[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Table 5

The optimized weights and biases of the developed ANN-based model to predict σ_max.

I							b1,i	b2
	j = 1	j = 2	j = 3	j = 4	j = 5
1	−5.288	0.974	0.115	−0.124	0.237	2.121	−0.633	1.772
2	−0.020	3.497	2.157	0.157	2.459	−0.857	1.606
3	−4.615	−0.558	−0.743	0.988	−0.925	−1.908	−0.092
4	−2.858	−0.075	−2.373	0.783	−1.055	−1.383	−0.509
5	−1.201	−1.491	−0.970	−0.522	−0.885	3.055	−0.035
6	−0.756	1.804	4.202	1.636	−1.023	1.835	3.599
7	−0.002	−1.251	−4.740	2.218	−0.499	1.323	−1.971
8	−4.088	1.246	2.770	−0.589	0.330	−2.059	1.539
9	2.196	1.176	−1.558	−2.904	−2.890	0.659	1.519
10	0.519	1.456	−1.624	1.092	−0.417	−2.393	0.747
11	−3.117	−2.333	−1.600	−0.911	−0.393	−1.537	0.218
12	−0.026	0.495	2.698	1.203	−0.665	−3.343	2.087
13	−3.189	0.415	0.263	3.192	−0.008	−1.378	1.887
14	−2.627	1.138	−0.152	−0.991	0.599	−2.080	0.345
15	−0.748	0.988	−0.362	2.850	−1.012	1.309	1.783
16	−1.820	−0.330	5.310	−0.836	2.682	1.322	2.900
17	4.885	−0.756	−0.698	−4.450	1.975	−0.703	−1.525
18	1.690	4.334	−2.065	2.208	−0.449	2.556	0.216
19	2.966	0.747	1.833	−1.345	−0.235	1.933	−2.519
20	0.710	1.635	0.142	0.234	2.167	1.664	0.974
21	−0.775	1.511	2.444	−1.423	0.499	2.918	1.118
22	−1.433	1.751	−0.098	2.574	0.924	1.049	0.727
23	−0.376	1.619	1.941	−1.190	−2.214	−0.530	0.037
24	0.571	−0.637	−1.583	1.343	0.394	−2.217	1.060
25	5.831	0.459	−0.280	0.811	−0.423	−2.284	−2.314
26	3.484	−3.748	3.357	−5.355	1.395	−0.355	0.712
27	−2.568	−1.966	0.084	−0.359	−1.958	2.117	−0.860
22	1.083	0.485	−4.025	0.816	−1.657	2.744	−2.142
29	−2.825	0.173	4.042	−0.750	0.857	2.337	2.254
30	−0.017	−1.234	−4.583	−1.940	0.968	−1.241	−2.621
31	1.081	−0.800	0.352	−1.558	0.610	−3.118	1.441
32	−2.956	1.570	0.844	−0.173	0.263	−2.507	−1.213
33	0.958	−0.203	1.742	−1.198	−1.032	−2.362	−1.351
34	2.337	6.044	−1.494	1.849	−0.748	−1.676	−0.694
35	1.236	0.410	−0.855	−2.636	−0.213	0.856	0.355