5. Conclusions

This chapter presents a trial of application of two supervised learning artificial neural networks (BPANN and RBFNN) to predict engineering material constants, Young's modulus and Poisson's ratio, of backfilled materials (soil and CLSMs). The training and testing data are obtained from numerical experiments using ANSYS. Concluding remarks can be summarized as follows:


In future, another neural network which is appropriate for regression, such as probabilistic neural networks (PNN) and supporting vector machines (SVM), maybe used for the study on the parameter recognition of engineering constants of problems in civil engineering.
