A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

Table 4

Partial learning samples.

Serial number	Input parameters						Output parameter
	Integrity coefficient	Reflector distribution coefficient	Poisson’s ratio	Young’s modulus (N/m^2)	Groundwater development state	Strength resilience (MPa)	BQ evaluation after excavation
1	0.33	0.3	0.38	1.7	0.7	43.6	223
2	0.61	0.4	0.31	3.4	0.4	62.8	326
3	0.42	0.6	0.41	1.9	0.8	41.5	192
4	0.65	0.4	0.33	2.8	0.4	47.2	262
5	0.5	0.2	0.36	1.8	0.5	44.7	231
6	0.75	0.3	0.27	7.4	0.2	67.9	384
7	0.7	0.2	0.30	6.6	0.3	60.5	323
8	0.45	0.4	0.35	2.1	0.5	42.3	247
9	0.26	0.5	0.37	1.3	0.8	39.6	208
10	0.45	0.3	0.33	2.8	0.4	46.7	275
.	.	.	.	.	.	.	.
.	.	.	.	.	.	.	.
.	.	.	.	.	.	.	.
31	0.55	0.1	0.27	8.4	0.2	59.1	368
32	0.4	0.5	0.43	2.1	0.6	44.8	183
33	0.44	0.3	0.37	2.6	0.4	47.2	282
34	0.57	0.3	0.33	3.5	0.4	52.5	312
35	0.23	0.2	0.45	1.4	0.2	41.2	169
36	0.43	0.4	0.37	2.2	0.4	41.8	232
37	0.35	0.2	0.33	3.1	0.4	50.6	292
38	0.55	0.1	0.26	7.6	0.2	59.9	378
39	0.23	0.5	0.36	1.7	0.4	42.5	224
40	0.54	0.4	0.34	2.7	0.3	48.5	268
.	.	.	.	.	.	.	.
.	.	.	.	.	.	.	.
.	.	.	.	.	.	.	.
76	0.40	0.3	0.35	2.8	0.4	46.5	282
77	0.41	0.4	0.33	2.2	0.3	47.3	272
78	0.31	0.3	0.42	1.5	0.5	39.5	194
79	0.20	0.5	0.35	1.9	0.4	41.8	240
80	0.77	0.3	0.31	7.2	0.2	69.4	392