Use of GMDH-type neural network to model the mechanical behavior of a cement-treated sand

Sand-cement stabilization is considered as one of the most common in situ methods in the soil improvement practices. Despite the importance of studying some fundamental characteristics such as the stress (q)-strain (epsilon) and the pore pressure (u)-strain (epsilon) behavior of the stabilized soils, very few studies have examined this so far. Hence, this paper aims at adopting an initiative approach which is group method of data handling (GMDH)-type neural network to specifically predict such behavior for cement-treated sands using the consolidated undrained (CU) triaxial test results. To do so, the q-epsilon and u-epsilon results from the CU tests are considered on the basis of different variables such as cement content (C), confining pressure (CP), porosity (eta) and curing time (D). The obtained data, regarding similar statistical characteristics, are randomly sorted into three groups namely training, validation and testing. Current modeling is based on the first group (80% of the data), whereas the comparisons are made among other approaches in terms of the last one. Moreover, to achieve more accurate predictions, parameters related to the stress (q(n-1)) and pore pressure (u(n-1)) in the previous strain level are assumed in the modeling. It can be concluded that the two-hidden layer model is capable of accurately predicting the q-epsilon and u-epsilon behavior for the testing data, compared to other machine learning methods. By and large, GMDH modeling is strongly suggested as a potent method to estimate the soil mechanical properties like brittle index (I-B), maximum strength (q(max)), failure strain (epsilon(f)) and stiffness (E-50).

Automated summary

This study used a neural network approach to predict the stress-strain and pore pressure-strain behavior in cement-treated sands, considering variables such as cement content, confining pressure, porosity, and curing time. The results show that GMDH modeling is capable of accurately predicting test data, compared to other machine learning methods.