基于改进迁移学习算法的岩体质量评价模型

胡建华,郭萌萌,周坦,张涛

Rock Mass Quality Evaluation Model Based on Improved Transfer Learning Algorithm

Jianhua HU,Mengmeng GUO,Tan ZHOU,Tao ZHANG

表2 训练样本

Table 2 Training samples

序号 RQD/% Rw/MPa Kv Kf ω/［L·（min·10m）-1］ 类别 1* 100.0 200.0 1.00 1.00 0.0 Ⅰ 2* 97.5 180.0 0.94 0.95 1.3 Ⅰ 3* 95.0 160.0 0.88 0.90 2.5 Ⅰ 4 92.5 140.0 0.81 0.85 3.8 Ⅰ 5 86.3 105.0 0.68 0.75 6.3 Ⅱ 6 82.5 90.0 0.60 0.70 7.5 Ⅱ 7 78.8 75.0 0.53 0.65 8.8 Ⅱ 8 68.8 52.5 0.41 0.55 13.8 Ⅲ 9 62.5 45.0 0.38 0.50 17.5 Ⅲ 10 56.3 37.5 0.34 0.45 21.3 Ⅲ 11 43.8 26.3 0.28 0.35 50.0 Ⅳ 12 37.5 22.5 0.25 0.30 75.0 Ⅳ 13 31.3 18.8 0.23 0.25 100.0 Ⅳ 14* 18.8 11.3 0.15 0.15 168.8 Ⅴ 15* 12.5 7.5 0.10 0.10 212.5 Ⅴ 16* 6.3 3.8 0.05 0.05 256.3 Ⅴ 17* 0.0 0.0 0.00 0.00 300.0 Ⅴ 18 82.0 95.0 0.70 0.35 20.0 Ⅱ 19 68.0 90.0 0.57 0.35 20.0 Ⅱ 20 40.0 25.0 0.22 0.35 20.0 Ⅳ 21 87.0 95.0 0.70 0.50 10.0 Ⅱ 22 76.0 90.0 0.57 0.50 10.0 Ⅱ 23 76.0 95.0 0.70 0.50 10.0 Ⅱ 24 72.0 90.0 0.57 0.50 10.0 Ⅱ 25 51.0 40.0 0.38 0.50 10.0 Ⅲ 26 52.0 25.0 0.22 0.50 10.0 Ⅲ 27 68.0 90.0 0.38 0.30 20.0 Ⅲ 28 28.0 40.0 0.32 0.30 20.0 Ⅳ 29 51.0 25.0 0.15 0.30 20.0 Ⅳ 30 75.0 95.0 0.70 0.50 0.0 Ⅱ 31 77.5 90.0 0.57 0.45 10.0 Ⅱ 32 75.5 90.0 0.45 0.52 8.0 Ⅱ 33 85.5 94.0 0.65 0.55 0.0 Ⅱ 34 85.0 93.0 0.60 0.50 0.0 Ⅱ 35 78.5 92.0 0.55 0.50 6.0 Ⅱ 36 80.0 95.0 0.50 0.45 0.0 Ⅱ 37 85.0 92.0 0.70 0.50 10.0 Ⅱ 38 78.0 80.0 0.75 0.50 0.0 Ⅱ 39 76.5 90.0 0.55 0.50 10.0 Ⅱ 40 85.0 95.0 0.65 0.50 0.0 Ⅱ 41 75.0 90.0 0.55 0.50 7.0 Ⅱ 42 75.0 90.0 0.55 0.50 10.0 Ⅱ 43 87.0 95.0 0.50 0.45 0.0 Ⅱ 44 82.0 96.0 0.75 0.35 0.0 Ⅱ 45 50.0 70.0 0.50 0.35 5.0 Ⅲ 46 50.6 26.0 0.26 0.35 20.0 Ⅲ 47 50.0 40.2 0.50 0.50 10.0 Ⅲ 48 52.0 25.0 0.20 0.50 5.0 Ⅲ 49 71.0 90.0 0.35 0.30 5.0 Ⅲ 50 50.9 34.0 0.32 0.35 21.0 Ⅲ 51 50.0 90.0 0.50 0.25 5.0 Ⅲ 52 30.2 70.0 0.40 0.20 10.0 Ⅲ 53 50.0 45.0 0.12 0.30 5.0 Ⅲ 54 51.0 35.0 0.32 0.35 15.0 Ⅲ 55 50.9 34.0 0.32 0.35 20.0 Ⅲ 56 50.0 45.0 0.15 0.35 5.0 Ⅲ 57 26.0 36.0 0.22 0.35 5.0 Ⅳ 58 31.5 20.0 0.23 0.25 46.0 Ⅳ 59 35.0 70.5 0.35 0.30 10.0 Ⅳ 60 31.5 20.0 0.23 0.25 50.0 Ⅳ