When designing a soil structure, results obtained from a soil investigation are converted into parameters that are directly needed for design calculation; a conversion error in such a case, however, is considered as a challenge to be addressed. If a soil constant necessary for numerical analysis of soil can be estimated accurately by using AI, the quantity of information required for analysis is expected to be increased. In this study, Internal friction angle data obtained by triaxial compression tests were arranged from KANSAI Soil databases and a trial estimation was made on values for Internal friction angle by means of artificial neural networks. First, 504 data sets collected from the Kansai area soil information databases (data of Kobe City) were created. As a result of the above, the coefficient of determination became 0.657, showing a Internal friction angle by excluding the UU test. Regarding future prospects, increase in AI data, comparison of AI algorithms, estimation of other soil constants, and verification of applicability per region are scheduled to be conducted.