The use of artificial neural networks has increased in many areas of engineering. In particular, this method has been applied to many geotechnical engineering problems and demonstrated some degree of success. A review of the literature reveals that it has been used successfully in modeling soil behavior, site characterization, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore a number of ways to adopt ideas from conjugate gradient and Back Propagation in the stochastic setting, using fast Hessian-vector products to obtain curvature information effectively. In our benchmark experiments the resulting highly scalable algorithms converge about an order of magnitude faster than ordinary stochastic gradient descent. The objective of this paper is to provide a general view to describe this method in predicting mechanical behavior and constitutive modeling issues in geo-mechanical behavior of cohesive soil to be used in geo-mechanics. In this research the Batching Back Propagation method (BBP) has been employed and the characterized parameters are introduced as initial void ratio, liquid limit, plasticity index, natural density, moisture percent, solid density of grain, over consolidation ratio, and pre-consolidation pressure. The paper also intends to present how much the input memory may cover the accuracy of predicted behavior of standard triaxial drained and undrained tests. The paper also discusses the strengths and limitations of the proposed method compared to the other modeling approaches. Also, the sensitivity of intended parameters is investigated. D D