Prediction of Zeolite-Cemented Sand Tensile Strength by GMDH type Neural Network

Abstract

Soil tensile strength (q_t) plays an important role in controlling cracks and tensile failures particularly in the design of foundations that usually fail under tensile stresses at the bottom of the treated layer. Soil-cement mixtures are used in many engineering applications including building of stabilized pavement bases and canal lining. Splitting tensile test (STT) is one of the common applied methods for indirect determination of q_t. Given that the determination of q_t of artificially cemented soils from STT—especially for samples with long curing time—is relatively costly and time-consuming, there is a need to develop some empirical models that can estimate determinable properties simply. In the current study, it has been analyzed that whether the Group Method of Data Handling (GMDH)-type Neural Network (NN) is suitable to predict the q_t of sands stabilized with zeolite and cement. For this purpose, a program of STT considering three distinct porosity ratios, four cement contents and six different percent of cement replacement by zeolite in 42, 56 and 90 days of curing time is performed in present study. Active particle (AP) has been introduced as a new parameter for modeling the GMDH-type NN. The performances of the proposed models reveal that GMDH is a reliable and accurate approach to predict the q_t of sands stabilized by zeolite-cement mixture. Proposing an equation in current study, it can be interpreted that AP is one of the key parameters to predict the q_t of zeolite-cemented sands. The sensitivity analysis on the proposed GMDH model with the best performance has shown that the proposed q_t is considerably influenced by cement content variations.

Keywords:

• Zeolite
• cement
• tensile strength
• neural network
• splitting tensile test