**5. Conclusions**

98 Earthquake Engineering

(a)

(b)

Francisco USA cases

(c)

lateral spreads with high degree of confidence.

Measured

R2 = 0.995

Measured

R2 = 0.980

Measured

Measured

Measured

Displacement, in m

R2 = 0.988

Measured = 1.1\* Predicted

Displacement, in m

Displacement, in m

86420 1210

= 1.1\* Predicted

Measured = Predicted

Measured = 0.9\* Predicted

**(a) Training Stage**

= 1.1\* Predicted

Measured = Predicted

Measured = 0.9\* Predicted

86420 1210

86420 1210

0 1 10 12

Measured = 0.9\* Predicted

Measured = Predicted

Predicted Displacement, in m

08642 10 12 Predicted Displacement, in m

0 1 10 12 Predicted Displacement, in m

NEFLAS execution on cases not included in the database (Figure 21.b and Figure 21.c) and its higher values of correlation when they are compared with evaluations obtained from empirical procedures permit to assert that NEFLAS is a powerful tool, capable of predicting

Measured

R2 = 0.970

Measured

Measured = 0.9\* Predicted

Measured

**(a) Training Stage (b) Testing Stage**

R2 = 0.965

Displacement, in m

Displacement, in m

R2 = 0.970

86420 1210

Predicted Displacement, in m

**(b) Testing Stage**

= 1.1\* Predicted

Measured = Predicted

86420 1210

86420 1210

0 8642 10 12

Measured = 0.9\* Predicted

Measured = Predicted

Measured = 1.1\* Predicted

Predicted Displacement, in m

08642 10 12 Predicted Displacement, in m

= 1.1\* Predicted

08642 10 12

Measured = 0.9\* Predicted

Measured

**Figure 21.** NN estimations vs measured displacements for a) the whole data set, b)Niigata Japan, c) San

**(a) Training Stage (b) Testing Stage**

Measured

Displacement, in m

Based on the results of the studies discussed in this paper, it is evident that cognitive techniques perform better than, or as well as, the conventional methods used for modeling complex and not well understood geotechnical earthquake problems. Cognitive tools are having an impact on many geotechnical and seismological operations, from predictive modeling to diagnosis and control.

The hybrid *soft* systems leverage the tolerance for imprecision, uncertainty, and incompleteness, which is intrinsic to the problems to be solved, and generate tractable, lowcost, robust solutions to such problems. The synergy derived from these hybrid systems stems from the relative ease with which we can translate problem domain knowledge into initial model structures whose parameters are further tuned by local or global search methods. This is a form of methods that do not try to solve the same problem in parallel but they do it in a mutually complementary fashion. The push for low-cost solutions combined with the need for intelligent tools will result in the deployment of hybrid systems that efficiently integrate reasoning and search techniques.

Traditional earthquake geotechnical modeling, as physically-based (or knowledge-driven) can be improved using soft technologies because the underlying systems will be explained also based on data (CC data-driven models). Through the applications depicted here it is sustained that cognitive tools are able to make abstractions and generalizations of the process and can play a complementary role to physically-based models.
