A GMDH predictive model to predict rock material strength using three non-destructive tests

• Main Authors: Li, Diyuan, Armaghani, Danial Jahed, Zhou, Jian, Lai, Sai Hin, Hasanipanah, Mahdi
• Format: Article
• Published: Kluwer (now part of Springer) 2020
• Subjects: TC Hydraulic engineering. Ocean engineering; TN Mining engineering. Metallurgy
• Online Access: http://eprints.um.edu.my/36323/

The uniaxial compressive strength (UCS) is considered as a significant parameter related to rock material in design of geotechnical structures connected to the rock mass. Determining UCS values in laboratory is costly and time consuming, hence, its indirect determination through use of rock index tests is of a great interest and advantage. This study presents a prediction process of the UCS values through the use of three non-destructive tests i.e., p-wave velocity, Schmidt hammer and density. This process was done by developing an intelligent predictive technique namely the group method of data handling (GMDH). Before constructing intelligence system, a series of experimental equations were proposed using three non-destructive tests. The results showed that there is a need to propose new model with taking advantages of all three non-destructive tests results. Then, several GMDH models were built through the use of various parametric studies on the most effective GMDH factors. For comparison purposes, an artificial neural network (ANN) was also modelled to predict rock strength. The obtained results of the ANN and GMDH were assessed based on system error and coefficient of determination values. The results confirmed that the proposed GMDH model is an applicable, powerful, and practical intelligence system that is able to provide an acceptable accuracy level for predicting rock strength.