Design of Jetty Piles Using Artificial Neural Networks

Table 8

Simulation results.

Pile number	Number of training cases
	3		15		27		39		50
	Target	Simul.	Target	Simul.	Target	Simul.	Target	Simul.	Target	Simul.
1	−0.6050	−0.6051	−0.6030	−0.6035	−0.7580	−0.7580	−0.7120	−0.7117	−0.7500	−0.7492
2	−0.6050	−0.6055	−0.6030	−0.6032	−0.7580	−0.7588	−0.7120	−0.7121	−0.7510	−0.7496
3	−0.6050	−0.6051	−0.6030	−0.6035	−0.7580	−0.7580	−0.7120	−0.7117	−0.7500	−0.7492
4	0.5230	0.5227	0.6090	0.6082	0.6680	0.6673	0.6540	0.6537	0.6400	0.6398
5	0.5220	0.5227	0^†	0.0001	0.7080	0.7092	0.6940	0.6937	0^†	−0.0004
6	0.5230	0.5227	0.6090	0.6082	0.6680	0.6672	0.6540	0.6538	0.6400	0.6399
7	−0.7660	−0.7649	−0.7440	−0.7433	−0.9210	−0.9210	−0.8060	−0.8057	−0.9990	−0.9990
8	0^†	−0.0008	−0.7490	−0.7475	0^†	−0.0004	−0.8600	−0.8599	0.9600	0.9593
9	−0.7660	−0.7657	−0.7440	−0.7432	−0.9210	−0.9203	−0.8060	−0.8059	−0.9990	−1.0010
10	−0.3970	−0.3963	0.4530	0.4528	−0.5100	−0.5096	0.5310	0.5302	−0.5580	−0.5580
11	−0.3980	−0.3974	0.4520	0.4521	−0.5080	−0.5093	0.5300	0.5294	−0.5600	−0.5600
12	−0.3970	−0.3963	0.4530	0.4528	−0.5100	−0.5096	0.5310	0.5302	−0.5580	−0.5580
RMSE	0.0008		0.0004		0.0007		0.0006		0.0005

RMSE: root mean squared error.
^†No pile at this location.