After preprocessing, 381 valid samples were retained and visualized for analysis and outlier handling. CNN was used for feature extraction, LSTM captured temporal dependencies, and NRBO optimized hyperparameters such as learning rate, regularization coefficient, and number of hidden layer nodes. The experimental results show that the NRBO-CNN-LSTM model performs the best on the test set, with a coefficient of determination of 0.98, a root mean square error of 6.27, and an average absolute error of 4.82, significantly better than other comparison models, demonstrating stronger fitting ability and generalization performance. This optimization model can effectively integrate multiple physical characteristics of rocks, achieve high-precision and high stability prediction of uniaxial compressive strength, and provide reliable technical reference for engineering practice.
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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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